# Modelon

*Generated: 2026-06-01*

## Blog Posts

### Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis
- **URL:** https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/
- **Description:** Explore insights from a vehicle dynamics thesis showing how machine learning accelerates simulation from hours to seconds while preserving accuracy—unlocking faster, scalable engineering workflows.
- **Image:** https://modelon.com/wp-content/uploads/2026/05/Blog-ML-Featured-Image-1202x-626.avif
- **Modified:** 2026-05-28

**Key Takeaways for Engineers and Engineering Leaders**

- **Machine learning (ML) can replicate high-fidelity simulation results** with strong accuracy with results up to R² ≈ 0.99
- **Surrogate models reduce simulation time from hours to seconds**, enabling faster iteration
- **Model selection matters**: GRUs delivered the best balance of accuracy and training efficiency
- **Inputs define success**: ML models perform best when the causality of the system is given and relevant data is provided
- **Different tools, different tasks: **ML complements, not replaces, physics-based modeling
- **Immediate applications**: real-time analysis, design space exploration, and rapid optimization

##### Why Simulation Teams Are Looking to Machine Learning

Engineering teams today are balancing two competing demands: increasing system complexity and shrinking development timelines. High-fidelity simulation remains essential for accuracy and confidence, but its computational cost can slow iteration, limit design exploration, and delay critical decisions.

At Modelon, this challenge is particularly visible when working with demanding engineering organizations, including motorsport teams operating at the limits of performance. In these environments, small modeling inaccuracies can translate directly into real-world consequences.

This thesis explored a key question: **Can machine learning complement physics-based simulation to accelerate workflows without sacrificing engineering rigor?**

Rather than replacing equations and system models, machine learning offers a way to approximate complex relationships when guided by relevant data and system expertise.

##### Predicting Vehicle Loads from Real-World Telemetry

The thesis focused on predicting forces and torques on a race car over a full lap: critical variables for understanding braking, cornering, and overall vehicle stability. Using telemetry data from NASCAR racing, the team worked with:

- driver inputs such as throttle, brake, gear, and steering angle.
- measured signals including acceleration, orientation and position.

Because forces and torques were not directly measured, they were computed using a detailed vehicle dynamics model in Modelon Impact. These simulation results served as the “ground truth” for training machine learning models.

This setup reflects a common industry scenario: physics-based models provide trusted results, while data-driven approaches aim to replicate or accelerate them.

##### A Structured, Physics-Informed Approach to Machine Learning

While the racecars took sharp corners, the project itself used straightforward engineering practices. Rather than using machine learning as a black box, the team:

- decomposed the system using a **causality graph**, a standard approach in Modelon’s modeling philosophy.
- broke the problem into **progressively complex subprocesses**.
- validated performance at each stage to ensure traceability and insight.

This physics-informed structure proved critical, not just for model accuracy, but for interpretability and refinement. It also reinforces an important principle: **successful machine learning in simulation depends on system understanding, not just pure data.**

##### Evaluating Machine Learning Models for Time-Series Simulation

Three machine learning architectures were evaluated to represent different approaches to time-series modeling. These models all provided their respective strengths and weaknesses in a simulation environment.

- **Timewise Multi-Layer Perceptron (MLP)**
  A fast, simple baseline that performed well for low-complexity tasks but struggled with nonlinear dynamics
- **Gated Recurrent Unit (GRU)**
  A recurrent model designed for time-series data that consistently delivered the best balance of accuracy and training efficiency
- **Neural Controlled Differential Equation (Neural CDE)**
  A continuous-time approach capable of modeling complex behavior, but difficult to implement and computationally expensive

Across all experiments, the GRU emerged as the most practical option, achieving strong accuracy without excessive training or computational effort.

##### Where Machine Learning Performs Well and Where It Doesn’t

One of the most important findings from the project is that machine learning performance is fundamentally tied to the information available.

When predicting vehicle speed from driver inputs and powertrain variables, a process without an explicitly defined physics model, the results were strong, with R² values around 0.98. This highlights the value of ML in situations where building a detailed physical model would be time-consuming or impractical.

However, predicting forces and torques using only driver inputs proved significantly more difficult. The models struggled because the available inputs did not fully capture the system’s underlying dynamics.

The breakthrough came when the models were given the same positional and dynamic inputs used by the physics-based simulation:

- The GRU model replicated forces and torques almost perfectly.
- R² values approached 0.99.
- Results closely matched the original Modelica simulation outputs.

This demonstrates critical insight for engineering teams. **This shows that machine learning can match high‑fidelity physical models, provided it is given sufficient information to work with.**

##### Speed vs. Fidelity: A Practical Engineering Tradeoff

For simulation engineers, the tradeoff between accuracy and speed is a daily reality. Physics-based Modelica simulations provide trusted, high-fidelity results, but for highly nonlinear systems they can take hours to compute. This is well suited for deep analysis but less practical for rapid iteration, real-time feedback, or large-scale design exploration.

Machine learning thus introduces a compelling alternative:

- Simulation results can be reproduced in **seconds instead of hours**.
- High levels of accuracy are achievable for many use cases.
- Iterative workflows become significantly faster and more scalable.

This enables new capabilities such as interactive design studies, real-time feedback loops, and large parameter sweeps that would otherwise be impractical.

##### The Path Forward: ML and Physics as Complementary Approaches

The most important takeaway is not machine learning versus physics-based simulation; it’s how the two work together. This thesis clearly demonstrates the power of both approaches:

- Physics-based models provide **accuracy, validation, and physical insight**.
- Machine learning models provide **speed and computational efficiency**.

In particular, using physics-based simulations to generate target variables then training ML models on those variables proved highly effective. This allows computationally expensive components to be replaced with fast, data-driven surrogates.

This approach aligns closely with Modelon’s broader vision: **AI should enhance physics-based modeling, not replace it.** As these workflows mature, they can be integrated into platforms like **[Modelon Impact](https://modelon.com/modelon-impact/)** and extended through FMI-based ecosystems to enable more interactive and scalable engineering processes.

##### Where Simulation Is Headed Next

This thesis reflects a broader shift already underway across engineering organizations. Simulation is no longer just about accuracy. It is about enabling faster decisions, broader exploration, and more responsive engineering workflows. The future lies in combining the strengths of both approaches:

- Physics-based modeling for trust, traceability, and deep insight
- Machine learning for speed, scalability, and adaptability

For engineering teams, the opportunity is clear: identify where computation is the bottleneck and apply machine learning strategically to remove it. In that balance, simulation evolves from the bottleneck, racing into a competitive advantage.

---

### Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point
- **URL:** https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/
- **Description:** Learn how transient simulation enables scalable two-phase direct-to-chip cooling for AI data centers—insights from a Modelon–University of Maryland collaboration.
- **Image:** https://modelon.com/wp-content/uploads/2026/05/Data-Center-1200x625-2.avif
- **Modified:** 2026-05-27

##### **Modelon & University of Maryland Researchers to Present Findings at Upcoming Conference**

Artificial intelligence and high-performance computing (HPC) are fundamentally reshaping the way** data centers** are designed. Power densities continue to rise, thermal margins are tightening, and workloads are becoming more dynamic and less predictable. HGX H100 racks introduced in 2022 consumed around 40–60 kW, while Blackwell racks introduced in 2024 consume around 120–140 kW, roughly three times more than previous AI racks. Next-generation racks expected in 2026 could reach 200–240 kW. For engineering teams, this shift requires a new approach to rack cooling, one that accounts for higher heat loads, faster transients, and system-level behavior earlier in the design process.

To better understand what comes next, we spoke with **Dr. Lingnan Lin, Assistant Professor of Mechanical Engineering** at the University of Maryland, College Park, and** Kagan Sears, a PhD student **in Prof. Lin’s lab. Dr. Lin’s research focuses on phase‑change heat and mass transfer, with applications spanning HVAC systems and electronics cooling. Together, their academic–industry collaboration with Modelon explores two‑phase direct‑to‑chip liquid cooling from both a physics and system‑level perspective. Their assessment cuts across boiling physics, transient system behavior, and practical implementation offering a clear view into why this technology matters now, what engineers are learning today, and where the field is headed.

##### Why two‑phase direct‑to‑chip cooling matters now

As Prof. Lin described during the interview, the core driver is simple, but profound: the thermal problem has changed faster than cooling architectures have.

*“If you look at where AI accelerators and next‑generation processors are going, the heat fluxes are no longer something that traditional approaches were designed for,”* Lin explained.

Two‑phase direct‑to‑chip cooling leverages boiling directly at the chip interface, absorbing heat through latent heat rather than sensible heat alone. For AI and HPC workloads with sharp power ramps, localized hotspots, and sustained high utilization, this provides a path to higher heat removal in a smaller footprint.

But neither Lin nor Sears framed two‑phase cooling as a silver bullet. Instead, they emphasized that its promise only becomes real when it is treated as *part of a system*, not just a superior cold plate.

##### Why transient system simulation is essential—not optional

A recurring message throughout the conversation was that steady‑state thinking breaks down for two‑phase systems.

*“We’re not just interested in whether boiling occurs,”* Sears noted. *“We want to understand how the system behaves dynamically—as loads change, as flow conditions vary, and as the loop responds over time.”*

Boiling initiation, vapor generation, flow instabilities, startup behavior, and control actions all evolve on different timescales. These dynamics directly affect performance, stability, and long‑term reliability. As a result, transient system simulation becomes a design requirement rather than a nice‑to‑have.

Through Modelon’s system‑level simulation capabilities, the team can model

- Interactions between the cold plate, working fluid, pumps, and heat rejection hardware
- Transient operating scenarios such as power ramping and workload variability
- The impact of control strategies on stability and thermal margins

*“Without transient modeling, you’re essentially designing blind to the behaviors that matter most,”* Sears said.

##### How continuous feedback loops accelerated research collaboration

Both Lin and Sears emphasized how the collaboration with Modelon changed the pace and depth of their work. Rather than relying solely on long experimental cycles, system simulation enabled rapid iteration at the concept level.

*“Being* *able* *to* *adjust* *assumptions,* *boundary* *conditions,* *and* *operating* *scenarios* *quickly* *helps* *us* *understand* *which* *ideas* *are* *worth* *pursuing* *experimentally,”* Sears explained.

From Modelon’s perspective, the value flows in the other direction as well. The academic environment provides a rigorous setting for validation against real two‑phase physics, strengthening confidence in the models and making them more useful for industrial decision‑making.

*“There’s a lot of value in closing the loop between simulation and experiment,”* Lin remarked. *“That’s where real insight comes from.”*

This feedback loop—fast iteration paired with physical validation—has allowed the collaboration to move beyond feasibility questions toward system‑level understanding.

##### Early learnings: what engineers should be paying attention to

While full results will be shared at the upcoming [Herrick Conferences](https://chpb.engineering.purdue.edu/herrick-conferences/) at Purdue University, several early observations stood out during the interview:

- **System dynamics dominate performance**: Small changes in operating conditions can produce qualitatively different behaviors in a two‑phase loop.
- **Controls shape outcomes**: Stability and efficiency depend as much on control strategy as on component design.
- **Simulation reduces experimental risk**: Transient models help identify unstable or unsafe regimes before hardware is ever built.

*“A lot of the surprises happen at the system level,”* Sears noted. *“That’s where simulation really earns its value.”*

These insights are particularly relevant for engineering managers weighing technology risk and development timelines.

##### Where two-phase cooling is heading

Looking ahead, Prof. Lin sees the field moving toward integrated design workflows where physics, component design, system architecture, and controls are developed together rather than sequentially.

*“The goal isn’t just higher heat flux capability,”* Lin said. *“It’s predictable, controllable behavior that scales with real systems.”*

Validated transient models and digital‑twin‑style approaches are likely to play a growing role, not only in R&D but also in system commissioning and operation. For data centers supporting AI at scale, this predictability may become just as important as raw cooling capacity.

For Modelon, this collaboration reflects a broader strategy: helping engineers move from component‑level optimization to **system‑level confidence** as data center cooling enters a more dynamic era.

##### Continue the conversation

The research discussed here will be presented in more technical depth this summer, but the questions facing two-phase direct-to-chip cooling are much bigger than any single study. As rack power densities continue to rise, the industry still needs deeper insight into issues such as refrigerant maldistribution across the rack, working-fluid selection under performance and regulatory constraints, and the implications of two-phase cooling on system architecture, controls, and heat rejection loops.

That is where collaboration between industry and research becomes especially important. Modelon and the [University of Maryland team](https://linlab.umd.edu/) are interested in hearing from data center technologists, cooling system developers, component suppliers, and engineering leaders who are actively evaluating next-generation cooling architectures. If your team is defining requirements, identifying technical barriers, or exploring sponsorship opportunities around two-phase systems and transient simulation, we welcome the conversation.

---

### AI Assisted Simulation Now in Modelon Impact
- **URL:** https://modelon.com/blog/new-ai-assistant-in-modelon-impact/
- **Description:** Modelon’s new AI Assistant in Modelon Impact helps engineers get started faster, troubleshoot more easily, and speed up simulation workflows.
- **Image:** https://modelon.com/wp-content/uploads/2026/04/AI-Assistant-Laptop-Video.gif
- **Modified:** 2026-05-15

###### **Modelon’s new AI Assistant helps engineers get started faster, troubleshoot more easily, and move through simulation work with greater confidence.**

Simulation software has long asked users to do a lot of the heavy lifting themselves. Setting up models, working through errors, and finding the right next step often takes time, experience, and access to the right expert at the right moment. For teams trying to scale simulation, that can slow down projects and make onboarding harder than it should be.

Modelon’s new **AI Assistant** is designed to reduce that friction inside **[Modelon Impact](https://modelon.com/modelon-impact/)**. Embedded directly in the platform, it brings guided support into everyday simulation workflows so users can move faster without having to search documentation, interpret logs on their own, or rely as heavily on senior specialists for routine questions.

![](https://modelon.com/wp-content/uploads/2026/04/AI-Assistant_Screen_2_HeatExchanger.png)

**With awareness of both system context and available libraries, the AI Assistant helps engineers find compatible component models faster.**

##### What the AI Assistant helps users do

- Get started with relevant example models and components
- Receive step-by-step help for common workflow questions
- Resolve compilation and simulation errors more quickly

This matters because many of the biggest slowdowns in simulation are not about solving the engineering problem itself. They come from setup friction, troubleshooting delays, and knowledge gaps between experienced users and everyone else on the team. The AI Assistant is built to help close that gap.

##### Why Modelon’s AI Assistant stands out

- Builds on Modelon’s proprietary simulation libraries and domain know-how
- Delivers guidance that is more relevant to real simulation work in Modelon Impact
- Designed to support guided, explainable workflows rather than generic answers

For users, the value is practical. Teams can reduce time to answer, help new users become productive faster, and make simulation expertise easier to scale across the organization. Instead of knowledge living with only a few experienced users, more of it becomes accessible in the workflow itself.

![](https://modelon.com/wp-content/uploads/2026/04/AI_Assistant_Screen3_ParameterSweep.png)

**Get help from the built-in AI Assistant to discover Modelon Impact features, like setting up parameter sweeps to explore system behavior.**

The launch fits in Modelon’s leading strategy to create more simulation value for both new and experienced users with AI. The AI Assistant is focused on guided, workflow-level support that improves accessibility and productivity. **[Modelon Impact Code Studio](https://modelon.com/blog/introducing-modelon-impact-code-studio/)** remains the environment for more advanced, expert-level and agentic work. Together, they create a stronger path for both new and experienced users.

The AI Assistant is now available for select customers using Modelon Impact cloud, extending Modelon’s effort to bring AI and physics-based simulation together in ways that support real engineering work.

**[Ask us](https://modelon.com/talk-to-an-expert/) how Modelon Impact helps engineering teams work faster with physics-based simulation + AI-assisted workflows.**

---

### How to Do Weather File Sweeps in Modelon Impact
- **URL:** https://modelon.com/blog/how-to-do-weather-file-sweeps-in-modelon-impact/
- **Description:** Eliminate manual weather file management. Learn how to run parallel weather file sweeps natively in Modelon Impact to analyze building and data center performance across climates without external scripts.
- **Image:** https://modelon.com/wp-content/uploads/2026/05/Weather-Sweep-Social-1200x625-1.avif
- **Modified:** 2026-05-15

A customer told us something recently that stuck with me.

They were evaluating how a **data center design** would perform across climates. Hot and humid. Cold and dry. Different cities, different weather years. The kind of analysis you *have* to do if uptime, efficiency, and resilience matter.

Technically, they were getting it done. Practically, it was painful.

They were running the same simulation over and over, swapping weather files, managing parameters, stitching results together using custom Python scripts they maintained on the side. Not because they wanted total control…not because it was innovative…because they didn’t know there was another way.

##### When “Advanced” Really Means “Overcomplicated”

This isn’t an edge case. If you design or operate buildings or data centers, multi‑climate analysis is foundational. But too often, it lives outside the modeling environment:

- External scripts to manage weather files
- Manual processes to rerun simulations
- Spreadsheets or notebooks to compare results

The workflow works until it doesn’t. Scripts break. Assumptions drift. One small change turns into hours of cleanup.

When we showed this customer how to do the same thing *natively* in **[Modelon Impact](https://modelon.com/modelon-impact/)**, the reaction surprised me. They weren’t blown away. They were relieved.

##### Weather File Sweeps, Without the Workarounds

Inside Modelon Impact, we helped them set up a weather sweep directly in the platform:

- Load multiple weather files
- Run simulations in parallel
- Compare results side by side—no exporting, no stitching

No external Python.
No fragile maintenance.
No reinventing the workflow for every project.

And that relief told us something important: this is a bigger pain point than we talk about.

##### A Better Way to Analyze Climate Performance

That’s why we created a short video showing exactly how to set up a **weather files sweep in Modelon Impact**.

In just a few minutes, you’ll see how to:

- Define multi‑climate scenarios
- Configure and run sweeps efficiently
- Analyze results directly where you model

Whether you’re stress‑testing a data center design or comparing building performance across regions, the goal is the same: understand how your system behaves in the real world without fighting your tools to get there.

👉 **[Watch the video](https://youtu.be/LnsQxmdV5Qc) to see how it works**!

Before you go, I’m curious. **How are you currently handling multi‑climate analysis in your workflow and what’s slowing you down the most?**

Let us know. Chances are, you’re not the only one.

---

### AI Liquid Cooling at System Scale
- **URL:** https://modelon.com/blog/ai-liquid-cooling-at-system-scale/
- **Description:** AI workloads are reshaping data center cooling. Learn why system-level simulation is essential for liquid cooling design, procurement, and operations.
- **Image:** https://modelon.com/wp-content/uploads/2026/04/Blog-Featured-Image-DCW_04_26.avif
- **Modified:** 2026-05-07

###### **A recent presentation at Data Center World highlights how system simulation is becoming a critical capability for designing, procuring, and operating AI data centers. **

AI has been embedded in digital infrastructure for years, quietly powering services like recommendation engines, security systems, and automation. What changed in late 2022 was accessibility. With the rise of generative AI tools like ChatGPT, advanced reasoning and multi‑step problem solving became available at scale. That shift fundamentally altered data center workloads.

What began as retrieval‑based processing has evolved into long chains of inference executed by AI agents that plan, reason, and adapt. As NVIDIA CEO Jensen Huang noted in his GTC 2026 keynote, *“The inference inflection has arrived.”*

For data center leaders, this inflection point shows most clearly in one place: **cooling**, and increasingly **liquid cooling**.

At last week’s **Data Center World **in Washington, DC, the Spark Session: [**Physics-based System Simulation for AI Data Center Cooling**](https://schedule.datacenterworld.com/session/spark-session-3-ideas-emerging-tech-ideas-for-data-center-ops/918580), highlighted how system simulation is becoming a critical capability for designing, procuring, and operating AI data centers under these new conditions.

##### AI Workloads are Redefining Thermal Reality

AI infrastructure is driving a rapid and sustained increase in power density that traditional cooling assumptions can no longer accommodate.

Key indicators include:

- Rack power densities rising from **2–5 kW per rack a decade ago** to **30–50+ kW today**, with **100+ kW per rack** already in sight
- AI‑optimized servers projected to represent **nearly half of total data center power consumption by 2030**
- Cooling consistently accounting for **40 percent or more** of total facility energy use
- Global data center electricity demand expected to **more than double by the end of the decade**

At these densities, a reliable liquid cooling strategy is becoming more important than ever. It increases coupling between thermal, hydraulic, and control domains, and it raises the importance of understanding system behavior as a whole under fast and nonlinear load transients.

##### Liquid Cooling is a System Challenge, Not a Component One

Modern AI data centers behave less like collections of independent subsystems and more like tightly integrated machines. In liquid‑cooled environments especially, small local changes can propagate rapidly across the entire plant.

Key characteristics include:

- AI training loads can swing from near idle to peak power in seconds.
- Thermal, hydraulic, and control responses are tightly coupled across racks, CDUs, pumps, and chillers.
- Component cut sheets describe isolated behavior but do not explain system response.

For engineering managers and operators, this means that decisions based solely on rated performance or steady‑state assumptions carry increasing risk. Selecting hardware without understanding its behavior in the full cooling system increasingly amounts to making high-impact decisions without visibility into their downstream consequences. Understanding true behavior requires **system‑level simulation**.

##### Why Physics‑Based System Simulation Matters More Now

System‑level models for simulating and analyzing cooling systems can use:

- **Modelica**, an open, equation‑based, multi‑physics modeling language
- **FMI (Functional Mock‑up Interface)** for model exchange across tools and organizations
- **API‑driven workflows** that expose physical behavior to operational software, analytics platforms, and AI workflows

This approach allows thermal, fluid, mechanical, and control dynamics to be modeled together in a single coherent framework. The model becomes a shared digital asset that supports multiple teams and workflows rather than a one‑off analysis.

![Modelica & Interface Approach ](https://modelon.com/wp-content/uploads/2026/04/Modelica_Interface_Approach.avif)

##### How does Simulation Provide Value Across the Data Center Lifecycle?

1. **Design: Quantifying Trade‑Offs in Liquid Cooling Architectures**
   System‑level simulation enables engineers to evaluate cooling topologies and equipment sizing decisions in the context of realistic AI workloads. Instead of optimizing for a single design point, teams can assess behavior across broad operating envelopes. Questions like these can be answered quantitatively:

   - How should CDUs, pumps, and heat exchangers be sized for transient AI loads?
   - What are the trade‑offs between different liquid cooling architectures?
   - How sensitive is overall efficiency to component‑level design choices?
2. **Procurement: From Cut Sheets to System Performance**
   Procurement decisions increasingly depend on understanding how equipment behaves inside a specific data center environment.

   Simulation‑ready component models allow owner/operators to evaluate equipment based on system‑level metrics such as energy, water use, and transient response. Manufacturers can also demonstrate performance advantages that are invisible in datasheets.

   For suppliers of valves, CDUs, pumps, and heat exchangers, providing simulation‑ready models is becoming a competitive differentiator rather than an academic exercise.
3. **Operations: Managing Reality Beyond Design Conditions**
   Even well‑designed facilities face conditions that were not fully anticipated. Examples include extreme weather, evolving GPU generations, fouling, partial failures, and shifting load profiles.

   The same system model developed during design can be reused to test control strategy changes, analyze failure modes, and assess capacity expansion scenarios without risking up-time. For operations teams, this enables informed decisions under uncertainty.

##### Case Study: One Valve, System‑Wide Consequences

This case examined how a single parameter change—the response time of a control valve in a liquid-cooled distribution loop—can create ripple effects across the broader cooling system.

Localized to the component, it was observed that under rapid AI load transient the faster valve delivers a steadier rack supply temperature compared to the slower one. When viewed at the system level, the model also revealed additional benefits to the chiller upstream of this component. For the slower responding valve, rack supply temperature excursion due to the load transient would have exceeded the design limit had it not been suppressed prior to the load jump. This however requires moving the upstream chiller from its efficient operating point.

![](https://modelon.com/wp-content/uploads/2026/04/Valve_Response_Time.avif)

*Illustrative data based on Modelica Buildings Library simulation framework*

##### Key Takeaways from Data Center Stakeholders

- Liquid‑cooled AI data centers are tightly coupled systems where local decisions have global impact.
- System‑level simulation is essential for understanding transient behavior and efficiency trade‑offs.
- Models deliver value across design, procurement, and operations.
- Simulation‑ready component models create transparency and trust between manufacturers and owner‑operators.
- Open standards such as Modelica and FMI enable collaboration across organizations and disciplines.

##### Final Thought on Liquid Cooling Simulation

As AI workloads continue to accelerate, cooling performance directly influences reliability, efficiency, and business outcomes. Liquid cooling in data centers raises the bar for both performance and complexity.

Physics‑based system simulation provides a practical way to navigate this complexity and make informed decisions before they become expensive or irreversible.

In the era of AI data centers, success depends not on optimizing components in isolation, but on **understanding the system as a whole**.

Explore Modelon’s [Data Center System Simulation...

---

### Engineering AI that Supports Real Decisions
- **URL:** https://modelon.com/blog/engineering-ai-that-supports-real-decisions/
- **Description:** Generic AI can explain engineering concepts—but real decisions need governed workflows, physics‑based models, and decision‑ready evidence.
- **Image:** https://modelon.com/wp-content/uploads/2026/04/Testimonial-1200x625-1.png
- **Modified:** 2026-05-07

##### Executive Summary

- Generic AI can explain engineering concepts, but it does not by itself produce trustworthy, decision-ready analysis.

- Useful engineering AI must connect engineering intent, executable physics-based models, study setup, solver behavior, assumptions, and evidence.

- Modelon’s advantage is not only trusted models, but the engineering context that governs how those models should be used.

- The right workflow should set boundaries, evidence requirements, and approval points without hard-wiring every engineering step.

- This approach helps teams set up studies faster, diagnose failures more efficiently, compare alternatives more clearly, and reuse engineering knowledge over time.

- The long-term value is more repeatable, evidence-based engineering decisions across teams and programs.

Core Idea

In engineering, the workflow should govern the work, not hard-wire every step. The AI agent can propose and adapt the plan, but it should do so inside defined engineering constraints, evidence requirements, and approval points.

Engineering teams rarely struggle because they lack possible answers. They struggle because getting from a question to a trustworthy answer takes work: choosing the right model, deciding which assumptions are acceptable, configuring a study that can actually run, understanding why a run failed, and turning results into something that supports a real decision.

That is where much of engineering time goes. It is also where generic AI usually stops being useful. A fluent answer from an AI chat assistant may help with orientation, but it is not the same thing as a simulation-ready study, a stable run, or a recommendation an engineer would sign off on.

At Modelon, we see engineering AI as something more concrete: a way to connect engineering intent, trusted physics-based models, reusable engineering know-how, and governed execution so teams can move faster without giving up rigor.

Much of today’s AI in engineering improves individual steps in the work: faster modeling, better search, smoother collaboration, or more task-level automation. Modelon’s direction is broader. The aim is not only to optimize how engineering work is performed, but to improve the decision loop itself, from engineering intent, through physics-grounded execution, to validated recommendations and governed action.

##### Why Generic AI is Not Enough

A general-purpose AI system can summarize documentation, explain concepts, and draft recommendations. That is useful, and it can remove some friction from engineering work. But in systems simulation, that is not where most of the value is created.

The harder part starts when a team has to turn a question into a credible study. Which model should be used? Which assumptions are acceptable? Which scenarios matter? What should be constrained, and what should be explored? If the run fails, is the problem numerical, structural, or simply a bad setup? If the results look plausible, are they also physically credible?

These are not unusual exceptions. They are part of everyday physics-based engineering work. That is why useful engineering AI cannot stop at reading, summarizing, and generating text. It has to connect to the models, study setup, solver behavior, assumptions, and evidence that determine whether a result can actually be trusted.

##### Trusted Physics and Engineering Context as the Foundation

For more than 20 years, Modelon has built equation-based modeling technology and reusable Modelica libraries for complex physical systems. That foundation is critical because, in engineering, trust depends on whether a system can represent and predict physical behavior credibly, not on whether it can produce persuasive language.

When AI is connected to executable engineering assets, it can do more than talk about alternatives. It can help evaluate them. It can support scenario analysis, trade-off studies, and model-backed reasoning against systems that can actually be configured, run, and interpreted in ways engineers recognize as credible.

This is also where Modelon Impact matters. Modelon Impact is not just a surface for information. It is where libraries, model variants, study definitions, assumptions, and results become part of a working engineering process.

But trusted models alone are not enough. Good engineers do more than run simulations. They decide which simplifications are acceptable, which scenarios matter, what ranges are realistic, which reference architectures are relevant, and which results deserve skepticism.

Much of that know-how lives in study templates, reference configurations, domain assumptions, evaluation logic, review habits, and best practices that experienced teams apply almost instinctively. This is the reusable engineering context of the system: the layer that connects objectives to reference architectures, constraints, workflows, evaluators, and acceptable operating envelopes.

If AI is going to be useful, that knowledge has to be available in a form the system can work with. The advantage is not only having trusted models. It is having the engineering context that tells the system how those models should be used before the first run even starts.

##### Governed Agentic Workflows, Not Workflow-as-Implementation

This is where many current agent demos feel incomplete in an engineering context. They treat the workflow as the implementation itself: every step is predefined, every transition is explicit, and each agent call is hard-wired into the flow. That can be reliable, but it is also rigid.

Modelon’s direction is different. The workflow should act as governance, not as a script. It should define lifecycle stages, required artifacts, evidence expectations, and approval points. Within those boundaries, the agent can propose a plan, adapt the sequence of work, and respond to what it learns from actual engineering results.

This distinction matters. Defining a study, running it, diagnosing failures, refining a search, comparing alternatives, and preparing a recommendation for review are different kinds of work. They use different context, different tools, and different checks. A governed agentic approach lets the system separate those concerns without turning the entire process into one hard-coded script.

It also creates a clearer path for trust. The agent can be adaptive, but the engineering process remains governed. Required evidence stays visible. Approvals happen at the right moments. The result is not uncontrolled autonomy. It is intelligent execution inside explicit engineering boundaries.

Mental Model

Workflow = governance. Agent = plan + adaptive execution. Simulation, data processing, and external tools = the execution substrate.

##### What this Looks Like in Practice

###### **From engineering intent to design exploration**

Consider a team exploring alternative thermal or control architectures against a set of efficiency and operating constraints. In many organizations, a surprising amount of time still goes into choosing the right library components, selecting a reference architecture, defining realistic parameter ranges, and getting the study into a state that is actually simulation-ready.

In a governed agentic workflow, the lifecycle stage may simply say: define intent, propose a plan, execute the study, evaluate the evidence, and prepare a recommendation. Within that frame, the agent can decide whether it should start with a reference configuration in Modelon Impact, generate a DOE, narrow the parameter space, or refine the study after the first results come back. The workflow governs the boundaries; the agent adapts the path.

That produces more than speed. It improves the quality of setup. A better-defined study produces better comparisons, fewer wasted iterations, and a clearer path from engineering intent to decision-ready evidence.

![](https://modelon.com/wp-content/uploads/2026/04/EngineeringAI_RealDecisions_Figure1_Final.png)

######...

---

### 5 Questions to Ask Before Trusting AI-Related Simulation Results
- **URL:** https://modelon.com/blog/5-questions-to-ask-before-trusting-ai-related-simulation-results/
- **Description:** Before trusting AI-generated simulation results, ask these five questions about physics, verification, traceability, engineering efficiency, and organizational learning.
- **Image:** https://modelon.com/wp-content/uploads/2026/04/Blog-Featured-Image-Automotive-1202x-626.png
- **Modified:** 2026-05-07

AI can now generate simulation models from natural language prompts. It can derive equations, write code, configure experiments, and produce results. For engineers evaluating these capabilities, the interesting question is no longer, “Can AI do it?” It almost always can.

The harder question is: “Should I trust the result enough to make a decision?”

That question matters because simulation results drive real consequences. A chiller plant sized using simulation will operate for 20 years. A suspension configuration validated in simulation goes into a vehicle that carries people. A control strategy tested in a virtual environment gets deployed on physical equipment. The gap between an impressive demo and a production-ready decision tool is not about AI capability. It is about engineering trust. Here are five questions that help distinguish one from the other.

**1. **Where did the physics come from?****

An AI that derives governing equations from first principles may produce results that look correct. The equations may compile, the simulation may converge, and the plots may show reasonable trends.

But “reasonable” is not the same as “validated.” A compressor model that assumes ideal gas behavior will give plausible COP values at moderate conditions and diverge from reality at high pressure ratios. A heat exchanger model that neglects fouling will consistently overpredict performance over the system’s lifetime. These errors do not announce themselves. They sit quietly in the results until someone makes a decision based on them.

Validated library components take a different approach. The physics has been verified against experimental data. The parameter ranges are documented. The known limitations are explicit. When an AI selects a validated component, it inherits that verification without needing to reproduce it.

What to look for: can the tool tell you where each equation came from and what data it was validated against? Or did the AI derive it during the session?

**2. **Who checks the AI’s work?****

A single AI agent that builds a model, runs a simulation, and presents results has no check on its own reasoning. If it selects the wrong component type, sets a parameter outside its valid range, or misinterprets a boundary condition, nothing catches the error before the engineer sees the result.

Engineering organizations have always required peer review of calculations. The same principle applies to AI-assisted workflows. A well-designed system separates the agent that proposes from the agent that verifies. Parameter values are checked against documented ranges. Unit consistency is enforced. Results are compared to expected magnitudes before being presented.

This is not about distrusting AI. It is about applying the same discipline to AI-assisted work that we already apply to human-assisted work.

What to look for: is there a verification step built into the workflow? Or does the output go directly from generation to presentation?

**3. Can I trace every number back to a source?**

When simulation results support a design decision, the audience needs to know what model was used, what parameters were set, where those values came from, and what assumptions were made. This is not paperwork. It is how organizations maintain accountability across projects that span years and involve dozens of people.

AI models generated from first principles during a conversation have limited traceability. The derivation happened in a session that may not be logged. The parameter values were chosen by the AI based on training data of unknown provenance. The assumptions may not be explicitly stated.

Models assembled from documented library components, on a platform that records configurations and experiment definitions, have inherent traceability. The component class path identifies exactly what was used. The parameter values can be traced to documented sources. The experiment history is preserved.

What to look for: if someone asks “where did this number come from?” in six months, can you answer without reconstructing the original AI session?

**4. **Does the AI spend its effort on engineering or on infrastructure?****

There is a practical cost dimension that is easy to overlook. Every AI interaction consumes computational resources, whether measured in tokens, API calls, or time. How those resources are distributed between infrastructure work and actual engineering work determines the productivity of the system.

An AI that derives physics, writes solver code, and builds parameter sweep logic from scratch spends most of its effort on model construction before any engineering insight is produced. An AI that works with pre-validated components and a platform that handles experiment execution spends its effort on the engineering problem itself: what to vary, what to compare, what the results imply for design.

This is not just an efficiency question. It is a quality question. Effort spent on reinventing infrastructure is effort not spent on study design, sensitivity analysis, and result interpretation, the parts that actually require engineering intelligence.

What to look for: when the AI finishes, how much of the session was engineering conversation vs. debugging generated code?

**5. ****Does the system get better with use?******

A tool that starts from zero every session is useful but limited. A system that learns from each study, which parameters mattered, which configurations were explored, which results led to decisions, becomes more valuable over time.

This does not mean the AI should learn unsupervised. It means that the structured data from each workflow, the component selections, parameter ranges, study configurations, and engineering conclusions, should be captured in a form that informs future work. When a team has run 200 chiller studies, study number 201 should benefit from that history.

For organizations where experienced engineers retire or move to other roles, this is particularly relevant. The knowledge that makes senior engineers productive does not have to leave with them if it is captured in the infrastructure rather than only in their heads.

What to look for: is each study a standalone event, or does it contribute to an organizational knowledge base that compounds over time?

**The Bottom Line**

AI for engineering simulation is advancing rapidly, and it should. The ability to move from engineering intent to simulation results in minutes rather than days is genuinely transformative.

But speed without trust is not useful for production decisions. The tools that will matter for industry are not the ones with the most impressive demos. They are the ones that can answer these five questions with evidence rather than promises.

The Modelica community has spent nearly three decades building open modeling languages and standard interfaces. Modelon has spent more than two decades building validated libraries on that foundation. AI does not replace any of it. It makes it dramatically more accessible and more productive, but only when it is connected to engineering infrastructure that was built for trust.

---

### From Intent to Action: Agentic AI for Vehicle Dynamics in Modelon Impact
- **URL:** https://modelon.com/blog/from-intent-to-action-agentic-ai-for-vehicle-dynamics-in-modelon-impact/
- **Description:** Explore how agentic AI in Modelon Impact transforms vehicle dynamics engineering—moving from intent to action with AI-assisted simulation workflows, validated physics models, and automated experiment execution.
- **Image:** https://modelon.com/wp-content/uploads/2026/03/Blog-Featured_Agentic_AI_VDL_1202x-626.png
- **Modified:** 2026-05-01

Artificial intelligence is already capable of turning engineering intent into executable models. The next step is putting that capability to work. Previously, we explored why equation-based, [physics-driven modeling is uniquely suited for AI](https://modelon.com/blog/from-intent-to-insight-ai-driven-physics-based-modeling-with-modelica/). Now, we move from capability to application. What happens when AI is not just generating models, but actively participating in engineering workflows? In this post, we show how that same foundation enables a new kind of interaction, where engineers move from intent to action through structured, AI-assisted execution inside [Modelon Impact](https://modelon.com/modelon-impact/).

##### A Different Kind of Engineering Conversation

Vehicle dynamics engineering is iterative by nature. I form a hypothesis, design a study, run it, interpret the results, and decide what to do next. Each step requires judgment. Most of the time, the judgment is not the hard part, the execution is. What changes with an AI agent is not the judgment. That stays with me. What changes is that execution becomes nearly free, which means the conversation between engineer and simulation can happen at a completely different pace. Here is what that looks like in practice.

##### The Workflow: Question, Proposal, Adjustment, Agreement, Execution

I open Modelon Impact and type:
“*Improve the performance of the compact vehicle in VDL.*”

The agent does not start running simulations. It proposes a way in:
“*I suggest we start with a combined longitudinal and lateral steady-state analysis to establish how far the compact is from its maximum performance envelope. This gives us an objective baseline before we change anything. Shall I proceed with this approach?*”

This is a good engineering instinct. You do not optimize before you understand the baseline. The agent knows this and proposes it unprompted.

I confirm the direction and narrow the scope: *“Good. Let’s limit it to chassis parameters, I want to focus on the balance of the car.”*

The agent proposes the parameter space: roll centers, anti-pitch and anti-dive hardpoints, mass location, inertia, springs, dampers, anti-roll bars, tires, and force distribution between traction and braking.

I adjust: *“Focus on springs, dampers, anti-roll bars, and mass distribution. Leave the hardpoints and inertia for a second phase.”*

The agent updates the scope and, before running anything, proposes a validation strategy: “*The steady-state combined handling diagram will show us the performance envelope and where the balance breaks down. I recommend we complement it with dynamic validation: step steer for transient yaw response, tip-in and tip-out while cornering for combined load transfer, and a four-post rig to validate ride and road holding. Does this plan work for you?”*

I agree. The agent executes.

![](https://modelon.com/wp-content/uploads/2026/03/Agentic_AI_VDL_Figure1_V2.png)

*Figure 1: *The Modelon Impact workspace with the compact vehicle chassis model configured for combined longitudinal and lateral steady-state analysis. The Vehicle Dynamics Library provides the validated model components the agent builds with.**

##### Why This Way of Working is Productive

The exchange above took a few minutes. The value is not the speed, it’s the structure. Starting with a high-level question forces clarity about what I actually want, before any simulation time is spent. The agent’s proposal gives me something concrete to react to, which is almost always easier than specifying a study from scratch.

Adjustment is low cost, I redirect with a sentence rather than reconfiguring a study. Agreement creates a shared understanding of what will be done and why, so when results arrive I know what to look for.

This is not a new way of working. It is the way good engineers have always worked together, one proposing, one adjusting, both aligned before execution begins. What is new is that the execution side of that partnership has become available at a scale and speed that changes what is practical.

A study that previously required a full day of setup, execution, and interpretation can now be proposed, refined, and completed in the time it used to take just to set up. That changes what you can afford to explore.

##### What Makes this Possible in Modelon Impact

This workflow does not run on general-purpose AI alone. It runs on three things working together.

1. **Validated physics models.** The [Vehicle Dynamics Library](https://modelon.com/library/vehicle-dynamics-library/) in Modelon Impact contains validated, production-grade models of chassis, suspension, tires, and powertrain components. When the agent configures a combined handling diagram study or a four-post rig simulation, it is not inventing physics, it is exercising models whose behavior is already trusted. The results mean something because the models mean something. An AI agent operating on unvalidated or black-box models produces results that look plausible but cannot be acted on. Validated physics is what makes the output decision-grade.
2. **The ability to build exactly the right model.** Modelon Impact and the Vehicle Dynamics Library give the agent the building blocks to assemble the study the problem requires, not a generic approximation. For the compact vehicle study, that means a full chassis model with the right suspension kinematics, the right tire model, and the right experiment structure for each maneuver. The agent does not adapt to what is available. It builds what is needed.
3. **The ability to operate the simulation environment.** Modelon Impact is programmable. The agent can set up experiments, sweep parameters, run cases, collect results, extract plots, and navigate deep links within the platform, all through a structured interface that returns real data, not text descriptions of data. This is what allows autonomous execution to produce a real engineering report rather than a summary of what a report might contain. These three capabilities, validated models, composable model building, and a programmable simulation environment, are what separate a genuinely useful agentic workflow from an impressive demonstration.

![Pareto front from the combined handling diagram Design of Experiments in Modelon Impact. ](https://modelon.com/wp-content/uploads/2026/03/Agentic_AI_VDL_Figure2.png)

*Figure 2: *Pareto front from the combined handling diagram Design of Experiments. Each point represents a chassis configuration from the parameter sweep. The frontier identifies the configurations that achieve the best balance of lateral grip and controllability, with the baseline shown for reference*.*

The agent then validated the shortlisted candidates dynamically. Step steer confirmed transient yaw behavior. Tip-in and tip-out while cornering stress-tested force distribution under real combined loading. The four-post simulation confirmed that the spring and damper changes that helped handling did not compromise ride quality beyond acceptable limits. The output was a structured engineering report: baseline assessment, Pareto analysis, dynamic validation results, and a specific recommendation on how to change the compact, with parameter targets, physical rationale, and expected improvement versus baseline across all validated maneuvers. It was not a summary of findings but a recommendation you can act on.

![The AI-generated engineering report for the compact vehicle study, delivered directly within Modelon Impact. ](https://modelon.com/wp-content/uploads/2026/03/Agentic_AI_VDL_Figure3_V2.png)

*Figure 3: *The AI-generated engineering report for the compact vehicle study, delivered directly within Modelon Impact. The report includes the baseline comparison, Pareto analysis, dynamic validation summary, and a concrete recommendation with expected performance improvement, with full traceability back to the simulation results.**

##### Vehicle Dynamics: A Great...

---

### Elevating HVAC-R & Energy Systems Simulation: New Modelon Library Content
- **URL:** https://modelon.com/blog/elevating-hvac-r-energy-systems-simulation-new-modelon-library-content/
- **Description:** Modelon’s experts understand the unique challenges faced by design and system engineers in the HVAC-R and energy industries. Our latest library content demonstrates our continued commitment to empowering users in modeling cutting-edge HVAC-R equipment and energy systems using validated components.
- **Image:** https://modelon.com/wp-content/uploads/2024/03/Release_2024_1_Energy_v6.png
- **Modified:** 2026-04-27

*This blog overviews library updates relevant to the energy and HVAC-R industries. [Full release notes](https://help.modelon.com/latest/release_notes/impact_2024_1/) can be accessed in the help center.*

*The updated libraries are available today for use in [Modelon Impact](https://modelon.com/modelon-impact/) and other Modelica-supported platforms.*

Modelon’s experts understand the unique challenges faced by design and system engineers in the HVAC-R and energy industries. The latest library content demonstrates our continued commitment to empowering users in modeling cutting-edge HVAC-R equipment and energy systems using validated components.

##### Expanded Heat Exchanger Model Options

Modelon has added two new heat exchanger models commonly used in refrigeration:

- Skin condenser heat exchanger
- Wire on Tube (Sawtooth) heat exchanger

Skin condenser heat exchangers offer advantages in compactness, enhanced heat transfer, reduced refrigerant charge, improved heat rejection, corrosion resistance, and lower air-side pressure drop.
 
Wire on Tube heat exchanger (sawtooth) design is valuable for its high heat transfer efficiency, adaptability to variable conditions, improved heat transfer coefficients, durability, and ease of manufacture.

![](https://modelon.com/wp-content/uploads/2024/02/NEW-Wire-On-Tube-Heat-Exchanger-Model-1024x422.png)

**New Wire on Tube heat exchanger from the Vapor Cycle Library (view from inside Modelon Impact).**

##### Enhanced Flexibility in Routing Configurations

The Heat Exchanger library now offers enhanced flexibility in routing configurations for fin-on-tube heat exchangers, one of the most common for residential heat pumps and air conditioning. New functionality, including partial stacking, templates for splits and joins, and skip tubes, gives users more control over their simulations. Additionally, air-side moisture condensation and Python scripts for calibration to experimental data offer greater accuracy and support more use cases inside Modelon Impact.

##### Industrial Data Format Support

Modelon Impact can now support an additional industrial data format to ensure compatibility and ease of use. Engineers can now utilize Python script to convert ASHRAE compressor test data to efficiency maps. A new compressor model capable of reading ASHRAE data directly has been introduced in the Vapor Cycle library, streamlining the modeling process.

##### Enhancements for Power Plant Simulation and Decarbonization

Power Plant Simulation is essential for facilities incorporating renewable and traditional fuel sources. System simulation software supports evidence-based decision-making, optimizing resource utilization, and driving innovation to mitigate climate change and achieve decarbonization goals.

The latest Modelon libraries release delivers key developments to the Thermal Power Library, ensuring a more efficient and user-friendly experience:

- New rotating bed Direct Air Capturing (DAC) absorber model.
- New two-phase/two-phase heat exchanger.
- New sensors for steam quality and superheating.

![](https://modelon.com/wp-content/uploads/2024/02/NEW-DAC-Rotating-Bed-Model-1024x520.png)

**New Rotating Bed Direct Air Capture model from the Thermal Power Library.**

The new [Modelon Library content](https://modelon.com/modelon-library-suite-modelica-libraries/) represents another step forward in making system simulation accessible, empowering engineers to create more robust, efficient, and sustainable designs. 
 
Modelon invites you to explore the upgraded HVAC-R and energy libraries to experience updates that push system design and simulation forward.

[Reach out](https://modelon.com/contact-us/) to our experts and bring the power of system simulation to your organization.

---

### Data Center Compliance: How to Validate Cold Plate Liquid Cooling Against ASHRAE Guidelines Virtually
- **URL:** https://modelon.com/blog/data-center-compliance-how-to-validate-cold-plate-liquid-cooling-against-ashrae-guidelines-virtually/
- **Description:** Learn how virtual simulation helps validate cold plate liquid cooling designs against ASHRAE guidelines, reduce risk, and improve compliance readiness in data centers.
- **Image:** https://modelon.com/wp-content/uploads/2026/02/ASHRAE_Blog-1200x625-1.png
- **Modified:** 2026-04-27

Today we are beyond the stage where liquid cooling is viewed as experimental, and it stands as a mainstream architectural choice for high density data centers. Cold plates, coolant distribution units (CDUs), rear door heat exchangers (RDHx), and even early two-phase systems are becoming standard topics of engineering conversations. Yet as more operators push beyond 800 W CPUs and multi-kilowatt GPUs, one theme consistently surfaces in design discussions:

**“How do we validate these emerging cooling designs against ASHRAE guidelines before they reach the test lab?”**

At this year’s [ASHRAE](https://ashrae.org/) Winter Conference and AHR Expo, the same question surfaced across seminars, paper sessions, and hallway conversations, especially among OEMs, system integrators, consulting engineers, and data center owners and operators.

Data center designers and OEMs want a faster, safer, and more repeatable way to evaluate liquid cooling performance before committing to physical prototypes; and ASHRAE standards are the benchmark they trust. This post explores why ASHRAE matters for cold‑plate systems, where simulation can support compliance readiness, and how virtual testing helps engineering teams build confidence before touching hardware.

##### Why ASHRAE Matters So Much in Data Center Liquid Cooling

**1. ASHRAE TC 9.9. is the authority for data center thermal design.**

Mechanical and thermal engineers working on data centers treat ASHRAE guidelines, especially those published through TC 9.9, as core engineering references. They define allowable and recommended temperature/humidity envelopes, equipment operating limits, and risk considerations for new cooling technologies.

When vendors and operators hear, “We simulated this according to ASHRAE TC 9.9 recommendations,” it immediately signals:

- credibility.
- alignment with industry practice.
- an understanding of engineering constraints data centers work with.

**2. ASHRAE standards continuously evolve**.

One important reminder from the ASHRAE conference: most ASHRAE standards undergo systematic revision cycles. For vendors developing cold‑plate systems or CDUs, this creates a moving target.

- Temperature ranges update.
- Test methods change.
- Efficiency expectations increase.
- Safety and leakage guidelines tighten.

Simulation helps teams explore how next‑generation requirements may affect their product designs before they invest in new lab testing or hardware revisions.

**3. ASHRAE guidelines mitigate risk for emerging fluids**.

A major theme at the conference this year was the uncertainty about the chemistry of technical fluids, coolants, and refrigerants:

- Viscosity behavior
- Flammability
- Volatility
- Environmental impact
- Compatibility with seals and pumps

As one presenter noted: with new fluids, unexpected viscosity can prevent a CDU from reaching target temperatures meaning the designed cooling system will fail to extract the required heat.

Performing test physically for multiple fluid options is expensive and sometimes infeasible. Virtually evaluating fluids against ASHRAE‑aligned temperature envelopes provides an early screening process before real‑world trials.

##### The Compliance Challenge: Cold‑Plate Liquid Cooling Isn’t Just “More Cooling”

Cold‑plate systems introduce new engineering challenges that traditional air‑cooled validation workflows don’t capture:

- transient thermal behavior during power spikes.
- pump staging and efficiency tradeoffs.
- impact of coolant viscosity on flow distribution.
- temperature deltas across plates under nonuniform heat loads.
- CDU capacity and stability under varying inlet conditions.
- failure mode impacts, including air ingress or partial blockage.

ASHRAE guidelines give the envelope, but not the performance curve inside that envelope. That gap is exactly where virtual testing earns its value.

##### Where Virtual Simulation Supports ASHRAE‑Aligned Compliance Readiness

A growing number of engineering teams are now using simulation to support earlier, faster, and more cost‑effective alignment with ASHRAE expectations. Below are the areas where virtual testing provides immediate value.

**1. Virtual Rating Tests Before Lab Testing**

ASHRAE’s standards for methods of testing/rating provide the skeleton for many lab procedures:

- **ASHRAE 198** (test methods for packaged equipment)
- **ASHRAE 37** (recirculating equipment)
- **AHRI 920** (for DOAS moisture removal, relevant for environmental conditioning)
- **TC 9.9 liquid‑cooling guidelines** (operating envelopes, reliability guidance)

While not all apply directly to cold plates, the philosophy carries over:

**Define boundary conditions → enforce operating limits → evaluate steady‑state and transient responses.**

Simulation can replicate these same constraints to help teams answer questions like:

- Does my cold‑plate system maintain safe chip temperatures under ASHRAE recommended conditions?
- What happens when I operate near the edges of the allowable range?
- How sensitive is my system to coolant viscosity, flow rate, or pump curve shape?
- How will a design change in geometry, fluid choice, or plate thickness impact compliance risk?

This becomes especially valuable when hardware labs are overscheduled, expensive, or unavailable.

**2. Multi-fluid Screening and Coolant Evaluation**

At the conference, engineers emphasized that fluid choice is a tricky step towards cold‑plate adoption. New coolant formulations continue to enter the market, each with different:

- Thermal conductivity
- Specific heat
- Viscosity curves
- Environmental profiles
- Supplier guarantees

Rather than testing fluids physically one by one, simulation lets teams virtually:

- map thermal performance.
- evaluate CDU and pump impact.
- quantify viscosity‑driven flow distribution issues.
- test system behavior under ASHRAE allowable extremes.

This improves engineering confidence before selecting anything for physical trials.

**3. Control Logic, Pump Staging, and System Stability**

One of the more memorable insights from the ASHRAE sessions:

*Limiting a pump to its highest‑efficiency region, then staging the next pump early, outperformed waiting until the running pump reaches full load.*

This is counterintuitive and costly to learn through trial and error.

Simulation enables:

- virtual pump staging strategies.
- controller tuning.
- failure mode experimentation.
- avoidance of unstable regions (e.g., low‑flow cavitation).
- CDU performance mapping under transient loads.

These kinds of control‑sequence optimizations came up in the Modelica‑focused talks. Many research groups demonstrated how variable‑step simulations help explore fast (second‑scale) flow dynamics and slow (hour‑scale) thermal behavior within a unified model, without impractically long simulations.

**4. Reducing Cost and Risk of Physical Testing**

Physical test labs are expensive to build and operate. ASHRAE speakers highlighted that:

- lab configuration is dictated by testing standards.
- equipment must often be retested when standards update.
- prototype failures can destroy expensive components.
- nonstandard fluids may pose risks to equipment.
- lead times for lab access are increasing.

Virtual testing doesn’t replace certification, but it does:

- reduce the number of physical prototypes.
- improve the probability of passing tests on the first attempt.
- identify high‑risk configurations early.
- support multi‑scenario testing that labs cannot feasibly duplicate.

This is especially useful as cooling transitions accelerate.

##### A Real Story from the ASHRAE Conference: The Value of Virtual Testing

One of the most compelling examples came from a DOE‑funded data center project. Simulation revealed that:

1. **The operating control algorithm was skipping a portion of the designed control logic
   **→ This created unnecessary mechanical loads.
2. **The system was overcooling air and requiring electric reheat
   **→...

---

### Hybrid Modeling with a Reduced Order Model and Neural Network App
- **URL:** https://modelon.com/blog/hybrid-modeling-with-a-reduced-order-model-and-neural-network-app/
- **Description:** Discover how Reduced Order Modeling (ROM) accelerates system simulation in Modelon Impact. Learn how a hybrid modeling approach—combining physics-based and data-driven techniques—optimizes efficiency without sacrificing accuracy. See our neural network-powered app made for the PHyMoS project.
- **Image:** https://modelon.com/wp-content/uploads/2025/03/PHyMOS-Blog-Cover.png
- **Modified:** 2026-04-27

Physical modeling offers predictive capabilities and a deeper understanding of system behavior through fundamental physics. However, in some cases, achieving lower computational costs or faster processing is more desirable. These use cases can benefit from hybrid modeling—a method that combines models with different traits, such as physics-based components and data-driven techniques. Reduced Order Modeling (ROM) plays a crucial role in this approach by simplifying complex models while maintaining accuracy, enabling faster simulations and real-time optimizations.

##### Introducing the Proper Hybrid Models for Smarter Vehicles (PHyMoS) Project 

In the last three years, Modelon has been proud to work on a government-funded research project in Germany. This project is called [*PHyMoS*](https://phymos.de/page/publications/), which stands for *Proper Hybrid Models for Smarter Vehicles*. We worked with other industry and research partners on this project.

The project focused on accelerating the development of smarter vehicles and, therefore, being able to model the dynamic behavior of these vehicles in a fast and efficient manner was key. We looked into ways to create data-driven models using Artificial Intelligence. We focused on a few different use cases. Physics-based modeling provided the data for these models. The project aimed to find the best balance between accuracy and manual effort.

![](https://modelon.com/wp-content/uploads/2025/03/PhymosGroupMunich.jpeg)

**Group photo of PHyMoS partners at the final meeting in Munich*  *

As part of the project outcome, Modelon demonstrated a partially automated workflow in [Modelon Impact](https://modelon.com/modelon-impact/) to generate a reduced-order Modelica model based on selected (sub)systems and key variables. In the next section, we will walk through this step by step using an example of an automotive fuel cell system model.

##### Reduced-order modeling workflow in Modelon Impact

Reduced Order Modeling (ROM) enables faster simulations without compromising accuracy within a defined operating range—critical for the PHyMoS project.

By simplifying complex models, ROM allows engineers to perform rapid design iterations, sensitivity analyses, and real-time optimizations. This is particularly beneficial when integrating data-driven techniques, as it streamlines computational workloads while maintaining predictive reliability.

Our workflow is considered a ROM technique that simplifies models using a data-driven approach, primarily leveraging neural networks. To support this, we developed a custom app written in Python as a Jupyter Notebook and rendered through Voila, providing a user-friendly interface with intuitive widgets for ease of use.

![](https://modelon.com/wp-content/uploads/2025/03/Neural-Network-App-GUI-overview.jpeg)

**Neural Network App GUI overview**

##### Selecting a component to be ‘*reduced’*

We start by selecting a subsystem/component we would like to use. The ROM technique retains subsystem/component behavior in a pre-determined operating range while speeding up simulation time. In the example below, we have a system model of an electric car driving cycle, and we want to simplify the fuel cell module in this case. Let’s assume we are interested in how the power load, ambient pressure, and temperature affect hydrogen consumption and, therefore, getting rid of the complexity of geometry, heat transfer, etc.

![](https://modelon.com/wp-content/uploads/2025/03/Fuel-Cell-Module.jpeg)

**Fuel cell module selected for reduced-order modeling**

##### Data generation for neural network training

After we identify the key input/output of our fuel cell module, we create a manual wrapper around it to expose the selected key variables and prepare a test bench to generate data from this physical model by means of batch simulation. This data will be used afterward to train the neural network model.  

Neural networks require lots of simulation data for proper training. By leveraging simulation data in this development process and eliminating bottlenecks like manual meshing and long solver times, we are able to contribute a stream of quality data. This approach can accelerate neural network training and unlock greater potential for a hybrid modeling approach.  

The plot below shows the result of 100 simulations, sweeping the input within the given range:  
Target power: 100kW to 150kW  
Ambient temperature: 293K to 303K  
Ambient pressure: 1 bar to 1.1 bar

![](https://modelon.com/wp-content/uploads/2025/03/Physical-data-on-fuel-flow.jpeg)

**Physical data on how fuel flow is affected by power setpoint and ambient temperature and pressure*  *

##### The neural network model

There are two parts to creating the neural network model.

First, the custom app employs [TensorFlow/Keras](https://www.tensorflow.org/guide/keras) package for creating a [Feed-Forward Neural Network](https://www.geeksforgeeks.org/feedforward-neural-network/) (FFNN) structure. The user can adjust the number of layers, the number of neurons for each layer as well as the activation function used. Once the structure is ready, the app will use the physical data from earlier to train the network, generating what we call *weights* and *biases*, which determine how the model behaves. The following plots depict the neural network prediction against the physical model output (note that the values shown are normalized values).

![](https://modelon.com/wp-content/uploads/2025/03/Normalized_TargetPower.jpeg)

![](https://modelon.com/wp-content/uploads/2025/03/Normalized-T_ambient.jpeg)

**Neural network predictions on fuel flow based on power setpoint and ambient temperature**

Second, we transfer the generated weights and bias values and package them into a Modelica-based model using components from an open-source [NeuralNetwork](https://github.com/AMIT-HSBI/NeuralNetwork) library. The following figures show how the neural network fuel cell model is validated against the original module. Given the same variations of power setpoint and ambient conditions over a period of T=1000s, hydrogen consumption is deduced.

![](https://modelon.com/wp-content/uploads/2025/03/Test-bench-model-1.jpeg)

![](https://modelon.com/wp-content/uploads/2025/03/image.png)

**Test bench validation against the physical fuel cell module**

In the simple test bench above, a significant speed-up from 29.18s elapsed simulation time to 0.047s is observed. The plotted key output, which is the hydrogen mass flow rate, shows similar values. Some discrepancies come from the initialization stage and the fuel cell’s dynamic response due to the changing power setpoint. 

##### Further applications of neural network methodologies

During the project, we used this integrated workflow for other parts, like heat exchangers. Overall, we saw a 29.13-second improvement in performance while keeping acceptable accuracy. 

In this test bench, simulation time decreased by 99.8%, from 29.18 seconds to just 0.047 seconds.

The custom app’s neural network methodology offers far more development potential. Other algorithms and frameworks can be explored to capture the dynamics behavior better, e.g., physics-informed neural networks (PINNs) enabling users to embed physical laws to guide the learning process. Researchers can also consider future work to fully automate the reduced-order modeling process.

##### Thank you to our partners in Innovation

We truly value the teamwork and knowledge of our respected partners in the PHyMoS project. These partners include:

– [ESI Germany](https://www.esi-group.com/company/worldwide/germany) 
– [LTX Simulation](https://www.ltx.de/english.html) 
– [The Robert Bosch Group](https://www.bosch.com/company/)
– [TLK-Thermo](https://www.tlk-thermo.com/en/)
– [XRG Simulation](https://xrg-simulation.de/en)
– [University of Augsburg](https://www.uni-augsburg.de/en/)
– [Technical University of Braunschweig](https://www.tu-braunschweig.de/en/)
– [Bielefeld...

---

### Unlocking Electrified System Modeling at Longer Time Scales
- **URL:** https://modelon.com/blog/unlocking-electrified-system-modeling-at-longer-time-scales/
- **Description:** Modelon Impact now enables fast, long-timescale simulations for electrified systems modeling—preserving circuit physics, enabling multi-domain analysis, and accelerating engineering insight by up to 100x. 
- **Image:** https://modelon.com/wp-content/uploads/2025/05/Blog-hero-Unlocking-Electrified-Systems.png
- **Modified:** 2026-04-27

Engineers designing electrified systems—from smart grids to electric vehicles—rely on simulation tools to validate performance, efficiency, and control strategies. There has often been a trade-off when simulating these systems: physics-based accuracy often meant short time horizons. 

The computational expense of accurate circuit models meant that these were unavailable for studying longer real-world scenarios or for simulating combined electrical and thermo-fluid systems. 

This is no longer a limitation with the [latest version of Modelon Impact’s Electrification Library](https://help.modelon.com/latest/library_documentation/release_notes/electrification/version_1_13/), which has unlocked the ability to simulate these systems over extended durations (many minutes and hours) with preserved accuracy of electric circuit models.

#### Faster Simulations of Systems with Electric Power Converters

Simulation performance will typically be a bottleneck for electrified systems due to differences in scale. The dynamics of the power electronics are so much faster than the time scales of the relevant operating scenarios. This is especially true when alternating currents (AC) are involved, such as in systems with inverters and rectifiers connected to the electric grid or driving electric motors. 

Some examples where longer time scales are important include: 

- Photovoltaics supplying the electric grid with slowly changing weather conditions 
- Fuel cells with slowly responding thermo-fluid dynamics 
- Variable speed compressors and their impact on overall thermal management 
- Full-length drive cycles with electric vehicles

![](https://modelon.com/wp-content/uploads/2025/05/SolarPowerToGrid.png)

*A photovoltaic array and battery supplying power to the electric grid, via electric power converters that involve both DC and AC (direct and alternating currents).*

The [latest version of *Modelon Impact’s Electrification Library*](https://help.modelon.com/latest/library_documentation/release_notes/electrification/version_1_13/) brings dramatic performance gains—in some cases, up to 100x faster simulations compared to previous versions of the library—using models that adapt the level of detail to match the relevant time horizon.  

This has unlocked the ability to ask and answer a big set of engineering questions, over the full range of relevant time scales, with the same tool and model structure that was previously only available for shorter scenarios. 

Some of the typical engineering questions related to power electronics include:

- What is the range of operating points for the expected real-world scenarios? 
- Are there any scenarios where the component limits are reached? (electrical, thermal, software) 
- Is the control implementation robust to disturbances and failure events? 
- Will there be any dynamic issues when these electrical components are integrated into a larger multi-domain system?

#### Why A Low-Fidelity Model Doesn’t Mean Low Accuracy

Adjusting model fidelity always comes with trade-offs. A common way to achieve fast performance is to rely on empirical models. But this also means we lose information about the physics, making it challenging to use component data directly, and valid results can only be ensured for operating conditions where we have data.

With our new low-fidelity models, we have taken a different approach: We scale down the frequency content. The key is that it is not relevant to resolve, e.g., 50 Hz sinusoid waveforms, if we study a scenario that is several hours long. With these models, we can reduce the resolution to get good performance, while still capturing the same results on average as the more detailed models.

![](https://modelon.com/wp-content/uploads/2025/05/Plots.png)

*Heat losses in an inverter, comparing the new low-fidelity model with a more detailed one. The losses are the same on average over the longer time horizon.*

These are compatible models on a fidelity spectrum, which makes it easy for engineers to adapt the detail to the relevant timescale. They can zoom in to study rapid transient events and waveforms measured in milliseconds or zoom out to study slow trends over operating cycles measured in minutes or hours, while staying in the same modeling paradigm, re-using the same parameters and controllers.

#### Leveraging Multi-Fidelity for Multi-Domain Simulations

Electrified systems are inherently multi-domain, spanning electrical, mechanical, thermal, and fluid dynamics. However, simulating these domains together can be computationally prohibitive if their time constants aren’t aligned. 

This challenge is solved with the latest version of the Electrification Library, which allows engineers to adapt the frequency content of the electrical models to match the dynamics of the other physical domains. 

This allows engineers to answer questions about integrated multi-domain systems that previously would have been unfeasible, for example: 

- Validate the control of an electric motor and inverter for a variable speed compressor in an air conditioning system. 
- Validate the robustness of the electric power supply for a fuel cell or electrolyzer. 

![](https://modelon.com/wp-content/uploads/2025/05/ElectrifiedVaporCycle.png)

*An electric motor and inverter driving a variable speed compressor in an air conditioning vapor cycle (a two-phase thermo-fluid system).*

This flexible model fidelity means that engineers can get the right fidelity for studying multi-domain behaviors of the larger system, but can also scale up the detail when they need to zoom in and study much faster electrical dynamics, using the same model structure in Modelon Impact.

##### Built for Engineering Impact

What makes these updates truly powerful isn’t just the improvement in simulation speed—it’s the ability to answer more of the right engineering questions more efficiently and confidently. Engineers can rely on physics-based modeling that remains accurate over extended, application-relevant time scales. They can simulate interactions across electrical, mechanical, thermal, fluid, and control domains, within a unified environment. The scalable fidelity of the models enables seamless transitions between high-detail and system-level representations with fewer types of models to maintain and easier validation. This flexibility provides insights supporting early-stage architecture decisions and detailed component-level validation, without the need to switch tools. 

Whether you’re evaluating hybrid energy systems, power distribution architectures, or electric drivetrains, Modelon libraries give you the power to simulate confidently over the time scales that matter most. 

##### Get Started Today

Ready to see how scalable simulation can transform your work? 

[Book a Demo](https://modelon.com/book-a-demo/)

[Explore the Electrification Library](https://modelon.com/library/electrification-library/)

---

### Simulating Coolant Distribution Units (CDUs): The Future of Data Center Cooling
- **URL:** https://modelon.com/blog/simulating-coolant-distribution-units/
- **Description:** Explore simulating CDUs to enhance liquid cooling efficiency in data centers and reduce energy consumption dramatically.
- **Image:** https://modelon.com/wp-content/uploads/2025/06/Featured-Image-2-1.jpg
- **Modified:** 2026-04-27

*Why CDUs are the key to liquid-cooling efficiency and how they can be simulated.*

#### Introduction to CDUs: A Critical Enabler

Data centers are critical to modern society, and their rapid expansion—driven by artificial intelligence, cloud computing, and hyperscale infrastructure—is transforming the way we process and store data. A significant increase in electricity consumption accompanies this growth. In Q1 2025 alone, U.S. data centers consumed an estimated 200 terawatt-hours (TWh), surpassing the 147 TWh used in all of 2023 ([Q1 2025 Data Center Activity Report](https://www.landgate.com/news/q1-2025-data-center-activity-report)).

Much of this energy powers the ever-evolving microprocessors that fuel these technologies. With the demands of AI, parallel processing, and the Internet of Things, microprocessors are becoming faster and more efficient—but also more power-hungry. The thermal design power (TDP) for microprocessors is expected to reach 700 W this year [1], generating substantial heat that must be effectively managed to maintain optimal performance.

This heat presents a dual challenge. First, cooling accounts for around 40% of the total energy used by data centers [2], placing a significant burden on overall efficiency. Second, air-cooling solutions are reaching their limits, struggling to manage TDPs exceeding 280 W [1]. Finding effective cooling strategies is now critical to sustaining data center growth without compromising performance or energy consumption.

Liquid cooling stands out as the most effective solution to the escalating heat challenges in modern data centers, thanks to water’s heat removal capacity, nearly three times that of air [3]. This superior cooling potential makes liquid systems essential for supporting higher thermal design power and ensuring reliable performance. Solutions range from indirect and direct systems to single- and two-phase immersion cooling, all of which rely on a central coolant distribution unit (CDU) to deliver and manage the liquid’s cooling power.

> > The CDU is a thermo-mechanical system that integrates key components, including pumps, control valves, sensors, and a heat exchanger, to manage the cooling fluid efficiently circulated between IT racks and the facility’s primary chiller plant. The CDU serves as an interface between the rack-side cooling loop and the chilled water loop, allowing for precise thermal control and isolation of the two systems.

#### The Simulation Gap: Why Modeling the CDU Matters

To transition from air-cooled solutions to liquid cooling, data center operators require tools that facilitate informed decision-making and planning for the transition. While real physical testing can be employed, this approach is often time-consuming and expensive, particularly when considering numerous conditions and variables. Simulation tools can help accelerate the design and engineering phase of systems by allowing for the virtual testing of hundreds of cases before initiating real-world testing.

However, while there may be multiple simulation tool options for designing and analyzing air-cooled systems, such as computer room air handlers (CRAHs), the same cannot be said for designing CDUs within liquid cooling systems. Current tools lack the flexibility to model CDUs and understand their performance within a model that includes broader data center building infrastructure (e.g., cooling towers, electrical grid, air zones, etc.).

This is where Modelon Impact shines. Built on the Modelica language, Modelon Impact is one of the only tools that offers the flexibility needed to model both air- and liquid-cooled systems effectively.  Being a platform that supports multi-domain libraries, engineers can leverage multi-disciplinary system modeling to explore and address key operational challenges, such as creating the optimal balance between air and liquid cooling within a given facility through system sizing, operational cost assessment, and power consumption simulation.

Ensuring a CDU will cool data racks efficiently, even when thermal loads spike or under low-load conditions, and in other extreme cases.

#### Liquid Cooling at Low and Peak Load: Modelon Impact Simulation Scenario

Focusing on the entire data center in a simulation often leads to overly complex models that are difficult to interpret and act upon. Instead, starting the simulation around the CDU provides a more actionable and insightful approach. CDUs serve as the critical interface between facility cooling infrastructure and IT equipment, making them ideal focal points for evaluating system performance, especially during peak thermal loads.

Take, for example, the system model studied by Heydari [4] shown in Figure 1 and its corresponding Modelica model, Figure 2.

![Figure 1 Schematic of Liquid Cooled Data Center](https://modelon.com/wp-content/uploads/2025/06/Figure-1.png)

*Figure 1*

![Figure 2 - Corresponding Modelica model](https://modelon.com/wp-content/uploads/2025/06/Figure-2.png)

*Figure 2*

The CDU is the subsystem block at the top, characterized by its heat exchanger effectiveness, flow capacity, and supply temperature control. The array of racks that connect can be modified to represent any number of racks with varying sizes and IT heat load profiles. Additionally, the row manifolds can be modeled to capture the pressure characteristics of the system.

In this example, a central CDU circulates water on the secondary side and 25% propylene glycol on the primary side, which is distributed through the rack manifolds. The CDU was initially tested with a subset of racks under low heat load conditions. As noted by Heydari et al. [4], such conditions can lead to temperature fluctuations on the primary side of the supply. To mitigate these fluctuations, a 3-way valve on the water side adjusts the flow. However, frequent valve actuation—due to aggressive opening and closing—can result in mechanical wear or failure. To address this issue, a controller is required to smooth the valve operation and ensure more stable control. Modelon libraries offer a variety of blocks that can be combined to evaluate control strategies. The simulation was designed to evaluate the control strategies proposed by Heydarii [4], considering the variations in heat load shown in Figure 3. Figure 4 presents the performance of a stable PID controller, contrasted with an unstable configuration. The results clearly demonstrate that a properly tuned PID significantly reduces oscillations and improves system stability.

![Figure 3 - Heat Load Per Rack](https://modelon.com/wp-content/uploads/2025/06/Figure-3.png)

*Figure 3*

![Figure 4 - Stable vs Unstable Control](https://modelon.com/wp-content/uploads/2025/06/Figure-4.png)

*Figure 4*

The second test considered the CDU with the full set of server racks and manifolds as shown in Figure 5. An additional PID was included to control the speed of the primary side pump. For this simulation, high load conditions were considered; the heat load schedule is described in Figure 6.

![Figure 5 - CDU model with full set of heat racks](https://modelon.com/wp-content/uploads/2025/06/Figure-5.png)

*Figure 5*

![Figure 6 - heat load schedule](https://modelon.com/wp-content/uploads/2025/06/Figure-6.png)

*Figure 6*

The simulation was configured to evaluate the controller behavior and overall system performance by varying the technical water (TW) supply temperature setpoint from 25 °C to 32 °C. Figure 7 illustrates the resulting supply temperature profile, while Figure 8 shows the return temperature. A distinct peak is observed when a high heat load is applied. The system responds by attempting to stabilize the temperature, exhibiting the dynamic behavior shown.

Figure 9 illustrates the variation in propylene glycol flow resulting from the variable-speed pump. The pump adjusts the flow rate in response to the load, initially reducing flow under lower demand and then ramping up to mitigate the peak...

---

### Simulating Thermal Fluids: Powering Innovation Through Virtual Design
- **URL:** https://modelon.com/blog/simulating-thermal-fluids-powering-innovation-through-virtual-design/
- **Description:** Discover how Modelon’s thermal-fluid simulation solutions empower engineers to design efficient, resilient systems across energy, HVAC, and transportation industries reducing risk, cost, and time to market.
- **Image:** https://modelon.com/wp-content/uploads/2025/11/Featured-Image.png
- **Modified:** 2026-04-27

##### Complexities of Thermal Fluids

From power generation and HVAC systems to electric vehicles and data centers, thermal fluids are central to how energy moves and systems perform. Their behavior determines efficiency, reliability, and sustainability—but it’s far from simple to predict.

Real-world thermal-fluid systems are inherently nonlinear and operated dynamically, with temperature, pressure, and flow intricately interdependent. These complex interactions often elude traditional testing methods, which struggle to replicate the full spectrum of operating conditions. While physical testing yields valuable insights, it remains time-intensive, expensive, and constrained in scope — limiting its effectiveness for comprehensive system validation.

**Simulation** bridges that gap, enabling engineers to explore system behavior virtually, optimize designs early and more cost-effectively, and make data-driven decisions with confidence.

##### Modelon’s Approach to Thermal-Fluid Simulation

Accurate simulation is essential to modern engineering. The platform, [Modelon Impact](https://modelon.com/modelon-impact/), combines decades of thermal-fluid expertise with an open, model-based approach that supports both technical depth and organizational collaboration.

With Modelon Impact, engineers can:

- **Capture multiphysics interactions:** Model how fluids, heat transfer, and control systems interact under real operating conditions.
- **Scale from components to systems:** Simulate everything from a single pump or heat exchanger to an entire thermal management loop.
- **Optimize early:** Test design trade-offs virtually to reduce prototype costs and development time.
- **Collaborate seamlessly:** A cloud-native environment enables teams across engineering, R&D, and operations to share models and align decisions.

This integrated workflow helps organizations move from reactive testing to proactive design, improve performance, and reduce risk and cost.

##### Simulation Applications Across Industries

Modelon’s thermal-fluid simulation solutions are used wherever energy transfer drives performance:

- **Data Centers**: Enable efficient heat evacuation directly at the chip level through advanced methods like immersion cooling, two-phase flow, and direct-to-chip liquid cooling. Reduce thermal resistance and support higher compute densities.  Ensure scalable, energy-efficient heat rejection across the entire infrastructure with facility-level integration.
- **Energy & Power:** Improve system efficiency and reliability through modeling of district heating, renewable integration, and cooling loops.
- **HVAC & Refrigeration:** Design and refine systems for comfort, sustainability, lower energy consumption and to meet energy efficiency and refrigerant regulations.
- **Automotive & Aerospace:** Simulate advanced fuel, oil, and battery cooling systems to enhance safety and performance.
- **Process Industries:** Analyze and optimize fluid-thermal dynamics to increase throughput and quality.

Built on the **Modelica® standard**, Modelon’s libraries provide transparent, reusable models that adapt to specific system needs — ensuring flexibility and longevity for your simulation investments.

##### Building Business Resiliency Through Simulation

In today’s competitive landscape, resiliency is as critical as efficiency. The ability to anticipate how systems will behave under different conditions *before *they’re deployed gives organizations a decisive advantage.

Thermal-fluid simulation enables teams to:

- **Predict and mitigate risks** by testing “what-if” scenarios digitally.
- **Ensure operational continuity** through designs that maintain stability under varying loads, environments, or disruptions.
- **Reduce downtime and maintenance costs** with systems optimized for performance and durability.
- **Adapt faster** to new technologies, regulations, and market demands without costly redesigns.

By embedding simulation into the design process, organizations strengthen their engineering resilience and make better decisions faster with lower risk.

##### A trusted Partner in Model-Based Engineering

For over two decades, **Modelon** has helped engineering organizations bring simulation into the center of their design and decision-making processes. The open, standards-based tools empower teams to design with confidence, collaborate effectively, and respond to change with agility.

See how Modelon supports the energy, HVAC, and transportation industries with advanced thermal-fluid simulation. Explore our [Energy & Power](https://modelon.com/industries/energy-power-system-simulation-optimization-software/)and [HVAC & Refrigeration](https://modelon.com/industries/hvac-system-simulation-solution/) solutions.

---

### Turning Frost into Warmth: A Data Center Holiday Story
- **URL:** https://modelon.com/blog/turning-frost-into-warmth-a-data-center-holiday-story/
- **Description:** See how simulation turns data center waste heat into warmth for a winter community, using heat pumps and district heating modeled in Modelon Impact.
- **Image:** https://modelon.com/wp-content/uploads/2025/12/image.jpg
- **Modified:** 2026-04-27

As the year winds down, we found inspiration far from our usual engineering corridors in a quiet little town near the North Pole. It’s a place where winter never takes a break, where lights twinkle through long nights, and where warmth is more than comfort—it’s community.

This year, our team imagined a story set there. A new AI data center sits just outside the town, humming through ice-cold winds. Inside, its servers work tirelessly generating heat that usually disappears unused into the frost-filled air. But this town needed warmth. And we had a different idea.

Using [Modelon Impact](https://modelon.com/modelon-impact/), we simulated an integrated cooling system where the data center’s waste heat doesn’t escape…but flows back into the community. Through a high-efficiency heat pump and a well-designed district heating loop, every watt of excess heat becomes a source of comfort, warming homes, schools, and those cozy holiday gatherings.

##### Modelon’s Thermal Systems Expertise

This little North Pole tale captures what we’ve been working toward all year, powered by our deep expertise in thermal systems:

- **Efficient Data Center Cooling:** Designing systems that manage high power densities while maximizing heat recovery.
- **Integrated Thermal and Energy Systems Engineering:** Connecting diverse systems like vapor compression cycles and district heating.
- **Model-Based Design using Modelon Impact:** Enabling rapid optimization and validation before physical build.
- **Sustainable Solutions:** Designing systems that give more energy value than they take.

##### The Integrated System Architecture

- **Smart Cooling for the Data Center:** We modeled the thermal dynamics of the cooling loop to ensure peak server efficiency and optimal waste heat capture as shown in Figure 1. This model can be reduced in fidelity maintaining required accuracy while moving to high level optimization studies in the next step.
- **Sustainable Heating for the Town: **The district network model guarantees reliable flow and temperature to every building purely acting as a consumer of heat.
- **A Circular Energy Story:** Modeled from end-to-end using our robust libraries as shown in Figure 2.

![](https://modelon.com/wp-content/uploads/2025/12/DC_AHU_HolidayBlog_25.png)

**Figure 1: Data center with air handling unit (AHU), coolant distribution unit (CDU), chiller plant, and cooling tower**

![Integrated simulation model used for optimization](https://modelon.com/wp-content/uploads/2025/12/DC_Model_HolidayBlog_25.png)

**Figure 2: The integrated simulation model used for optimization**

##### Key Simulation Results

Simulations confirmed the system’s feasibility and high performance, proving the economic and environmental benefits as shown in Figure 3.

![Heat flow rates](https://modelon.com/wp-content/uploads/2025/12/DC_HeatFlow_HolidayBlog_25.png)

*Figure 3: Simulation results from the optimized system*

##### Simulation for Community Impact

At Modelon, we’ve spent years building superlative expertise in:

- **Data Center Cooling Systems**: Mastering the complexity of liquid cooling, chiller/CRAC stacks, and energy optimization.
- **Heat Pump and Refrigeration Modeling**: Optimizing the Coefficient of Performance (COP) for maximum energy upgrade.
- **District Heating and Circular Thermal Systems**: Integrating complex networks for sustainable community energy.

This experiment reflects how those strengths can come together using rigorous simulation to turn an engineering challenge into an opportunity for sustainability and community impact.

As 2026 approaches, we’re excited to keep pushing engineering forward—where smart simulations meet real-world impact, and where even the coldest places can find new warmth.

Wishing you all a wonderful holiday season and a bright, sustainable new year from our engineering family to yours. Stay warm!

---

### An Introduction to the Fundamentals of HVAC System Simulation
- **URL:** https://modelon.com/blog/an-introduction-to-the-fundamentals-of-hvac-system-simulation/
- **Description:** Understand HVAC system simulation, from modeling building loads and equipment behavior to testing control strategies, reducing risk, and improving energy efficiency in buildings.
- **Image:** https://modelon.com/wp-content/uploads/2026/01/Jan-Post-1-Blog-Featured-Image.png
- **Modified:** 2026-04-27

In today’s world, heating, ventilation, and air conditioning (HVAC) systems play an essential role in maintaining comfortable and healthy indoor environments. From residential buildings to large commercial complexes, HVAC systems account for a significant share of building energy consumption. As a result, optimizing system design, control strategies, and operational performance are major priorities in the construction and facilities management fields.

One of the most powerful tools for achieving these priorities is **HVAC system simulation**. By creating a virtual representation of a building’s HVAC setup and testing it under various conditions, engineers, designers, and energy modelers can predict system performance, identify inefficiencies, and propose improvements all before the building is even constructed or the retrofit project starts.

##### 1. What is HVAC System Simulation?

HVAC system simulation involves using specialized software to model how heating, cooling, and ventilation systems will behave in a given building or space under different conditions. By inputting building geometry, construction materials, occupancy schedules, weather data, equipment characteristics, and control logic, the simulator can output.

- **Energy consumption** for heating, cooling, and ventilation.
- **Comfort conditions** such as temperature, humidity, and indoor air quality.
- **Equipment performance**, including system capacity, part-load behavior, and efficiency.
- **Operational costs**, allowing a cost–benefit analysis of different design alternatives or retrofits.

This virtual environment helps building professionals predict whether a proposed HVAC design will meet the desired performance criteria before any real-world implementation saving both time and resources.

##### 2. Why is HVAC Simulation Important?

1. **Energy Efficiency**
   Buildings account for a large portion of global energy use and greenhouse gas emissions. Accurately simulating HVAC performance helps identify system inefficiencies, enabling better design choices and controls that reduce energy consumption and carbon footprint.
2. **Cost Savings**
   By identifying optimal designs and operational strategies early on, owners and engineers can reduce construction costs (e.g., by avoiding oversized or improperly selected equipment) and improve long-term operational savings (lower energy bills, fewer maintenance issues).
3. **Enhanced Comfort**
   Properly controlled and balanced HVAC systems ensure consistent temperatures, humidity levels, and airflow. Simulation allows you to evaluate comfort metrics and fine-tune system components or controls to maintain comfortable indoor environments.
4. **Risk Reduction**
   Using simulation to run “what-if” scenarios—such as extreme weather events or changes in occupancy—helps identify potential system failures or inadequacies before they become costly operational or comfort problems.
5. **Regulatory Compliance and Certification**
   Green building certifications (LEED, BREEAM, WELL, etc.) and energy codes (ASHRAE 90.1, IEC standards) increasingly require detailed energy modeling studies. Accurate HVAC simulations are a key part of meeting these standards.

##### 3. Core Components of an HVAC System Simulation

When setting up or interpreting an HVAC system simulation, it helps to understand the key inputs and outputs:

1. **Building Geometry and Envelope**
   The shape, orientation, and materials of the building structure are crucial. Elements like wall, roof, and window assemblies impact heat gains and losses. Accurate data here sets the foundation for a reliable HVAC model.
2. **Internal Loads**
   Occupants, equipment, and lighting all generate heat. Accounting for these internal heat gains is essential for sizing and simulating HVAC loads properly.
3. **Weather and Climate Data**
   Local weather conditions—temperature, humidity, solar radiation, wind speed—are typically integrated from standardized weather files or local meteorological data.
4. **HVAC Equipment Details**
    – **Type of system** (e.g., VRF, chillers, packaged units, furnaces, boilers).
    – **Capacity** (nominal cooling/heating capacity).**Efficiency** (COP, EER, SEER, AFUE ratings, etc.).
    – **Part-load performance** (how efficiency changes under partial load conditions).
    – **Control strategies** (thermostatic control, variable frequency drives, scheduling, etc.).
5. **Ventilation and Airflow**
    – **Airflow rates** for fresh air, recirculated air, and exhaust.
    – **Duct layout** and leakage considerations.
    – **Filtration** and indoor air quality (IAQ) parameters.
6. **Control Logic**
    – **Setpoints** for temperature and humidity.
    – **Schedules** that reflect occupancy patterns or operational hours.
    – **Automation strategies** (such as reset schedules, occupancy sensors, demand-controlled ventilation).

##### 4. The Simulation Process

1. **Preliminary Data Gathering**
    – Collect or define architectural drawings, building dimensions, material properties, equipment specs, and operational schedules.
2. **Model Creation**
    – Define the building geometry.
    – Assign construction types and material layers.Specify internal loads (occupancy, lighting, equipment).
    – Set up the HVAC system, including all relevant components and controls.
3. **Verification & Calibration**
    – Review inputs carefully to ensure they reflect reality.
    – Where possible, calibrate the model against known data, such as utility bills or measured performance from an existing building.
4. **Simulation Execution**
    – Run simulations for the entire year or selected periods.
    – Examine detailed outputs such as hourly temperature profiles, load breakdowns, and equipment operations.
5. **Analysis & Optimization**
    – Compare results (energy usage, comfort metrics) against targets.
    – Adjust parameters (equipment size, insulation levels, controls) iteratively to find the best combination of performance and cost.
6. **Reporting & Communication**
    – Summarize insights (e.g., cost savings from improved controls, ROI of better insulation) in graphs, charts, and easy-to-understand figures.

##### 5. Best Practices & Tips for HVAC System Simulation

1. **Start Simple**
   It is often better to develop a simpler, conceptual model first, particularly in early design stages, and refine details as more information becomes available.
2. **Use Representative Schedules**
   The occupancy and operational schedules can significantly influence HVAC loads. Gather reliable data if possible, or use conservative approximations when data is unavailable.
3. **Accurate Weather Data**
   Rely on weather files consistent with your building location. Even small discrepancies in local climate conditions (e.g., very humid vs. dry summers) can lead to large divergences in heating/cooling requirements.
4. **Validate with Real Data**
   If you have access to an operational facility, compare the simulation results with actual meter readings or measured performance. This feedback loop will strengthen your model’s credibility.
5. **Focus on System Controls**
   Many real-world issues with HVAC performance stem from poor control strategies. Don’t overlook how the system will be operated, scheduled, and regulated. Small improvements in controls can yield large energy savings.
6. **Iterative Approach**
   HVAC system simulation is most valuable when done iteratively. Experiment with different design strategies, compare results, and refine. Doing so ensures you find an optimal balance of efficiency, comfort, and cost.

##### 6. The Future of HVAC Simulation

The industry is moving toward more integrative and intelligent approaches to HVAC simulation, driven by:

- **Building Information Modeling (BIM)**: Seamless integration of 3D design and energy modeling platforms to reduce the need for duplicate data entry and speed up workflow.
- **Machine Learning & AI**: Advanced algorithms that can...

---

### Digitally Engineering the Future of Data Centers
- **URL:** https://modelon.com/blog/digitally-engineering-the-future-of-data-centers/
- **Description:** Explore how simulation is transforming data center design and reliability in an expert conversation with LBNL’s Michael Wetter.
- **Image:** https://modelon.com/wp-content/uploads/2026/01/Jan-Post-3-Blog.jpg
- **Modified:** 2026-04-27

##### *A Conversation with Simulation Pioneer Michael Wetter*

As data centers scale at an unprecedented pace—driven largely by AI, cloud services, and high-density computing—the industry is confronting a new level of complexity in energy demand, cooling strategies, and the need for near-perfect uptime. Few people understand this challenge better than **Dr. Michael Wetter**, a leading expert in model-based systems engineering at **Lawrence Berkeley National Laboratory (LBNL)** and one of the primary architects behind the widely used **Modelica Buildings Library** whose development has been funded by the US Department of Energy, the California Energy Commission and the US Department of Defense.

Modelon sat down with Dr. Wetter to discuss how modern simulation is reshaping data center innovation, the emerging technologies creating new engineering challenges, and what the industry needs to accelerate growth.

##### Why Simulation Matters Now More Than Ever

According to Dr. Wetter, modeling and simulation have become indispensable tools for data center owners, designers, and operators. With soaring electricity and water consumption and increasing pressure for operational reliability, simulation offers something the physical world can’t: **a safe, repeatable, environment to validate performance before systems are built or failures occur**.

“Using tools like the Modelica Buildings Library, teams can model a cooling plant or energy system and de-risk design decisions before equipment is installed, and once the system is operational, they can use the model as a digital twin to make adjustments to optimize operations and adapt proactively to extreme events,” explains Wetter.

Heat waves, for example, sharply increase cooling power requirements. With simulation, operators can forecast the impact on electricity usage or water consumption and adjust their operating strategy *before* the event occurs improving reliability and reducing unnecessary cost or risk.

##### De-Risking Design and Improving Reliability

Uptime is the defining KPI for any data center. Even a brief cooling failure can force operators to throttle or shut down IT equipment which is an extremely expensive and reputation-damaging outcome.

Dr. Wetter notes that simulation helps avoid these outcomes by making it possible to test control sequences, emergency responses, and new operating strategies offline. “You cannot test failure modes or extreme conditions on a live data center. The cost is too high. But you *can* test them in simulation,” Wetter explains.

By modeling the mechanical systems and the control logic together, teams can evaluate:

- Responses to power loss or grid curtailment
- The effect of IT load spikes on the energy system
- Extreme weather events
- Equipment staging and sequencing
- Grid-interaction strategies such as demand flexibility

The [Modelica Buildings Library](https://simulationresearch.lbl.gov/modelica/index.html), which LBNL maintains and **Modelon **contributes to and supports, provides the physics-based models that underpin these analyses.

“It’s time for the 21st century design engineer to update their digital toolbox. Today’s engineer needs to de-risk complex mechanical systems in the *design phase*. The Modelica Buildings Library offers validated physics-based components, advanced controls, and solutions that scale to district energy systems. When coupled with Modelon’s cloud platform, they make a great foundation for exploring next generation systems with confidence.”
 

#### Victor Braciszewski

Sr. Mechanical Engineer, SmithGroup

##### Emerging Data Center Technologies Bring New Modeling Challenges

The industry consensus is clear: the next decade of data center development will be defined by the transition to **liquid cooling** and **energy system integration**.

“Liquid cooling is relatively new and comes in many forms: cold plates, immersion cooling, hybrid systems. Their dynamic behavior and integration with the mechanical plant are not yet well understood and offer new opportunities for efficient provision of cooling. This is where modeling can significantly reduce cost and improve reliability,” says Wetter.

Simulation enables faster adoption of these advanced cooling solutions without over-engineering or sacrificing uptime. The Buildings Library provides a robust foundation for representing component physics, with the flexibility to tailor and extend models for specific cooling technologies. For advanced chip-level heat transfer, Modelica can even be co-simulated with finite-element tools using FMI, giving engineers a truly multi-scale view.

##### A Shift Toward Holistic, Integrated System Design

One of the biggest challenges Dr. Wetter sees is cultural, not technical: data centers are still designed largely through **siloed engineering workflows**. Mechanical engineers, electrical engineers, controls teams, and IT system designers often optimize their subsystems independently, leaving opportunities for improved integration unexplored.

He argues that the answer lies in adopting methodologies already proven in automotive and semiconductor industries. “Platform-based design and model-based systems engineering allow teams to integrate verticals—from chip cooling to central plant design—into a single performance-driven workflow.”

This shift would enable:

- Earlier and more informed design decisions
- Rapid iteration using virtual prototypes
- Clear specification of component requirements
- Continuous verification from design to deployment
- More cost-effective, scalable energy systems

Modelon supports this transformation with expert engineering guidance, specialized software for energy and thermal systems, proven libraries developed with partners, and cloud-based collaboration and workflows.

##### Lowering Barriers to the Adoption of Simulation

During the conversation, Dr. Wetter identified two major hurdles preventing broader industry uptake of simulation:

1. **A shortage of trained modeling professionals**
2. **Limited access to scalable workflows and domain support**

He sees a major opportunity for companies like Modelon to support capability-building in the market. “Modelon has an important role to play in training the industry and helping companies get up to speed. That’s essential for scaling the use of these technologies.”

As the market accelerates and data centers become increasingly mission-critical infrastructure, the need for trained modelers and accessible modeling workflows will grow rapidly.

##### Looking Ahead: Data Centers as Model-Driven Systems

When asked what excites him most about the next 5–10 years, Dr. Wetter envisions a future where models become central to how data centers are designed, built, and operated.  “We hope to see models become formal specifications of the system and used from concept design through deployment and continuously updated during operation. This would transform reliability, scalability, and cost.”

That vision aligns directly with Modelon’s commitment to open standards like Modelica and FMI, and to enabling simulation-driven engineering practices across the data center ecosystem.

##### Creating Awareness in an Industry Ready for Change

Perhaps the most striking point in the conversation was this: despite the enormous value simulation can deliver, **awareness in the data center industry remains low**.

At major data center events, Wetter has found that very few professionals are aware of the power and availability of advanced simulation. “There is a huge opportunity to bring these tools to the forefront. The industry needs them, it just doesn’t know it yet.”

Through collaboration with researchers like LBNL and support from industry partners, awareness and accessibility are rapidly growing and Modelon is leading that charge.

##### Partnering to Engineer What’s Next

Data centers power the digital world. Ensuring their performance and reliability in an era of rapid growth requires a new...

---

### From Intent to Insight: AI-Driven, Physics-Based Modeling with Modelica
- **URL:** https://modelon.com/blog/from-intent-to-insight-ai-driven-physics-based-modeling-with-modelica/
- **Description:** Explore how AI and Modelica enable faster, physics-based system simulation. Learn how engineers move from intent to insight using open, equation-based modeling and validated libraries for real-world applications.
- **Image:** https://modelon.com/wp-content/uploads/2026/03/AI-Driven-Blog-Screen-Shot_03_26v2.png
- **Modified:** 2026-04-27

Artificial intelligence is changing engineering workflows at a remarkable pace. But in the race to adopt AI, one question matters more than most:

**What kinds of engineering tools are actually ready for this new era?**

The answer may have less to do with flashy interfaces and more to do with something foundational: open, structured, machine-readable, physics-based modeling languages.

That is where Modelica stands out.

##### Why Modelica Is Built for the AI Era

There are several reasons Modelica works especially well with modern AI systems. **First, Modelica is equation based.** Today’s reasoning-focused language models are becoming increasingly capable at handling mathematics, symbolic structure, and constraint-based logic. A modeling language built around equations, physical relationships, and modular composition aligns naturally with how these models reason.

**Second, Modelica is both human friendly and machine friendly.** Engineers can read and understand physical intent directly in the code, while AI systems can parse structure, relationships, and constraints. This creates a direct bridge between domain expertise and automation.

**Third, the ecosystem matters.** A clear language specification, shared libraries, technical examples, open publications, and decades of adoption make Modelica unusually learnable for AI systems. This foundation allows AI to operate on Modelica models in ways that are not possible with black-box or GUI-centric tools.

It’s worth emphasizing that the strengths described above didn’t appear by accident. The [Modelica](https://www.modelica.org)community has spent decades refining a language where physical intent is expressed with clarity and transparency—and this is precisely what makes today’s AI systems able to operate on it so effectively. This foundation allows AI to generate meaningful models even from a blank slate, which we’ll explore next.

##### Early Signals: When AI Met Equation-Based Modeling

In 2023, before today’s reasoning models and long-context systems became widely available, we ran an experiment while preparing a pre-sales demonstration. We prompted GPT-4 to generate Modelica code for a diffusive-reactive fluid system. Within minutes, it produced syntactically correct code with coherent structure, documentation, and test examples.

The result was not perfect. One clear issue was that time derivatives were approximated using finite differences rather than the der() operator. This limitation was easy to identify and correct through a follow-up prompt.

The experience showed that equation-based modeling provides a strong substrate for AI assistance. Instead of spending time on boilerplate coding, engineers can focus on shaping questions, interpreting results, and solving the actual problem.

##### Fast-Forward to Today: AI Turns Engineering Intent into Models

Today’s AI systems offer longer context windows, stronger mathematical reasoning, improved code generation, and more robust debugging behavior. We explored what a general-purpose AI assistant could do in Modelica with almost no special setup.

Using simple conversational prompts such as ‘define the model structure’, ‘create the components’, ‘run the simulation’, and ‘update the equations’, we guided the AI to build a small Modelica package for analyzing thermal behavior in a data-center-like system.

This workflow represents what we refer to as *vibe coding* in Modelica: a fast, creative way to explore model structures directly from natural language. The AI relied only on first principles and publicly available libraries.

The result was not production-grade. Important effects such as detailed hydraulics and validated component maps were missing. However, the models were physically reasonable and useful for early-stage analysis, achieved in roughly half a day starting from an empty workspace.

![](https://modelon.com/wp-content/uploads/2026/03/Figure1_VibeCoding.png)

![](https://modelon.com/wp-content/uploads/2026/03/Results-in-AI.jpg)

*Figures 1-2: *Microsoft 365 Copilot selects and interprets the top system designs based on simulation results from Modelon Impact**

##### What Changes When Validated Libraries Are Present

Exploratory models demonstrate speed, but they also expose a boundary. Plausible is not the same as trustworthy. To understand what changes when that boundary is crossed, we ran a second experiment using validated vehicle dynamics models.

Starting from an empty workspace, we used AI assistance to set up a complete vehicle dynamics assessment based on an existing validated vehicle dynamics library. Instead of inventing components, the AI assembled and configured trusted model structures.

Within a single AI-supported session, we generated handling diagrams, transient response analyses, sensitivity studies, optimization results, and a structured engineering report summarizing the findings.

This workflow produced decision-grade results. The AI did not guess the physics. It accelerated configuration, analysis, and interpretation of models whose behavior was already trusted.

![AI-Driven Physics Based Modeling - Modelon](https://modelon.com/wp-content/uploads/2026/03/Figure2_MI_CodeStudio_03_26.png)

*Figure 3: Screen shot of Modelon Impact Code Studio, the AI study creation with the resulting report (right) and the AI generated models (left)*

##### From Exploration to Engineering Acceleration

Together, these examples show the progression clearly. AI combined with Modelica enables rapid movement from intent to executable models, even when starting from nothing. Real engineering acceleration occurs when AI operates on top of validated libraries.

In practice, this requires an environment that combines equation-based modeling, programmable workflows, validated libraries, and integrated AI assistance. Product naming is intentionally kept to figures. The value lies in the physics-based modeling platform, not the language model itself.

##### What is your moonshot?

If AI-assisted, physics-based modeling is already possible today, the question is what problems become feasible when engineers can move from intent to insight in hours rather than weeks. Modelica did not become AI-ready by accident. It became AI-ready by design.

---

### Off the Grid: Why F1’s Thermal Limits Demand Simulation Flexibility
- **URL:** https://modelon.com/blog/off-the-grid-why-f1s-thermal-limits-demand-simulation-flexibility/
- **Description:** See how flexible system simulation helps F1 engineers tackle 2026 thermal challenges, optimize heat exchangers, and predict cooling performance.
- **Image:** https://modelon.com/wp-content/uploads/2026/02/Blog-Featured-Image-F1.png
- **Modified:** 2026-03-12

In Formula 1, heat is the enemy of speed. As we approach the 2026 regulation changes, the most significant shift in powertrain architecture in a generation, the battle against that enemy is moving from the radiator shop to the simulation environment.

For thermal engineers, F1 has always been a “proving ground”. But as we push the limits of power density, we find that the biggest bottleneck isn’t just the physics of heat transfer but it’s the flexibility of the tools we use to model it.

##### Today’s Thermal Cliff

The upcoming 2026 regulations mandate a nearly 50/50 power split between the Internal Combustion Engine (ICE) and the Energy Recovery System (ERS). While total power remains high, the electrical contribution surges from roughly **120 kW to 350 kW**, putting immense thermal demand on battery and Motor Generator Unit (MGUK) systems. At the same time, ICE output drops from 550 kW to around 400 kW, shifting the heat-rejection balance dramatically toward electrical component systems.

Unlike an engine, which sheds a significant portion of heat through exhaust, electrical components require tightly controlled liquid cooling within narrow temperature windows.

Complicating matters further, F1’s new **Active Aerodynamics**—switching between high‑downforce “Z‑mode” and low‑drag “X‑mode”—means the mass flow rate through cooling ducts becomes a fast‑changing, transient variable rather than a smooth curve. Small front‑wing flap angle adjustments redirect flow into or away from sidepod inlets, making cooling performance highly mode‑dependent.

![](https://modelon.com/wp-content/uploads/2026/02/Figure-1_-Z-Mode.png)

*Figure 1 – Z mode showing high downforce at front and rear*

![](https://modelon.com/wp-content/uploads/2026/02/Figure2_X-Mode.png)

*Figure 2 – X mode showing less drag*

##### The Problem with “Off-the-Shelf” Modeling

Traditional 1D simulation tools rely on standardized assumptions and rectangular radiator cores. But in modern F1 packaging, nothing is standard. Sidepod geometry is optimized millimeter‑by‑millimeter around aerodynamic performance, and the cooling system must adapt to the available shape and not the other way around.

When performance margins are razor‑thin, “black‑box” heat exchanger models limit what engineers can explore. If a radiator needs to be curved, tapered, or non‑rectangular to maximize the “Coke bottle” narrowing of the rear bodywork, the simulation tool must allow engineers to define this bespoke geometry from first principles.

##### Maximizing “Coke Bottle” Volume: Non-Rectangular Heat Exchangers

At Modelon, we’ve worked with high‑performance teams to close the “geometry gap.” Modern F1 engineers face a packaging puzzle: fitting required cooling capacity into a shrinking, tightly sculpted aerodynamic envelope. Using the Modelon** [Heat Exchanger Library](https://modelon.com/library/heat-exchanger-library/)**, engineers can move beyond rectangular blocks and model the real physical geometry:

- **Bespoke discretization:** The heat exchanger is discretized to match physical taper or curvature.
- **Volumetric efficiency:** Triangular or trapezoidal stacks utilize narrow regions of the car’s packaging that would otherwise become unused “dead space.”

![](https://modelon.com/wp-content/uploads/2026/02/Figure-3.png)

*Figure 3 – Visualization of a non-rectangular radiator core*

This tightly aligns with ongoing trends in F1 where teams use **advanced manufacturing and 3D‑printed metal cores** to achieve compact, aerodynamically favorable exchanger shapes.

##### Closing the Gap: CFD to 1D Mapping

Air hitting the heat exchanger is never uniform. Suspension, wheel wake, and inlet flicks distort it. In Z‑mode, this distortion intensifies, starving the core, while X‑mode delivers cleaner flow. Beyond uneven airflow, stacked heat exchangers also suffer from pre‑heated inlet air, further reducing cooling efficiency. Relying on average mass‑flow risks oversizing and unnecessary weight.

To account for these flow irregularities, [Modelon Impact](https://modelon.com/modelon-impact/) enables **inhomogeneous boundary conditions** that directly bring detailing from CFD. Using a stream tube approach, engineers map a 2D grid of air velocity and temperature from CFD into the 1D model. The heat exchanger therefore “sees” the true face loading, including starved and overenergized regions.  

![](https://modelon.com/wp-content/uploads/2026/02/Figure-4_Heat-Exchanger.png)

*Figure 4 Visualization of heat exchanger pipes that is run with CFD inputs*

This mirrors the approach used by F1 teams today, where combined CFD + 1D thermo‑fluid simulations feed directly into lap‑simulation tools.

##### Beyond Radiators: Intercoolers and Turbo Heat Rejection

So far, the focus has been on radiators and ERS cooling but intercoolers represent a critical parallel heat rejection challenge. With turbocharging pressures exceeding 4.7 bar absolute, air entering the intercooler can exceed 200°C before cooling—demanding compact, highly efficient liquid to air cores. Advanced placement strategies, like low mount intercoolers used by several modern teams, influence center of gravity, ducting complexity, and thermal interactions between systems. Supporting all heat exchangers—not just radiators—ensures fidelity across the entire cooling pack.

##### Track-Driven Cooling Adjustments

Cooling needs shift dramatically from track to track:

- Hot races demand larger outlet areas or more open louver configurations.
- High altitude races reduce air density, decreasing cooling effectiveness.
- Teams adjust water, oil, ERS, and intercooler loops based on weather and circuit profile.

Your simulation environment should allow engineers to assess:

- Louver sizing and opening strategies
- Cooling performance across ambient temperatures and pressures
- Duct exit area variations
- Transient heat‑soak behavior during Safety Cars and traffic

This reflects how F1 teams today design **modular cooling packages** tailored to individual circuits.

##### System-Level Thinking

F1 thermal management is not about individual components—it’s about their interaction. Battery, inverter, intercooler, oil coolers, and combustion systems all interact thermally in transient conditions.

Because Modelon Impact is built on the Modelica open standard, engineers can integrate a high‑fidelity cooling pack into a larger powertrain or ERS model within minutes. This enables:

**Transient Lap Simulation**

Predicting battery derating or inverter temperature spikes at the end of a long straight when the cooling system temporarily loses effectiveness—especially with a 350 kW electrical system.

![](https://modelon.com/wp-content/uploads/2026/02/Figure-5.png)

*Figure 5 – Integrated System-Level Thermal Model*

**Failure-Mode Prediction**

System models can reveal risks such as:

- Coolant boiling margins
- Cavitation in narrow channels
- Over‑temperature excursions in intermittent flow regions

These are real concerns for teams using virtual calibration workflows.

**The Lap Time Trade‑off**

Simulations can quantify whether a slightly heavier, bespoke heat exchanger offers net lap‑time gains. For example, 1 kg of weight costing a few hundredths of a second but enabling a higher power deployment strategy.

##### Turn Thermal Complexity into Competitive Advantage

You may not be designing for the Monaco Grand Prix, but the physics of the “thermal ceiling” apply everywhere. Tight packaging, transient loads, track‑specific variability, and multi‑domain coupling are challenges shared by:

- [Data center cooling](https://modelon.com/industries/data-centers/) systems
- High‑density battery electric trucks
- Aerospace systems
- Cooling distribution units (CDUs)

When teams push the limits of feasibility, their tools cannot be the limitation. In high‑performance engineering, **simulation flexibility is a competitive advantage**. [See how Modelon Impact supports high-performance thermal...

---

### Modelon Impact Updates: Achieve More with Faster Speeds and Fewer Clicks
- **URL:** https://modelon.com/blog/modelon-impact-updates_2025-2/
- **Description:** Modelon Impact 2025.2 boosts simulation speed and usability with enhanced experiment workflows, live plotting, modernized tools, and streamlined modeling.
- **Image:** https://modelon.com/wp-content/uploads/2025/12/Featured-Image-2.png
- **Modified:** 2026-03-10

The latest release of Modelon Impact (2025.2) delivers powerful new capabilities designed to accelerate multi-case simulation workflows and simplify model setup and analysis. Whether you’re comparing design alternatives or optimizing configurations, this release helps you achieve more with fewer clicks and deeper insights.

In addition to performance enhancements, you’ll see improvements in usability and the overall modeling experience. Key updates include a streamlined process for creating your first model, a modernized Modelica code editor, enriched parameter dialogs, and a redesigned diagnostics interface for simulation logs.

##### Modelon Impact Release Highlights

New features include:

- **Experiment View:** With a more intuitive UI and better post-processing tools, you can easily set up and run complex simulation studies. Watch how it works in this [**video**](https://modelon.com/blog/experiment-setup-in-modelon-impact/)**.**
- **Live Plotting:** This feature lets you monitor simulation results in real time before simulation completion.
- **Redesigned Diagram-Layer Plotting:** Users now have a more modern and dedicated view for analyzing results and more powerful and intuitive plotting capabilities including new plot types, a synchronized zoom across plots, and a new way to select and compare results.
- **Code Edit Enhancements:** For power users, we’ve upgraded the editor’s core technology with command palette, minimap, improved search, and [VS Code integration](https://modelon.com/blog/introducing-modelon-impact-code-studio/).
- **Library Usability and Performance Improvements:** Experience a streamlined process for creating your first model, richer parameter dialogs, and better simulation logs.

For a complete list of updated features and functionality, read the [**Release Notes in Modelon Help Center**](https://help.modelon.com/latest/release_notes/latest/).

---

### Introducing Modelon Impact Code Studio
- **URL:** https://modelon.com/blog/introducing-modelon-impact-code-studio/
- **Description:** Modelon Impact Code Studio, delivers a modern VS Code–based developer experience with AI-assisted modeling, real-time error detection, and cloud-native integration.
- **Image:** https://modelon.com/wp-content/uploads/2026/01/Modelon-Impact-Code-Studio-Featured-Image.png
- **Modified:** 2026-03-10

Modeling teams are moving faster than ever and your tools should keep up. Today, we’re introducing **Modelon Impact Code Studio**, a modern coding experience built for Modelica developers who want speed, clarity, and control. 

##### What is Modelon Impact Code Studio?

The VS Code–powered development environment was released late last year and includes integrated Modelica language support through a pre-installed Modelica Language Server Protocol extension. This provides a modern and productive editing experience with intelligent language features.

Code Studio offers a professional, cloud-native IDE experience for teams building Modelica libraries and models. The built-in Modelon Impact editor remains ideal for smaller or quick edits.

- **Real-time diagnostics:** Errors and warnings from the compiler and language server displayed directly in the editor
- **Go to Definition:** Jump to class, component, or symbol definitions
- **Autocompletion:** Context-aware suggestions for Modelica constructs
- **Seamless integration:** Directly open the active model and workspace from the Modelon Impact UI
- **Structured file overview:** Browse classes, models, functions, and symbols via the Outline view and breadcrumb navigation. 

What else is new in **Modelon Impact**? Learn more in the [latest release post](https://modelon.com/blog/modelon-impact-updates_2025-2/).

---

### Engineering Innovation at F1 Speed: Modelon’s Lights Out Sprint Hackathon
- **URL:** https://modelon.com/blog/engineering-innovation-at-f1-speed-modelons-lights-out-sprint-hackathon/
- **Description:** Inside Modelon’s Lights-Out Sprint Hackathon, where cross-functional teams delivered rapid engineering innovations in just 120 minutes.
- **Image:** https://modelon.com/wp-content/uploads/2026/02/Feature_Image_F1Hackathon_Blog.png
- **Modified:** 2026-02-19

At Modelon, innovation doesn’t just happen in long cycles—it thrives in short, intense bursts of creativity. Last week, our India office turned an ordinary afternoon into a high energy, 120-minute *Lights Out Sprint Hackathon*, inspired by the precision and speed of an **F1 pit stop**.

Three cross-functional teams spanning product development, quality engineering, customer success, and physics stepped onto the starting grid with one goal: **build something meaningful, fast.**

No prework. No blockers. No hierarchy.
Just collaboration, ingenuity, and the mindset: **“Let’s find a way.”**

**Simulation** bridges gaps, enabling engineers to explore system behavior virtually, optimize designs early and more cost-effectively, and make data-driven decisions with confidence.

##### **⏱ **The Race Format

- **5 Minutes:** The challenge drops.
- **95 Minutes:** Teams build at full throttle.
- **20 Minutes:** Demos on the podium. Clear, sharp, impactful.

The spirit of the event was simple:
**Think boldly, move quickly, and prove what’s possible when engineering meets coordinated speed.**

![](https://modelon.com/wp-content/uploads/2026/02/Team_India-1-1024x560.jpg)

##### **🚀 **The Results: Three Teams, Three Breakthroughs

- **Team 1 – Designing Simplicity with Purpose:** Team 1 tackled a real-world engineering UX challenge and delivered a **clean, intuitive prototype** that streamlined a complex workflow into a smooth, elegant experience. In just over an hour, they demonstrated how thoughtful design can make sophisticated engineering more accessible and more delightful.
- **Team 2 – Pushing Boundaries in Exploratory Testing:** Team 2 applied modern testing techniques to a demanding feature, uncovering **fresh behavioral insights and performance patterns**. Their work showcased how rapid exploration, creative tooling, and engineering instinct can reveal opportunities for refinement that traditional test cycles may miss.
- **Team 3 – Engineering at Speed – The Modelica Mythbuster:** Team 3 set out to show that high-fidelity modeling can be fast—*very fast*. They built a functioning valve model directly from a datasheet, validated it, and extracted engineering insights all **within the race window**. This is a brilliant demonstration of Modelon’s mission: to make advanced system simulation faster, smarter, and more approachable.  

##### 🔥 What This Event Shows About Modelon

The Sprint Hackathon wasn’t just fun, it highlighted something essential about Modelon’s culture:

- **We move fast when it matters.**
- **We collaborate across teams effortlessly.**
- **We prototype, test, and iterate with intention**.
- **We are engineered for customer success.**

The same agility that powered this event is what we bring to our customers, whether helping them accelerate modeling workflows, simplify deployment, or uncover engineering insights with speed and confidence.

##### ****🏁****The Takeaway** **

A 120-minute sprint. 
Three teams. 
Dozens of ideas. 
Tangible outcomes. 

And above all, a reminder that innovation is not only about tools. It’s about **mindset, teamwork, and the willingness to move with purpose**. See you at the next starting grid.

##### Your trusted Partner in Model-Based Engineering

For over two decades, **Modelon** has helped engineering organizations bring simulation into the center of their design and decision-making processes. The open, standards-based tools empower teams to design with confidence, collaborate effectively, and respond to change with agility.

Ready for your next race? See how Modelon supports the automotive industry. Explore our [automotive simulation solutions](https://modelon.com/industries/automotive-system-simulation-software/) and learn more about the [vehicle dynamics](https://modelon.com/library/vehicle-dynamics-library/) library.

---

### Designing Closed-Loop Control Systems with Modelica
- **URL:** https://modelon.com/blog/designing-closed-loop-control-systems-with-modelica/
- **Description:** A closed-loop control system automatically adjusts outputs based on feedback to maintain accuracy and stability. Learn how it differs from open-loop systems with examples, benefits, and use cases in automotive, aerospace, and energy applications.
- **Image:** https://modelon.com/wp-content/uploads/2025/03/Controls-and-Modelica-4.png
- **Modified:** 2026-01-26

##### What is a Closed-Loop Control System?

A closed-loop control system automatically adjusts outputs based on real-time feedback to maintain stability and accuracy. Unlike open-loop systems, closed-loop systems measure performance, compare it against a target, and correct deviations—making them more reliable and adaptable in dynamic environments.

This blog series explores the steps to implement a closed-loop control system using a Solid Oxide Fuel Cell (SOFC) system model as an example.

SOFC systems are a great example of complex controls due to the interplay of thermal, electrical, and chemical dynamics. Achieving optimal performance, efficiency, and durability requires precise control of variables such as fuel flow rates, temperature regulation, and power output. 

A closed-loop control strategy is essential for an SOFC system because it continuously adjusts key operating parameters—such as fuel flow and temperature—based on real-time feedback to ensure optimal efficiency and stability. This approach helps maintain safe operating conditions, improve system responsiveness to disturbances, and extend the lifespan of critical components. 

Systems risk inefficiencies, reduced lifespan, and unstable operation without a well-structured approach to control strategy. Leveraging Modelon Impact, engineers can develop high-fidelity models that accurately capture system behavior, enabling robust control strategies. A model-driven approach facilitates early validation, optimization, and seamless integration of control algorithms, ultimately leading to more reliable and efficient SOFC systems.

##### Solid Oxide Fuel Cell System Model Explained

The SOFC model is derived from the Fuel Cell Library example, FuelCell.Examples.SOFC.System.FullSystemSOFC, found in [Modelon Impact](https://modelon.com/modelon-impact/).

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_system-1024x643.png)

*SOFC system model representation in Modelon Impact*

The SOFC system consists of multiple interacting fluid streams that mix, react, and exchange heat. In a real-world setup, pressure, flow rates, composition, and temperature must be regulated. In our initial model, the fluid flow rates are perfectly controlled as boundary conditions, allowing analysis of fundamental system behavior and performance. 

However, real-world systems require active regulation, necessitating a more flexible model.  

Instead of creating separate models for each use case, we take advantage of [Modelica](https://modelica.org/)‘s object-oriented approach, which allows us to define reusable components. Specifically, we create a base model that captures the core system dynamics, much like a blueprint. In Modelon Impact, this base model acts as a foundation that engineers can extend and customize to develop and test different control strategies—without needing to rebuild the system from scratch. 

##### SOFC Pressure Boundary Control Approach

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_pressure_boundary.png)

*SOFC boundary control representation in Modelon Impact*

By extending the base class, we introduce a pressure regulation mechanism using valves. The actuator-controlled valve openings adjust fluid flow rates based on sensor feedback. This structured approach allows easy modification and extension of control strategies while maintaining consistency across system variations.

##### Implementing Closed-Loop Control System

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_controlled.png)

*SOFC controlled system modelon*

To actively regulate the system, we implement a closed-loop control strategy using standard PID controllers from the Modelica Standard Library. These controllers adjust valve openings based on real-time feedback, maintaining stable operation under varying conditions. The Modelon library also includes a PID autotuner to optimize controller gains efficiently. 
 

Comparing the controlled system’s performance against the idealized model (with perfectly controlled boundary conditions) helps evaluate controller effectiveness. The following visualization illustrates the inlet air temperature response:

##### Analyzing SOFC Stack Temperature in Modelon Impact

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_stack_temp.png)

*SOFC stack temperature graph*

The controlled system closely follows the expected thermal behavior, ensuring performance metrics are met. This validation step is crucial for assessing transient response characteristics, disturbance rejection, and overall control robustness.

##### Supervisory Control and Discrete Logic

Beyond continuous PID control, SOFC systems often require supervisory controllers for startup, shutdown, and fault handling. Modelon Impact supports hybrid modeling of continuous and discrete-time dynamics, enabling the implementation of state machines for mode management.

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_ignition_logic.png)

*SOFC ignition logic state machine*

This state machine coordinates system behavior during different operational phases, ensuring safe and efficient transitions. Such logic-driven control structures improve system reliability and adaptability.

##### Closed-Loop Control System with Model-Based Design

Using a model-based design approach, we can embed system models directly within controllers for feedforward control, state estimation, and model predictive control (MPC). For example, a feedforward control strategy leveraging model inversion can preemptively adjust system inputs for improved response time.

![](https://modelon.com/wp-content/uploads/2025/03/SOFC_ignition_result.png)

*SOFC ignition results showing feedforward control impact*

While feedforward control enhances responsiveness, it is not inherently robust to disturbances. Thus, real-world applications often prefer a hybrid approach combining feedforward and feedback mechanisms. 

##### Modelon Impact: A Critical Tool for Control Strategy

By leveraging Modelica’s flexibility and Modelon Impact’s simulation capabilities, engineers can develop highly accurate models, implement advanced control strategies, and streamline the transition from simulation to real-world deployment. Whether for SOFC systems or other complex engineering applications, [Modelon Impact](https://modelon.com/modelon-impact/) is an essential tool for modern control development.

**What is an example of a closed-loop control system?**

Common examples include a car’s cruise control system, which adjusts throttle based on speed feedback, and a thermostat, which regulates temperature using continuous measurements.

**How is a closed-loop system different from an open-loop system?**

Closed-loop systems use feedback to compare outputs against the desired goal and adjust automatically, while open-loop systems do not measure or correct performance

**Why are closed-loop systems important?**

They provide higher accuracy, stability, and adaptability in dynamic conditions, making them essential in applications like aerospace, automotive, robotics, and energy systems.

---

### Designing and developing advanced aircraft escape systems for fighter pilots
- **URL:** https://modelon.com/blog/designing-and-developing-advanced-aircraft-escape-systems-for-fighter-pilots/
- **Description:** Fighter pilots often face critical and life-threatening situations that require a military aircraft manufacturer to design and expertly craft escape systems -...
- **Image:** https://modelon.com/wp-content/uploads/2022/06/Designing-and-developing-advanced-aircraft-escape-systems-for-fighter-pilots.jpg
- **Modified:** 2026-01-26

***Fighter pilots often face critical and life-threatening situations that require a military aircraft manufacturer to design and expertly craft escape systems – ensuring safety opportunities exist in case of emergency evacuation. ***

In a previous project, Modelon worked with a military aircraft manufacturer on escape systems, specifically the pilot ejector seat, that would propel a pilot’s seat out of an aircraft using a rocket motor. Because the safety and survival of the pilot depends on this system efficiency, proper designing of it is essential. The ejection system must work reliably despite many disturbances such as: aircraft and seat aerodynamics (including their interactions), potential collisions due to continuous movement, uncertainty in body position and mass, and many more.

The new design process uses a model-based approach, delivering critical data needed for the design requirements, and when exchanging information with subcomponent suppliers (for example, on system timing), Additionally, the aircraft manufacturer wanted to shorten the simulation and model validation phase. A focus of the effort was the modeling of the ejector seat propulsion system such as capturing pressure wave propagation, temperature rise and thrust generated due to the combustion of the propellant. This knowledge is of immense importance when designing the systems against material failure and sizing the different system components.

Modelon created pyrotechnical models of the ejector systems using its [Pneumatics Library,](/library/pneumatics-library/) Modelon Base Library, and [custom-built models](/products-services/). The seat propulsion system includes four main components including:

1. **Cartridge**– Used as a starter motor manually ignited by the pilot. Models the combustion of the solid propellant producing heat and pressure used to ignite the engine.
2. **Hose**– This is the connection between the cartridge and the rocket. Models the pressure waves, temperature and enthalpy due to the rapid combustion in the cartridge.
3. **Engine**– Models the multi-body behavior of the engine as well as the combustion of the solid propellant. Through the nozzle, the combustion will generate thrust which ejects the seat.
4. **Nozzle**– Models the generated thrust from the rocket engine given the pressure difference between the chamber and ambient, and inlet and outlet as well as throat areas.

An example layout of the ejector system can be seen in figure 1 below.
![Example layout of the ejector system](https://modelon.com/wp-content/uploads/2022/06/Example-layout-of-the-ejector-system.jpg)
*Figure 1: Example layout of the ejector system*

Due to different model fidelity requirements and the unavailability of detailed data during early design stages, two variants of the models where created. The first version represents the behavior of the system without a detailed description of its physical implementation. The second version includes more sophisticated equations describing the detailed combustion and propagation physics.

By attaching the rocket to an external seat via the multi body connector a full ejection sequence can be modeled. The ignition time for the cartridge is set to 7.1 second. The pressure and temperature of the rocket engine chamber and cartridge can be seen in figure 2 and 3.
![Pressure in rocket and cartridge](https://modelon.com/wp-content/uploads/2022/06/Pressure-in-rocket-and-cartridge.jpg)
*Figure 2: Pressure in rocket and cartridge*

![Temperature in rocket and cartridge](https://modelon.com/wp-content/uploads/2022/06/Temperature-in-rocket-and-cartridge.jpg)
*Figure 3: Temperature in rocket and cartridge*

The results show that both the pressure and temperature are delayed due to the hose between the cartridge and rocket. At about 7.4 seconds, the temperature in the rocket has reached the ignition temperature of the propellent. This results in a drastic increase of temperature as seen in figure 3. Due to the combustion in the rocket, thrust is generated and the seat is launched. Figure 4 show the vertical acceleration of the seat in g’s due to the generated thrust.

![Vertical acceleration of seat](https://modelon.com/wp-content/uploads/2022/06/Vertical-acceleration-of-seat.jpg)
*Figure 4: Vertical acceleration of seat*

The peak acceleration is around 11g and the propellent is burned out in about 0.5 seconds. In this example, two phases of the ejection are modeled. The first part is the launch phase where the seat accelerates to its peak acceleration. Then a short period of deceleration before the sustainer phase starts at approximately 7.5 seconds.

Below you can see a video visualizing the vertical launch of the seat from an aircraft.This video was generated via the Visualization Library developed by DLR, the German Aerospace Centre ([http://www.systemcontrolinnovationlab.de/methoden-tools-2/modellbibliotheken/](http://www.systemcontrolinnovationlab.de/methoden-tools-2/modellbibliotheken/)).

 

The aircraft manufacturer has deployed the design process and leveraged the results in a new aircraft program. The aircraft manufacturer now has higher confidence in the reliability of its methods and models, and uses model-based design with its suppliers. Shortened modeling and validation cycles give it a competitive advantage.

###### For more information on Modelon’s aerospace capabilities [click here.](https://modelon.com/industries/aerospace-systems-modeling-and-simulation-software/)

---

### Propagating Replaceable Medium Automatically
- **URL:** https://modelon.com/blog/propagating-replaceable-medium-automatically/
- **Description:** Many Dymola users who integrate Modelon libraries may currently struggle with selecting the medium in each component. This is forcing users to either do it...
- **Image:** https://modelon.com/wp-content/uploads/2022/07/Modelon-Tips_1280_600.jpg
- **Modified:** 2026-01-26

Many Dymola users who integrate Modelon libraries may currently struggle with selecting the medium in each component. This is forcing users to either do it manually on each component or propagate the medium at a top level for each of them. This manual update is a tedious process, error-prone and not conducive to working efficiently or effectively.

With the release of Dymola 2019 FD01, a new flag has been added: Advanced.MediaPropagation. This flag can be set to:

- 0 – Dymola will behave as described previously
- 1 –  The media will be automatically propagated at a top-level with pop-up windows informing the modeler of viable component combination options
- 2 – The media will be automatically propagated at a top-level with fewer pop-up windows

With this feature activated, Dymola detects when two components including a medium are connected and automatically offer the modeler to propagate the medium at a top level. When connecting an additional component to these models, the propagation will be suggested (if Advanced.MediaPropagation = 1) or performed (if Advanced.MediaPropagation = 2).

The MediaPropagation flag is a great improvement that simplifies the workflow of modelers using medium packages in their model. Set Advanced.MediaPropagation=1 or 2 and enjoy a smooth usage of our libraries in Dymola!

Now, the advantages of the medium package are combined with effective user experience and therefore, many Modelon libraries– such as [Liquid Cooling Library](https://modelon.com/library/liquid-cooling-library/), [Jet Propulsion Library](https://modelon.com/library/jet-propulsion-library/), [Fuel System Library](https://modelon.com/library/fuel-system-library/), etc. – are even nicer to use!

![](https://modelon.com/wp-content/uploads/2022/07/flag-picture.png)

If you like this flag you can turn it on automatically while opening Dymola by adding it to the setup.dymx file in your local AppData\Roaming\DassaultSystemes\Dymola\ folder. Once opened, add the following line in the type of flag.

Note: the propagation is not final and could be “broken” if in a component the user overwrites the propagation modifier.

## Pages

### Data Centers
- **URL:** https://modelon.com/industries/data-centers/
- **Description:** Maximize performance, reliability, and ROI of data center cooling systems from concept to commissioning.
- **Modified:** 2026-05-15

### Data Center Liquid Cooling Simulation

Design high-performance, resilient data center cooling systems with confidence. Modelon Impact enables engineering teams to virtually test, validate, and optimize liquid and hybrid cooling architectures from concept through commissioning—reducing development time, minimizing operational risk, and improving energy efficiency at scale.

Modelon Impact supports advanced cooling architectures such as **pumped two-phase loops** and **refrigerant-based heat rejection**, enabling engineers to evaluate phase-change dynamics, heat exchanger performance, and control strategies within a full system model.

[Request a Demo](https://modelon.com/talk-to-an-expert/)

[See Applications](#applications)

![Data Center](https://modelon.com/wp-content/uploads/2026/02/data-center.png)

![Data Center Liquid Cooling Simulation - Modelon](https://modelon.com/wp-content/uploads/2026/02/Data-Center-Web-Graphic.svg)

#### Built for Engineering Teams and Complex Cooling Systems

With **[Modelon Impact](https://modelon.com/modelon-impact/)** and its validated libraries, simulation engineers, thermal and controls engineers, and data center designers responsible for high-density and AI infrastructure can:

- **Accelerate design decisions:** assess cooling capacity vs. ambient + IT loads, identify limiting components, and compare loop sizing and equipment options.
- **Optimize efficiency (PUE/WUE):** test staging and control strategies (setpoints, pump/valve behavior, heat-exchanger performance, air–liquid split) and quantify tradeoffs.
- **Run transient what-if studies:** understand response to workload spikes and weather variation; quantify temperature excursion range/duration.
- **Support resilient operations:** explore degraded-performance scenarios and control sensitivity to reduce risk of overheating and throttling.
- **Improve reporting readiness:** connect engineering results to measurable outcomes (energy/water use, PUE/WUE trends) for internal targets and stakeholder communication.
- **Design for compliance and operational readiness: **align with ASHRAE thermal guidelines and deliver traceable, explainable simulation results engineers and stakeholders can trust.

#### Proven Impact of Virtual Design Optimization

###### 20-50**%**

Reduction in Development Time

###### 25**%**

Reduction in Energy Use

###### **$**1M

**in Risk Avoidance**

###### 300**x**

**More Water Efficiency**

#### Shorten development cycles, reduce risk, and improve energy efficiency across the entire cooling ecosystem.

As data centers scale in power density and complexity, liquid cooling becomes a necessity. But managing it efficiently poses new operational risks.** Modelon Impact** delivers a physics-based system simulation platform purpose-built to optimize liquid cooled infrastructures.

A unified, cloud-based multi-physics software platform for 1D system simulation, optimization, and control, **[Modelon Impact](https://modelon.com/modelon-impact/)** provides:

- Multi-case studies and design-of-experiments via **Experiment View**
- **Live plotting** for real-time monitoring of simulation outputs
- Intuitive **drag-and-drop modeling** canvas that help visualize system layout
- Access to model source code with a modern, **AI-enabled** text editor for
  maximum customization flexibility
- Integrations for customized, automated workflows 
- **Link sharing **for quick and easy model deployment 
- Model export compliant with the **FMI (Functional Mock-up Interface) **standard for integration with control design and digital twin platforms

[**Request a Quick Tour of Modelon Impact**](/book-a-demo/)

#### Why Engineering Teams Choose Modelon for Liquid Cooling

**Validated libraries. Open standards. Rapid modeling. System-level insight.**

##### Open-Standard Simulation Framework

Use clear, physics-based models that grow with your engineering needs. Collaborate with data center owners, cooling solution providers, and equipment vendors while keeping your critical IP protected.

##### Cloud-Native Simulation Platform

Run large design studies, multi-case simulations, and optimization workflows directly in the browser. Collaborate across teams and easily scale computing resources.

##### Industry-Proven Component Libraries

Use Modelon’s [Liquid Cooling Library](https://modelon.com/library/liquid-cooling-library/), [Energy System Library](https://modelon.com/library/energy-systems-library/), [Vapor Cycle Library](https://modelon.com/library/energy-systems-library/), [Heat Exchanger Library](https://modelon.com/library/heat-exchanger-library/), and the [Modelica Buildings Library](https://simulationresearch.lbl.gov/modelica/index.html) to model pumps, valves, heat exchangers, coils, piping networks, controls, and HVAC systems.

##### Comprehensive Multi-Physics Modeling

Seamlessly simulate electrical systems, thermal networks, hydraulic loops, and control strategies together for accurate system-level insight.

#### From Chip to Facility: What Engineers Can Solve with Modelon Impact

**End-to-end liquid cooling systems in a single, open-standard environment.**

Modelon provides validated, physics-based component models and complete system templates for designing, testing, and optimizing liquid cooling architectures.

- Model with Accuracy and Speed
- Commission with Confidence
- Operate with Insight
- Reduce Energy and Water Use
- R&D: Plan for Growth and Change

- Accelerate model development with AI-assisted tools
- Leverage high-accuracy, calibrated physics-based models
- Use supplier-certified components from a reusable model library cooling capacity scales with compute load, even as densities rise and cooling

- Simulate and validate cooling system behavior before go-live (flow balance, thermal margins, control stability)
- Identify design flaws, mismatches, or hidden risks during commissioning – not after deployment
- Accelerate time to readiness by enabling faster tuning of control

- Monitor, predict, and optimize cooling performance in real time using a high-fidelity, site-specific digital twin
- Run “what-if” simulations to evaluate the impact of workload changes, component faults, or retrofits without disrupting live systems
- Extend equipment life and reduce unplanned downtime through predictive diagnostics and scenario planning

- Optimize system-wide cooling setpoints, pump/fan speeds, and component utilization to minimize PUE
- Quantify efficiency gains from liquid cooling investments – and continuously improve them
- Lower operating costs while meeting sustainability goals

- Use real operational data to guide future expansion, retrofits, or vendor selection
- Ensure cooling capacity scales with compute load, even as densities rise and cooling technologies evolve (e.g., two-phase)

#### What customers are saying about Modelon

###### Modelon’s system‑level capabilities allowed us to understand two‑phase direct‑to‑chip cooling in data centers as an integrated system and explore realistic operating conditions with more confidence.

Lingnan Lin, Ph.D.

Department of Mechanical Engineering, University of Maryland

![](https://modelon.com/wp-content/uploads/2026/05/UMD-Logo.avif)

###### If we get stuck, it’s refreshing to be able to directly reach out to the developers of the technology to get the support and direction that we can trust. This level of support helps us make quicker decisions in our product development cycle.

Arne Knoblauch

Chief Technology Officer, 1W1 GmbH

![](https://modelon.com/wp-content/uploads/2025/02/1w1_gmbh-logo.png)

###### Working with Modelon not only enhances our confidence in the design’s overall performance, but also enables us to identify and address potential issues related to startup, stop, and trip events.

David Danesi

Project Manager & Technical Proposal Leader, Turboden

![](https://modelon.com/wp-content/uploads/2025/02/turboden-logo.png)

###### Modelon reviewed our requirement and developed a model...

---

### Company
- **URL:** https://modelon.com/company/
- **Description:** Modelon's simulation software helps organizations in automotive, aerospace, energy, and other spaces develop technical systems. Learn about us here.
- **Image:** https://modelon.com/wp-content/uploads/2023/02/Modelon_Social_Impact_Blog.jpg
- **Modified:** 2026-05-11



---

### Homepage (Updated 4/26)
- **URL:** https://modelon.com/
- **Description:** Modelon provides cloud-native system simulation software and services helping engineering teams design, simulate, and optimize complex industrial systems faster.
- **Modified:** 2026-04-28

**Accelerate industrial system design** with AI-enhanced, physics-based simulation.

Modelon Impact delivers intelligent system simulation on a cloud-based platform for faster project turnaround, lower costs, and greater collaboration.

[Talk to an Expert](/talk-to-an-expert/)

[See Data Center Solutions](/industries/data-centers/)

![](https://modelon.com/wp-content/uploads/2026/04/modelon-home-hero-graphic.avif)

###### What do we think about:

[AI](https://modelon.com/blog/category/ai/)

											[Agentic AI](https://modelon.com/blog/category/ai/agentic-ai/)

											[Data Centers](https://modelon.com/blog/category/data-centers/)

											[HVAC Simulation](https://modelon.com/blog/category/hvac-simulation/)

[See all posts →](https://modelon.com/news-blog/)

![Accelerating Simulation with Machine Learning](https://modelon.com/wp-content/uploads/2026/05/Blog-ML-Featured-Image-1202x-626-768x400.avif)

Agentic AI

##### [Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/)

![Direct-to-Chip Cooling Tipping Point](https://modelon.com/wp-content/uploads/2026/05/Data-Center-1200x625-2-768x400.avif)

AI

##### [Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/)

![Accelerating Simulation with Machine Learning](https://modelon.com/wp-content/uploads/2026/04/Blog-Featured-Image-DCW_04_26-768x400.avif)

Agentic AI

##### [AI Liquid Cooling at System Scale](https://modelon.com/blog/ai-liquid-cooling-at-system-scale/)

![Modelon quotes Thomas Nilsson](https://modelon.com/wp-content/uploads/2026/04/Testimonial-1200x625-1-768x400.png)

Agentic AI

##### [Engineering AI that Supports Real Decisions](https://modelon.com/blog/engineering-ai-that-supports-real-decisions/)

![](https://modelon.com/wp-content/uploads/2026/04/AI-Assistant-Laptop-Video-768x400.gif)

Agentic AI

##### [AI Assisted Simulation Now in Modelon Impact](https://modelon.com/blog/new-ai-assistant-in-modelon-impact/)

![5 Questions to Ask Before Trusting AI-Generated Simulation](https://modelon.com/wp-content/uploads/2026/04/Blog-Featured-Image-Automotive-1202x-626-768x400.png)

AI

##### [5 Questions to Ask Before Trusting AI-Related Simulation Results](https://modelon.com/blog/5-questions-to-ask-before-trusting-ai-related-simulation-results/)

![From Intent to Action: Agentic AI for Vehicle Dynamics in Modelon Impact](https://modelon.com/wp-content/uploads/2026/03/Blog-Featured_Agentic_AI_VDL_1202x-626-768x400.png)

Agentic AI

##### [From Intent to Action: Agentic AI for Vehicle Dynamics in Modelon Impact](https://modelon.com/blog/from-intent-to-action-agentic-ai-for-vehicle-dynamics-in-modelon-impact/)

![](https://modelon.com/wp-content/uploads/2026/03/AI-Driven-Blog-Screen-Shot_03_26v2-768x400.png)

Agentic AI

##### [From Intent to Insight: AI-Driven, Physics-Based Modeling with Modelica](https://modelon.com/blog/from-intent-to-insight-ai-driven-physics-based-modeling-with-modelica/)

![Validating Liquid Cooling Against ASHRAE Guidelines - Modelon](https://modelon.com/wp-content/uploads/2026/02/ASHRAE_Blog-1200x625-1-768x400.png)

Blog

##### [Data Center Compliance: How to Validate Cold Plate Liquid Cooling Against ASHRAE Guidelines Virtually](https://modelon.com/blog/data-center-compliance-how-to-validate-cold-plate-liquid-cooling-against-ashrae-guidelines-virtually/)

![Digitally Engineering the Future of Data Centers - Modelon](https://modelon.com/wp-content/uploads/2026/01/Jan-Post-3-Blog-768x400.jpg)

Blog

##### [Digitally Engineering the Future of Data Centers](https://modelon.com/blog/digitally-engineering-the-future-of-data-centers/)

**Modernize your R&D processes** with a scalable, customizable, collaborative simulation platform.

##### Transform your system development

Validate design concepts prior to physical prototyping for faster turnaround time and reduced cost. Replace in-house systems that have become to difficult to maintain.

##### Upgrade to a modern, AI-enabled, cloud-based platform

Increase openness, accessibility, and collaboration with an agile, intelligent platform that can grow alongside your company.

##### Experiment with digital twins

Replicate components, products, or even whole building designs in your simulation library to compare existing models to proposed changes and test novel parts.

##### Rapidly build multi-domain models

Thermal, fluid, chemical, electrical, and mechanical domain models can be combined to build a single comprehensive system model.

##### Analyze system performance and efficiency

Multiple what-if scenarios can run in parallel to understand system performance and optimize parameters within real-world conditions.

Key Features

##### Content-rich, multi-domain libraries

Choose from 19 unique libraries, developed over decades by industry experts.

##### Modelica libraries and 3rd party library support

Import Modelica-based libraries or develop your own models.

##### Industry-leading Modelica compiler

Run parallel simulation in a fraction of the time other solutions require.

##### Analysis dashboard for data visualization

View simulation results in a graphical interface for faster comprehension.

##### Collaboration tools to support multiple users

Empower teams with shareable workspaces and version control.

##### Customizable user experience

Develop custom workflows that match your team’s working style.

##### Accessible code layer

Retain full control and customize your model’s code base. Collaborates with leading AI tools.

##### FMU import and export

Transfer models between compatible tools for further development and testing.

**Achieve real business outcomes** with system simulation.

2-3x

##### Faster time to result

Using Modelon Impact, Universal Hydrogen simulated their liquid hydrogen system 2 to 3 times faster than with in-house and other commercial simulation tools.

<5%

##### Variability in simulation results compared to testbench

MAN Energy Solutions successfully validated their heat pump system model with less than 5% variability in simulation results compared to test bench results.

50%

##### Shorter start up time

Siemens Energy improved combined cycle flexibility with faster start-up, achieved better economic performance through maximized power output, and extended asset lifetime by effectively managing temperature gradient constraints.

[View Case Studies](/support-learning/resources/case-studies/)

**Innovate** in your industry.

##### Aerospace

Incorporate cutting edge model components to simulate propulsion systems, thermal management, and fuel cells for hydrogen-powered and electric aircraft.

[See Aerospace](/industries/aerospace-systems-modeling-and-simulation-software/)

##### Automotive

Shorten R&D cycles and time-to-market by testing concepts on virtual twins of existing vehicle systems using component libraries for thermal management, cabin comfort, drivetrain, and fuel cells.

[See Automotive](/industries/automotive-system-simulation-software/)

##### Data Centers

Leverage libraries in two-phase, liquid cooling, and air cooling, for multi-domain models that simulate performance under a range of operating conditions: on the chip, facility, and plant level.

[See Data Centers](/industries/data-centers/)

##### Energy & Power

Explore solutions for power plant design, clean energy storage, and renewable energy sources on a physics-based system simulation platform with workflows for common energy applications.

[See Energy & Power](/industries/energy-power-system-simulation-optimization-software/)

##### HVAC & Refrigeration

Explore steady state and dynamic simulation for geothermal, air-to-air, and water-source refridgeration, heat pump, and air conditioning systems in commercial, residential, and industrial applications.

[See...

---

### Presentations
- **URL:** https://modelon.com/presentations/
- **Description:** INVESTOR PRESENTATION:




April 24, 2026 - Q1 Investor Presentation: Video recording in English



February 17, 2026 - Q4 Investor Presentation: Video...
- **Modified:** 2026-04-24

##### INVESTOR PRESENTATION:

- April 24, 2026 – Q1 Investor Presentation: [Video recording in English](https://modelon.com/april-24-2026-q1-investor-presentation/)
- February 17, 2026 – Q4 Investor Presentation: [Video recording in English](https://modelon.com/february-17-2026-q4-investor-presentation/)
- October 30, 2025 – Q3 Investor Presentation:  [Video recording in English](https://modelon.com/october-30-2025-q3-investor-presentation/)
- July 24, 2025 – Q2 Investor Presentation:  [Video recording in English](https://modelon.com/july-24-2025-q2-investor-presentation/)
- April 30, 2025 – Q1 Investor Presentation:  [Video recording in English](https://modelon.com/april-30-2025-q1-investor-presentation/)
- February 21, 2025 – Q4 Investor Presentation:  [Video recording in English](https://modelon.com/february-21-2025-q4-investor-presentation/)
- November 7, 2024 – Q3 Investor Presentation: [Video recording in English](https://modelon.com/november-7-2024-q3-investor-presentation/)
- August 21, 2024 – Q2 Investor Presentation: [Video recording in English](https://modelon.com/august-21-2024-q2-investor-presentation/)
- May 14, 2024 – Q1 Investor Presentation: [Video recording in English](https://modelon.com/may-14-2024-q1-investor-presentation/)
- February 29, 2024 – Q4 Investor Presentation: [Video recording in English](https://modelon.com/february-29-2024-q4-investor-presentation/)
- November 10, 2023 – Q3 Investor Presentation: [Video recording in English](https://modelon.com/november-10-2023-q3-investor-presentation/)
- August 23, 2023 – Q2 Investor Presentation: [Video recording in English](https://modelon.com/q2-2023-investor-presentation/)
- May 16, 2023 – Q1 Investor Presentation: [Webcast in English](https://www.youtube.com/live/VraDqAPG9TE?feature=share)
- March 3, 2023 – Q4 Investor Presentation: [Webcast in English](https://www.finwire.tv/webcast/modelon/q4-2022/)
- November 11, 2022 – Q3 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q3-report-2022)
- August 24, 2022 – Q2 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q2-report-2022/register)
- May 17, 2022 – Q1 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q1-report-2022/register)
- March 4, 2022 – Q4 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q4-report-2021/register)
- November 16, 2021 – Q3 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q3-report-2021/register)
- August 20, 2021 – Q2 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q2-report-2021/register)
- May 31, 2021 – Q1 Investor Presentation: [Audiocast in English](https://tv.streamfabriken.com/modelon-q1-report-2021/register)

##### COMPANY PRESENTATION:

- May 16, 2023 – Company presentation by CEO Magnus Gäfvert at Redeye SaaS Day: [Video recording in English](https://www.redeye.se/video/event-presentation/909163/modelon-ceo-magnus-gafvert-presents-at-redeye-saas-day-may-16-2023)
- April 6, 2022 – Company presentation by CEO Magnus Gäfvert at Redeye SaaS Seminar: [Video recording in English](https://www.redeye.se/video/event-presentation/838127/modelon-magnus-gafert-ceo-presents-at-redeye-saas-seminar-2022)
- April 29, 2021 – Company presentation by CEO Magnus Gäfvert together with CFO Jonas Eborn: [Audiocast in English](https://financialhearings.com/event/13850)

---

### April 24, 2026 – Q1 Investor Presentation
- **URL:** https://modelon.com/april-24-2026-q1-investor-presentation/
- **Description:** https://player.vimeo.com/video/1186177804
- **Modified:** 2026-04-24



---

### Investor Relations
- **URL:** https://modelon.com/investor-relations/
- **Modified:** 2026-04-24



---

### Modelon Impact
- **URL:** https://modelon.com/modelon-impact/
- **Description:** Modelon Impact is a next-generation, cloud-native, modeling and simulation platform, built on Modelica, that offers an easy-to-use, browser-based interface.
- **Modified:** 2026-04-21

### System modeling and simulation you can trust—from concept to validated design.

Meet Modelon Impact – an AI-enabled cloud platform helping engineers to rapidly design, simulate, and analyze complex thermofluid systems within a single collaborative workspace.

.

[Take a Quick Tour](https://modelon.com/modelon-impact-product-overview/)

[View Features](#features)

##### Build

Create comprehensive system models with readily available, drag and drop components.

##### Analyze

Conduct experiments and visualize results to assess system performance and efficiency.

##### Collaborate

Share models with peers and simulation results with stakeholders through a simple weblink.

##### Learn

Advance your design and simulation capabilities with online resources and one-on-one expert assistance.

![](https://modelon.com/wp-content/uploads/2025/02/MI-Logo-2.svg)

#### See Modelon Impact in action

Modelon Impact’s product manager walks through how the platform elevates your system engineering capabilities.

[Watch](/modelon-impact-product-overview/)

#### **Fully customizable**
models

Harness the complete customizability of your system model to better fit your use case for simulation.

- Switch between diagram view and code view for a flexible modeling experience
- Design custom workflows that better fit your personal system design and simulation process
- Build models faster with drag and drop libraries
- Use the Experiment Builder to define and run parallel experiments on your models

[Explore Modelon Impact](/modelon-impact-product-overview/)

![A model created with Modelon Impact and a terminal with Modelica code](https://modelon.com/wp-content/uploads/2025/02/Impact-Customizable-Models.svg)

![Illustration depicting a model an integrating into third party tools like MS Excel](https://modelon.com/wp-content/uploads/2025/02/Impact-Customizable-Models-XLS.svg)

#### **Flexible** Integrations

Connect Modelon Impact across your tech stack to streamline and improve design decisions.

- Import and export FMUs for use across tools
- Access Modelon Impact directly in Microsoft Excel to visualize simulation results
- Share models in App Mode to simplify interfaces and focus users on results
- Build custom connections to third-party software using REST API
- Retain version control in GitHub with a direct integration to Modelon Impact

[Explore Modelon Impact](/modelon-impact-product-overview/)

#### **Powerful** simulation solver

Run complex simulations and visualize results with ease to make important design decisions.

- Use a suite of numerical solvers for dynamic and steady state simulation
- See results progress in real-time with live plotting
- Write Python custom functions directly in Modelon Impact
- Simulate system models built from various sources with co-simulation

[Explore Modelon Impact](/modelon-impact-product-overview/)

![A model created by Modelon Impact and a representation of a simulation solver](https://modelon.com/wp-content/uploads/2025/02/Impact-Simluation-Solver.svg)

![](https://modelon.com/wp-content/uploads/2025/02/Hybrid-Energy-opt-1024x512.jpg)

![](https://modelon.com/wp-content/uploads/2025/02/HybridSystem-1024x651.jpg)

#### Energy Systems Optimization

Leverage system and component models for hydrogen, electricity, and thermal energy production facilities and perform techno-economic optimizations over defined time horizons.

![](https://modelon.com/wp-content/uploads/2025/02/Heat-Pumps-opt-1024x512.jpg)

![](https://modelon.com/wp-content/uploads/2025/02/IndustrialHP-1024x591.jpg)

#### Industrial Heat Pump Design

Reduce commissioning time for regulatory-compliant heat pumps by optimizing ramp rates, startup and shutdown processes, and implementing advanced control systems.

![](https://modelon.com/wp-content/uploads/2025/02/Data-Cooling-opt-1024x512.jpg)

![](https://modelon.com/wp-content/uploads/2025/02/DataCooling-1024x758.jpg)

#### Controls for Data Center Cooling

Develop robust control strategies to enhance performance, minimize equipment failures, ensure regulatory compliance, and support seamless scalability as infrastructure grows.

More on [Liquid Cooling](https://modelon.com/industries/data-centers/)

Previous











					Next

###### Modelon Impact allowed us to build a robust transient system model. Their customer success team consistently answered questions and were hands-on in helping us make progress.

Michael Johnson

Senior Engineer, Babcock Power

![](https://modelon.com/wp-content/uploads/2025/02/babcock-power-logo.png)

###### Modelon Impact’s integration with JupyterLab has significantly enhanced our engineers’ ability to develop custom web applications, streamlining the communication of technical results to peers and decision-makers.

Ms. Akiko Tajiri

Senior Engineer, MURATEC

![](https://modelon.com/wp-content/uploads/2025/04/Muratec-logo.png)

###### Modelon Impact has been instrumental in modeling complex HVAC systems, including chiller plants. The software’s capabilities have empowered our team to push forward innovative solutions in energy-efficient building technologies.

Mathieu LeCam

Senior Research Scientist in Grid-Interactive Efficient Buildings, BrainBox AI

![](https://modelon.com/wp-content/uploads/2025/02/brainbox-ai-logo.png)

![](https://modelon.com/wp-content/uploads/2025/04/Untitled-design-8.png)

![](https://modelon.com/wp-content/uploads/2025/04/Untitled-design-4.png)

### Modelon Impact Capabilities & Features

##### Modelon Impact Add-ons

###### Deploy Add-On

Run Modelon Impact models through external tools and web applications.

###### Productivity Add-on

Supports multi-core simulation. Offered in tiers depending on number of simultaneous cores.

###### FMU Export Add-on

Supports FMU export for use outside of Modelon Impact.

[Learn More About the Add-ons](/blog/about-deploy-and-productivity-add-ons/)

#### Frequently Asked Questions

##### Why should I choose Modelon Impact over other system simulation tools?

Global companies of all sizes use Modelon Impact as their preferred system simulation tool. One reason is because Modelon Impact is a cloud-based tool. This means that users can access powerful simulation solvers where they have internet access. This also means engineering teams that are spread across different locations can collaborate on designing systems much more easily than with traditional system simulation tools. 

 Second, the components and models that our users need to get started come pre-configured and validated within [Modelon’s libraries](https://modelon.com/modelon-library-suite-modelica-libraries/). Modelon’s modeling libraries cover multiple domains and have been trusted by customers for more than a decade. Through tight collaborations with customers, our experts have created content for relevant trends in each industry vertical that Modelon supports. Modelon Impact Pro users have access to all Modelon content.  

 Another reason why companies select Modelon Impact is because it’s built on open standards. This means that Modelon expects and encourages its users to export and import models so they can be used in conjunction with other engineering tools, such as in-house and 3rd party modeling platforms and workflows. Additionally, Modelon Impact is useful for engineers wanting to take the insights from their system models outside their immediate team. Modelon Impact makes it easy to share models with an intuitive UI called App Mode that non-modeling experts can interpret and run.  

 Lastly, Modelon values efficient and personable customer support. Each customer is assigned a dedicated representative to help them with their tool onboarding, adoption, and support. Customers also have direct access to Modelon’s industry experts who can guide them on approaches to take for specific system simulation challenges.

##### Does Modelon Impact integrate with other tools in my toolchain?

Yes, easily integrate Modelon Impact into your existing in-house...

---

### Resources
- **URL:** https://modelon.com/support-learning/resources/
- **Description:** Access Modelon support and learning resources including comprehensive product documentation, step-by-step tutorials, FAQs, release notes, libraries, and...
- **Modified:** 2026-04-19

Access Modelon support and learning resources including comprehensive product documentation, step-by-step tutorials, FAQs, release notes, libraries, and training materials to help you master Modelon Impact and Modelon tools for system modeling and simulation.

For customers that are in need of assistance, please visit our [Help Center](https://help.modelon.com/latest/).

---

### Training and Learning Resources for Modelon Software
- **URL:** https://modelon.com/support-learning/training/
- **Description:** Expert-led, customized training for Modelon Impact and Modelica-based system simulation. Accelerate your team's adoption with hands-on programs, self-guided resources, and partner courses from TLK Energy.
- **Modified:** 2026-03-31

Engineers adopting system simulation and Modelon Impact software need practical guidance to become productive quickly. Modelon provides customized training programs tailored to your engineering workflows, models, and simulation goals.

The training is designed to help teams accelerate adoption of [Modelon Impact](https://modelon.com/modelon-impact/), apply Modelica-based system simulation, and develop scalable modeling practices across projects.

Whether you are starting with physics-based modeling or expanding advanced simulation workflows, Modelon is here to support your learning journey.

[**Request Info on Modelon Training**](#request-training)

![](https://modelon.com/wp-content/uploads/2026/03/training-for-teams.jpg)

#### Customized Modelon Impact Training for Engineering Teams

Modelon provides custom training programs designed around your organization’s modeling challenges and engineering applications.

Training is delivered by experienced Modelon engineers who work daily with [**Modelon Impact**](/modelon-impact/), Modelica libraries, and system simulation workflows in both product development and consulting projects.

Customized training programs may include:

- Modelon Impact platform training for building, simulating, and analyzing system models
- Modelica modeling best practices for scalable and reusable model development
- System simulation workflows integrating thermal, hydraulic, electrical, and control domains
- Application-focused training for industries such as data centers, automotive, HVAC, and energy systems
- Hands-on modeling exercises using your team’s own architectures and models

If your organization is introducing system simulation into engineering workflows, expanding Modelica adoption, or scaling simulation across teams, let us help you define the right training program.

[Request Modelon Training](#request-training)

#### Learn Modelon Impact with Self-Guided Resources

For foundational learning and day-to-day product guidance, the [**Modelon Help Center**](https://help.modelon.com/latest/) provides a comprehensive set of resources for engineers using Modelon software.

The Help Center includes:

- Getting started guides for Modelon Impact
- Step-by-step modeling tutorials
- Product documentation and technical references
- Best practices for Modelica modeling and system simulation
- Knowledge base articles and troubleshooting resources
- Release notes and feature updates

These resources allow engineers to quickly learn core workflows, answer technical questions, and deepen their understanding of Modelon tools at their own pace.

![](https://modelon.com/wp-content/uploads/2026/03/self-guided-resources.jpg)

![](https://modelon.com/wp-content/uploads/2026/03/Quadratisch.jpg)

#### Modelica Online Training Courses from Modelon Partner TLK Energy

For engineers looking for virtual, open-enrollment courses in Modelica modeling and system simulation, consider training programs offered by our partner **TLK Energy.**

In these week-long, hands-on courses, attendees will learn object-oriented modeling and simulation using concrete examples from the field of thermal systems. 

**TLK Energy’s Modelica training is conducted using Modelon Impact**, giving participants hands-on experience building and simulating models directly in the platform.

Learn more about [**training opportunities from TLK Energy.**](https://tlk-energy.de/en/modelica-training)

#### Supporting Your Simulation Journey

Successfully adopting system simulation and Modelica-based modeling requires both the right tools and the right expertise. Modelon works closely with customers to ensure teams can learn, adopt, and scale simulation effectively.

Through a combination of:

- Customized training programs
- Self-service learning resources
- Partner-led Modelica training

Modelon supports engineering teams at every stage of their simulation journey.

[**Contact Modelon**](https://modelon.com/talk-to-an-expert/) to discuss your team’s training needs.

#### Request Training

---

### Thanks for your submission!
- **URL:** https://modelon.com/thank-you-request-demo/
- **Description:** One of our experts will get back to you soon...usually within one business day.







Get Insights from our Blog





























Get Social With...
- **Modified:** 2026-03-26

**One of our experts will get back to you soon…usually within one business day.**

#### Get Insights from our Blog

- ![Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/wp-content/uploads/2026/05/Blog-ML-Featured-Image-1202x-626.avif)

  [Agentic AI](https://modelon.com/blog/category/ai/agentic-ai/), [AI](https://modelon.com/blog/category/ai/), [Blog](https://modelon.com/blog/category/blog/)

  ### [Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/)

  Key Takeaways for Engineers and Engineering Leaders Why Simulation Teams Are Looking to Machine Learning Engineering teams today are balancing…

  [Read: Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/)
- ![Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/wp-content/uploads/2026/05/Data-Center-1200x625-2.avif)

  [AI](https://modelon.com/blog/category/ai/), [Blog](https://modelon.com/blog/category/blog/)

  ### [Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/)

  Modelon & University of Maryland Researchers to Present Findings at Upcoming Conference Artificial intelligence and high-performance computing (HPC) are fundamentally…

  [Read: Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/)
- ![How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/wp-content/uploads/2026/05/Weather-Sweep-Social-1200x625-1.avif)

  [Blog](https://modelon.com/blog/category/blog/), [News](https://modelon.com/blog/category/news/)

  ### [How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/blog/how-to-do-weather-file-sweeps-in-modelon-impact/)

  A customer told us something recently that stuck with me. They were evaluating how a data center design would perform…

  [Read: How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/blog/how-to-do-weather-file-sweeps-in-modelon-impact/)

###### Get Social With Us

- [*linkedin-in*](https://www.linkedin.com/company/modelon/)
- [*facebook-f*](https://www.facebook.com/ModelonSoftwareSolutions/)
- [*youtube*](https://www.youtube.com/user/modelonweb)

---

### Thanks for your submission!
- **URL:** https://modelon.com/investor-relations-thank-you-contact-us/
- **Description:** Thanks for contacting us! Someone from our team will reach out to you shortly.






Get Insights from our Blog





























Get Social With...
- **Modified:** 2026-03-26

Thanks for contacting us! Someone from our team will reach out to you shortly.

#### Get Insights from our Blog

- ![Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/wp-content/uploads/2026/05/Blog-ML-Featured-Image-1202x-626.avif)

  [Agentic AI](https://modelon.com/blog/category/ai/agentic-ai/), [AI](https://modelon.com/blog/category/ai/), [Blog](https://modelon.com/blog/category/blog/)

  ### [Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/)

  Key Takeaways for Engineers and Engineering Leaders Why Simulation Teams Are Looking to Machine Learning Engineering teams today are balancing…

  [Read: Accelerating Simulation with Machine Learning: Lessons from a Vehicle Dynamics Thesis](https://modelon.com/blog/accelerating-simulation-with-machine-learning-lessons-from-a-vehicle-dynamics-thesis/)
- ![Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/wp-content/uploads/2026/05/Data-Center-1200x625-2.avif)

  [AI](https://modelon.com/blog/category/ai/), [Blog](https://modelon.com/blog/category/blog/)

  ### [Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/)

  Modelon & University of Maryland Researchers to Present Findings at Upcoming Conference Artificial intelligence and high-performance computing (HPC) are fundamentally…

  [Read: Why Two‑Phase Direct‑to‑Chip Cooling is Reaching a Tipping Point](https://modelon.com/blog/why-two-phase-direct-to-chip-cooling-is-reaching-a-tipping-point/)
- ![How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/wp-content/uploads/2026/05/Weather-Sweep-Social-1200x625-1.avif)

  [Blog](https://modelon.com/blog/category/blog/), [News](https://modelon.com/blog/category/news/)

  ### [How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/blog/how-to-do-weather-file-sweeps-in-modelon-impact/)

  A customer told us something recently that stuck with me. They were evaluating how a data center design would perform…

  [Read: How to Do Weather File Sweeps in Modelon Impact](https://modelon.com/blog/how-to-do-weather-file-sweeps-in-modelon-impact/)

###### Get Social With Us

- [*linkedin-in*](https://www.linkedin.com/company/modelon/)
- [*facebook-f*](https://www.facebook.com/ModelonSoftwareSolutions/)
- [*youtube*](https://www.youtube.com/user/modelonweb)

---

### Talk to an Expert
- **URL:** https://modelon.com/talk-to-an-expert/
- **Modified:** 2026-03-20



---

### Request a Modelon Impact Demo
- **URL:** https://modelon.com/request-a-modelon-impact-demo/
- **Modified:** 2026-03-20



---

### Contact Us About Modelon Services
- **URL:** https://modelon.com/contact-services/
- **Description:** Modelon provides software solutions and expert services to organizations worldwide. With offices globally positioned we're ready to help.
- **Modified:** 2026-03-20



---

### Contact Us
- **URL:** https://modelon.com/contact-us/
- **Description:** Reach out to us to learn more about our robust software solutions and expert services. Our international team proudly serves organizations worldwide.
- **Modified:** 2026-03-17



---

### Industries
- **URL:** https://modelon.com/industries/
- **Description:** Modelon Impact can be utilized for automotive, industrial, aerospace, and other uses. See how our solutions can meet your specific system modeling needs.
- **Modified:** 2026-02-26



---

### February 17, 2026 – Q4 Investor Presentation
- **URL:** https://modelon.com/february-17-2026-q4-investor-presentation/
- **Description:** https://player.vimeo.com/video/1153886985
- **Modified:** 2026-02-20



---

### Refining Control Strategy with Modelica-based Modeling
- **URL:** https://modelon.com/refining-control-strategy-with-modelica-based-modeling/
- **Description:** Optimize your control strategy by integrating comprehensive modeling to eliminate guesswork and boost performance.
- **Modified:** 2026-02-09

#### Refining Control Strategy with Modelica-based Modeling

##### Get your free copy of our eBook and discover how dynamic, physics-based simulations drive superior control strategies—faster, safer, and more profitably than ever before.

![](https://modelon.com/wp-content/uploads/2025/04/Controls-Ebook-Image.png)

Are your control systems still limited by manual tuning, physical testing, or overly simplistic models? The next wave of product innovation demands a deeper, more holistic approach to simulation. In this eBook, *From Concept to Control: Using Comprehensive Modeling Solutions to Fully Explore Control Strategy*, you’ll learn how leading organizations transform their engineering workflows, eliminate guesswork, and stay ahead of the competition.

###### **What You’ll Learn**

- **Why traditional testing methods fall short** of modern regulatory demands and changing market expectations.
- **How dynamic, multi-physics simulations** let you predict and optimize long-term performance in a fraction of the usual time.
- **Real-world industry success stories**—from HVAC to power generation—that illustrate game-changing efficiency gains and streamlined workflows.
- **The practical steps** to integrate comprehensive modeling into your existing processes and unlock best-in-class control strategies.

Ready to revolutionize your approach to control? Download our free ebook now and start exploring the full potential of physics-based simulation. Your future customers—and your bottom line—will thank you.

#### Download the eBook

Name

This field is for validation purposes and should be left unchanged.

First Name(Required)

Last Name(Required)

Email(Required)

Phone

Job Title(Required)

Company(Required)

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCôte d'IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSaint MartinSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

Industry(Required)

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC & RefrigerationIndustrialTelecommunicationsAcademiaOther

I consent to my submitted data being collected and stored.(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I would like to subscribe to Modelon's quarterly e-newsletter.

Yes

---

### Heat Pump System Design and Simulation
- **URL:** https://modelon.com/heat-pump-design-simulation/
- **Description:** Explore the essentials of heat pump system design for efficient heating and cooling solutions tailored to your needs.
- **Modified:** 2026-02-09



---

### Get JModelica.Org
- **URL:** https://modelon.com/get-jmodelica-org/
- **Modified:** 2026-02-09

LinkedIn

This field is for validation purposes and should be left unchanged.

First Name(Required)

Last Name(Required)

Email(Required)

Phone

Job Title(Required)

Company(Required)

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCôte d'IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSaint MartinSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

Industry(Required)

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC & RefrigerationIndustrialTelecommunicationsAcademiaOther

I consent to my submitted data being collected and stored.(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I would like to subscribe to Modelon's quarterly e-newsletter.

Yes

---

### Training Request
- **URL:** https://modelon.com/support-learning/training-registration-sales/
- **Description:** Contact Modelon



If you are interested in a customized training course please indicate this in the questions/comments section.
- **Modified:** 2026-02-09

#### Contact Modelon

If you are interested in a customized training course please indicate this in the questions/comments section.

Facebook

This field is for validation purposes and should be left unchanged.

First Name(Required)

Last Name(Required)

Job Title(Required)

Company or Institution Name(Required)

Company or Institution Email Address(Required)

Phone

Industry

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC and RefrigerationIndustrialTelecommunicationsAcademiaOther

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCôte d'IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSaint MartinSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

Interested in

Customized Workshops

Customized Training

Custom Workflow Development

Customized App Development

Custom Component, Model and Library Development

Strategic Consulting Services

How can we help?

I consent to my submitted data being collected and stored.(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I'd like to join Modelon's newsletter

Yes

---

### Meet Modelon at PTC 2026
- **URL:** https://modelon.com/meet-modelon-at-ptc-2026/
- **Description:** As data centers scale in power density and complexity, liquid cooling becomes a necessity. But managing it efficiently poses new operational risks. Modelon...
- **Image:** https://modelon.com/wp-content/uploads/2025/12/Event-Featured-Image-1.png
- **Modified:** 2026-02-09

![Meet with Modelon at PTC'26 in Hawaii](https://modelon.com/wp-content/uploads/2025/12/Event-Featured-Image-1-1024x533.png)

As data centers scale in power density and complexity, liquid cooling becomes a necessity. But managing it efficiently poses new operational risks. **Modelon Impact** delivers a physics-based system simulation purpose-built for liquid cooled infrastructures. Test thousands of design scenarios virtually to shorten development-to-commission cycles, reduce risk, and improve energy efficiency across the entire cooling ecosystem.

Schedule a one-to-one meeting with our experts in the **PTC Hub, Table #53** by completing the form below.

Learn more and [register](https://www.ptc.org/attend/) for the conference. Connect with our experts attending the event, [Matt Brisbois](https://www.linkedin.com/in/mattbrisbois/) and [Lixiang Li](https://www.linkedin.com/in/lixiang-li-a0528139/) on LinkedIn.

##### Schedule a meeting with our system simulation experts at PTC 26.

LinkedIn

This field is for validation purposes and should be left unchanged.

First Name(Required)

Last Name(Required)

Job Title(Required)

Company or Institution Name(Required)

Company or Institution Email Address(Required)

Phone(Required)

Industry

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC and RefrigerationIndustrialTelecommunicationsAcademiaOther

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCôte d'IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSaint MartinSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

How can we help?

I consent to my submitted data being collected and stored.(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I would like to subscribe to Modelon's quarterly e-newsletter.

Yes

---

### Maximize performance, reliability, and ROI of data center cooling systems.
- **URL:** https://modelon.com/maximize-performance-reliability-and-roi-of-data-center-cooling-systems/
- **Description:** As data centers scale in power density and complexity, liquid cooling becomes a necessity. But managing it efficiently poses new operational risks. Modelon...
- **Image:** https://modelon.com/wp-content/uploads/2025/12/Event-Featured-Image-1.png
- **Modified:** 2025-12-19

![Meet with Modelon at PTC'26 in Hawaii](https://modelon.com/wp-content/uploads/2025/12/Event-Featured-Image-1-1024x533.png)

As data centers scale in power density and complexity, liquid cooling becomes a necessity. But managing it efficiently poses new operational risks. **Modelon Impact** delivers a physics-based system simulation purpose-built for liquid cooled infrastructures. Test thousands of design scenarios virtually to shorten development-to-commission cycles, reduce risk, and improve energy efficiency across the entire cooling ecosystem.

Schedule a one-to-one meeting with our experts in the **PTC Hub, Table #53** by completing the form below.

Learn more and [register](https://www.ptc.org/attend/) for the conference. Connect with our experts attending the event, [Matt Brisbois](https://www.linkedin.com/in/mattbrisbois/) and [Lixiang Li](https://www.linkedin.com/in/lixiang-li-a0528139/) on LinkedIn.

[Schedule a Meeting at PTC](https://outlook.office.com/book/MeetwithModelonatPTC2026@modelon.com/)

---

### Partners & Resellers
- **URL:** https://modelon.com/company/partners-resellers/
- **Description:** Modelon proudly supports an expanding list of Modelica-based platforms. Partners include: ANSYS, Dassault Systèmes, Maplesoft, Ricardo, ESI ITI, Siemens
- **Modified:** 2025-12-12



---

### Research and Academia System Simulation Solutions
- **URL:** https://modelon.com/industries/academia-research/
- **Description:** Looking to bring systems modeling and simulation to your institution or into your academic research? See how Modelon may be able to help!
- **Modified:** 2025-11-19



---

### Modelon Impact Product Overview
- **URL:** https://modelon.com/modelon-impact-product-overview/
- **Modified:** 2025-11-13



---

### Extra General Meeting 2025-10-21
- **URL:** https://modelon.com/egm2025oct/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Tuesday, October 21, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ)...
- **Modified:** 2025-10-07

An Extraordinary General Meeting in Modelon AB (publ) will be held on Tuesday, October 21, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn@modelon.com no later than October 17, 2025. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals are available for download below.

- Notice and agenda for Extra General Meeting ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2025/10/Notice-EGM-251021.pdf))
- Annual report 2024 ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/03/Modelon-year-2024.pdf))
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/10/Fullmaktsformular-251021.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2025/10/Proxy-form-251021.pdf))
- Postal voting form ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/10/Formular-postrostning-251021.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2025/10/Postal-voting-form-251021.pdf))
- Short biography of [Jason Yoo](https://modelon.com/people/jason-yoo/)representing Briarwood Capital Partners, fourth largest owner​
  with 5.5% share ownership after the September rights issue​
- Short biography of [Johan Andreasson](https://modelon.com/people/johan-andreasson/)representing Noledom Holding AB, largest owner with 27.6% share ownership after the September rights issue

---

### Careers
- **URL:** https://modelon.com/company/careers/
- **Description:** We are continuously looking for talented and skilled engineers with experience in modeling, simulation, and computer programming. Your qualities should include...
- **Modified:** 2025-10-03

We are continuously looking for talented and skilled engineers with experience in modeling, simulation, and computer programming. Your qualities should include a strong analytical mind, customer orientation, self-governed efficiency, and team capabilities. You appreciate the challenges of working in a growing global company.

#### Open Positions

---

### MODELICA: WHAT IS IT AND WHY IS IT IMPORTANT?
- **URL:** https://modelon.com/what-is-modelica/
- **Description:** Modelica is a non-proprietary, object-oriented language for modeling complex physical systems. It allows users to represent and simulate mechanical, electrical, thermal, hydraulic, and control systems using reusable, mathematically governed components.
- **Modified:** 2025-08-18

[<<Back to About Modelon](https://modelon.com/company/)

#### What is Modelica?

Modelica is a non-proprietary, object-oriented language for modeling and simulating complex physical systems. It represents mechanical, electrical, thermal, hydraulic, and control domains using reusable, mathematically governed components which allows multidisciplinary system behavior to be described, simulated, and validated in one environment.

The value of the Modelica language is that users are enabled to effectively design and operate their technical systems within any Modelica-compliant tool.

Modelon is committed to delivering on the promise of Modelica’s open-standard technologies – offering Modelica-based libraries that have been developed in close cooperation with leading enterprises to reflect industry needs and trends. Modelon will continue to prove and provide excellence in solutions for simulation-based systems design needed to serve any client.

#### WHAT IS THE BENEFIT OF SWITCHING TO MODELICA?

The benefit of switching to Modelica is **efficiency**! In many modeling languages users must describe the same object in 4 or 5 different ways to get through all stages of product design. With Modelica you need only one single description. That gain in efficiency lowers the threshold of entry to introduce or improve any model-based design process, existing or newly designed.

#### Learn More

- Modelon offers expert led training courses. Attend a [Modelica course](https://modelon.com/support-learning/training/modelica-introduction/)!
- Interested in the nitty-gritty? Visit the [modelica.org website](https://www.modelica.org/) for more information on the language.

**What is Modelica used for?**

Modelica is used to model and simulate dynamic systems across multiple domains—mechanical, electrical, thermal, hydraulic, and control—so engineers can analyze system behavior and optimize designs.

**How is Modelica different from Simulink?**

Modelica is a declarative, equation-based language with standardized, reusable libraries; Simulink is a block-diagram environment. Many tools support co-simulation between them via FMI.

**Is Modelica free?**

The Modelica language and many core libraries are open and free to use. Tools that implement Modelica range from open-source (e.g., OpenModelica) to commercial solutions (e.g., Modelon Impact).

**What is the benefit of switching to Modelica?**

Efficiency and reuse. You describe system physics once with standardized components, then simulate, test, and iterate across use cases—reducing duplication and speeding development.

---

### Leadership
- **URL:** https://modelon.com/company/leadership/
- **Modified:** 2025-08-13



---

### Extra General Meeting 2025-08-20
- **URL:** https://modelon.com/egm2025aug/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Wednesday, August 20, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ)...
- **Modified:** 2025-07-17

An Extraordinary General Meeting in Modelon AB (publ) will be held on Wednesday, August 20, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn@modelon.com no later than August 14, 2025. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals are available for download below.

- Notice and agenda for Extra General Meeting ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2025/07/Notice-EGM-250820.pdf))
- Annual report 2024 ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/03/Modelon-year-2024.pdf))
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/07/Fullmaktsformular-250820.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2025/07/Proxy-form-250820.pdf))
- Postal voting form ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/07/Formular-postrostning-250820.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2025/07/Postal-voting-form-250820.pdf))
- Board of Directors’ report pursuant to Ch. 13:6 and Ch 14:8 of the Swedish Companies Act ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/07/SE_Board-of-Directors-report-250715.pdf)/[English pdf](https://modelon.com/wp-content/uploads/2025/07/Board-of-Directors-report-250715.pdf))
- Auditor’s statement pursuant to Ch. 13:6 and Ch 14:8 of the Swedish Companies Act ([Swedish pdf](https://modelon.com/wp-content/uploads/2025/07/Auditors-statement-250716.pdf))
- Full terms for warrants of series TO1 2025/2028 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2025/07/Ai-Terms_Warrants_TO1-2025_2028.pdf))
- Full terms for warrants of series TO2 2025/2028 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2025/07/Aii-Terms_Warrants_TO2-2025_2028.pdf))

---

### Community Coming Soon
- **URL:** https://modelon.com/community-coming-soon/
- **Description:** Modelon Community Coming Soon



A new space for modeling and simulation experts to connect, collaborate, and grow—together.



Be among the first to get...
- **Modified:** 2025-07-15

![](https://modelon.com/wp-content/uploads/2025/07/Help-Center-Background.png)

### Modelon Community Coming Soon

A new space for modeling and simulation experts to connect, collaborate, and grow—together.

Be among the first to get exclusive early access.

---

### Modelon Library Suite
- **URL:** https://modelon.com/modelon-library-suite-modelica-libraries/
- **Description:** Modelica libraries built by Modelon have been developed in close cooperation with leading enterprises to reflect industry needs and trends.
- **Modified:** 2025-04-08



---

### Modelon Impact Overview (longform)
- **URL:** https://modelon.com/modelon-impact-overview-l/
- **Modified:** 2025-03-17



---

### HVAC & Refrigeration
- **URL:** https://modelon.com/industries/hvac-system-simulation-solution/
- **Description:** Modelon Impact enables accurate physical modeling and simulation for HVAC and Refrigeration systems and sub-systems.
- **Image:** https://modelon.com/wp-content/uploads/2022/09/Modelon_Energy_Process_Hero_770_650_v1.svg
- **Modified:** 2025-02-28



---

### Thermal Power Generation and Storage
- **URL:** https://modelon.com/thermal-power-generation-and-storage/
- **Description:** Discover the advantages and challenges of thermal power generation system design in the quest for sustainable energy solutions.
- **Image:** https://modelon.com/wp-content/uploads/2024/03/Guide_Thermal_Hero_v2.jpg
- **Modified:** 2025-02-28



---

### Thermal Power Library
- **URL:** https://modelon.com/library/thermal-power-library/
- **Description:** Modelon’s Thermal Power Library provides a comprehensive modeling, simulation, and optimization framework for thermal power plant operation.
- **Modified:** 2025-02-28



---

### Hydrogen Storage
- **URL:** https://modelon.com/hydrogen-storage/
- **Description:** Learn how hydrogen storage can revolutionize energy storage solutions and contribute to a sustainable future.
- **Image:** https://modelon.com/wp-content/uploads/2024/03/Guide_Hydrogen_Hero_v3.jpg
- **Modified:** 2025-02-28



---

### Control Strategy Design Solutions Overview
- **URL:** https://modelon.com/control-systems-design-solutions-overview/
- **Description:** Learn how Modelon supports control systems design applications through modeling and simulation technology.
- **Modified:** 2025-02-28



---

### Board of Directors
- **URL:** https://modelon.com/investor-relations/board-of-directors/
- **Modified:** 2025-02-04

---

---

### TISAX Compliance
- **URL:** https://modelon.com/tisax-compliance/
- **Description:** For Modelon, confidentiality, availability and integrity of information are of greatest importance. We have taken extensive measures to protect sensitive and...
- **Modified:** 2025-01-31

![](https://modelon.com/wp-content/uploads/2025/01/TISAX-Result-coloured-RGB-300x167.jpg)

For Modelon, confidentiality, availability and integrity of information are of greatest importance. We have taken extensive measures to protect sensitive and confidential information. 

To verify that our measures are sufficient, we have used an external accredited auditor to go through our routines and technical solutions, based on a [requirements catalogue](https://portal.enx.com/isa5-en.xlsx) with key aspects of the international standard ISO/IEC 27001. The requirements are adapted to the automotive industry and expanded in some areas. This assessment, TISAX(Trusted Information Security Assessment Exchange), is developed by the German Association of the Automotive Industry (VDA).

Modelon has completed a TISAX audit, conducted by Det Norske Veritas (DNV). TISAX results are not intended for the general public, but if you are a TISAX participant, our results can be retrieved [from the ENX portal](https://enx.com/TISAX/tisaxassessmentresults). 

Modelon AB in Lund, Sweden 

Scope-ID: S5VHH2 

Assessment-ID: ALTMK2-2

Modelon Deutschland GmbH in Munich, Germany

Scope-ID: S69F2P 

Assessment-ID: ALTMK3-2

---

### Renewable Energy Integration Solutions
- **URL:** https://modelon.com/renewable-energy-integration-solutions/
- **Description:** Learn how Modelon supports renewable energy integration applications for system design through modeling and simulation.
- **Modified:** 2024-12-25



---

### Services
- **URL:** https://modelon.com/services/
- **Description:** Need help with training, integration, or consulting? No matter where you are on your journey with system design and simulation, we’re here to assist.
- **Modified:** 2024-12-18



---

### Thermal Management Solutions Overview
- **URL:** https://modelon.com/thermal-management-solutions-overview/
- **Description:** Learn how Modelon supports thermal management applications for system design through modeling and simulation.
- **Modified:** 2024-12-04

Thermal management is an essential aspect of many industrial applications, including aerospace, automotive, energy, and HVAC&R systems. Effective thermal management is crucial for the safe and efficient operation of these systems.  

Modelon Impact can be used to design, simulate, and evaluate thermal management systems for both performance and economic impact. Applied to a single system or integrated thermal systems, engineers can design and optimize their systems for performance, efficiency, and safety, efficiency, and effectiveness for a range of thermal attributes over real-world scenarios.   

---

### Hydrogen Storage Solutions Overview
- **URL:** https://modelon.com/hydrogen-storage-solutions-overview/
- **Description:** Learn how Modelon supports hydrogen storage applications for aerospace, automotive, and energy through system modeling and simulation.
- **Modified:** 2024-11-20



---

### Vehicle Dynamics Library
- **URL:** https://modelon.com/library/vehicle-dynamics-library/
- **Description:** Modelon's Vehicle Dynamics Library provides an open and user-extensible environment for full vehicle and vehicle subsystem analysis. 
- **Modified:** 2024-10-31



---

### Liquid Cooling Library
- **URL:** https://modelon.com/library/liquid-cooling-library/
- **Description:** This library is used for modeling and simulating liquid cooling systems for virtual prototyping, component dimensioning and control design.
- **Modified:** 2024-10-31



---

### Jet Propulsion Library
- **URL:** https://modelon.com/library/jet-propulsion-library/
- **Description:** Our library provides a foundation for modeling and simulating jet engines, including model-based design of integrated aircraft systems.
- **Modified:** 2024-10-31



---

### Heat Exchanger Library
- **URL:** https://modelon.com/library/heat-exchanger-library/
- **Description:** Our Heat Exchanger Library provides an environment for heat-exchanger design and analysis with convenient system model integration interfaces.
- **Modified:** 2024-10-31



---

### Environmental Control Library
- **URL:** https://modelon.com/library/environmental-control-library/
- **Description:** This Modelica library for aircraft environmental control systems is designed to study energy consumption and thermal conditions.
- **Modified:** 2024-10-31



---

### Engine Dynamics Library
- **URL:** https://modelon.com/library/engine-dynamics-library/
- **Description:** Modelon's Engine Dynamics Library is used for multi-domain combustion engine systems modeling, simulation and analysis.
- **Modified:** 2024-10-31



---

### Energy Systems Library
- **URL:** https://modelon.com/library/energy-systems-library/
- **Description:** This Modelica-based library designed to plan and optimize industrial to utility-scale energy systems. It enables holistic system analyses.
- **Modified:** 2024-10-31



---

### Electrification Library
- **URL:** https://modelon.com/library/electrification-library/
- **Description:** Our Electrification Library is a multi-physics Modelica library suitable for a variety of electrification applications, including aircraft.
- **Modified:** 2024-10-31



---

### Electric Power Library
- **URL:** https://modelon.com/library/electric-power-library/
- **Description:** The Modelica Electric Power Library is ideal for efficient modeling, simulation and analysis of electric power systems, including smart grids.
- **Modified:** 2024-10-31



---

### Aircraft Dynamics Library
- **URL:** https://modelon.com/library/aircraft-dynamics-library/
- **Description:** Modelon’s Aircraft Dynamics Library is a Modelica-based library for the design and simulation of fixed-wing aircraft and their sub-systems.
- **Modified:** 2024-10-31



---

### Air Conditioning Library
- **URL:** https://modelon.com/library/air-conditioning-library/
- **Description:** The Modelica Air Conditioning Library is used  to design, analyze and optimize automotive air conditioning systems during early design stages.
- **Modified:** 2024-10-31



---

### Vapor Cycle Library
- **URL:** https://modelon.com/library/vapor-cycle-library/
- **Description:** The Modelica Vapor Cycle Library is used to design vapor cycle systems for heating, cooling and waste-heat recovery, including heat pumps.
- **Modified:** 2024-10-31



---

### Pneumatics Library
- **URL:** https://modelon.com/library/pneumatics-library/
- **Description:** Modelon’s Pneumatics Library is used to verify and optimize the design of complete pneumatic systems throughout a product lifecycle.
- **Modified:** 2024-10-31



---

### Hydro Power Library
- **URL:** https://modelon.com/library/hydro-power-library/
- **Description:** Our library provides a framework for modeling and simulating hydro power plant operations, enabling users to study multiple plant designs.
- **Modified:** 2024-10-31



---

### Hydraulics Library
- **URL:** https://modelon.com/library/hydraulics-library/
- **Description:** Modelon’s Hydraulics Library is valuable for industries that develop hydraulic components, like automotive, aerospace and industrial equipment.
- **Modified:** 2024-10-31



---

### Fuel System Library
- **URL:** https://modelon.com/library/fuel-system-library/
- **Description:** The Fuel System Library is a tested Modelica library targeting the design and verification of fuel systems on civil and military aircraft.
- **Modified:** 2024-10-31



---

### Fuel Cell Library
- **URL:** https://modelon.com/library/fuel-cell-library/
- **Description:** The Fuel Cell Library helps model, simulate, analyze and control fuel cell design and operation, especially for PEMFC and SOFC systems.
- **Modified:** 2024-10-31



---

### Industrial Equipment
- **URL:** https://modelon.com/industries/industrial-equipment/
- **Description:** Modelon Impact has everything you need for physical modeling and simulation for a range of industrial equipment applications. Check out our platform here!
- **Modified:** 2024-10-30



---

### Don’t Miss The Next Modelon Innovate Conference
- **URL:** https://modelon.com/innovate_information_signup/
- **Description:** Modelon Innovate is a conference for engineering design and innovation focused on approaching commercial challenges with simulation.
- **Modified:** 2024-10-22

![](https://modelon.com/wp-content/uploads/2023/10/7bef3672-951b-4ef6-ba0f-6bee299c52a3-2048x839-1-1024x349.png)

Thank you to everyone who attended Modelon Innovate 2024 in Copenhagen, Denmark! This conference took Modelon Innovate to a new level with presentations from engineering design and innovation professionals tackling long-standing sustainability challenges with system simulation.

The next Modelon Innovate conference is already in the works. Don’t miss our announcements, including dates, early registration, and speaking opportunities. **Sign up for the latest information.**

##### Keep me posted about the next Modelon Innovate conference! 

---

### Energy and Power System Simulation Solutions
- **URL:** https://modelon.com/industries/energy-power-system-simulation-optimization-software/
- **Description:** Modelon's energy and power system simulation software enables users to develop energy storage systems, renewable energy integration, control design.
- **Image:** https://modelon.com/wp-content/uploads/2022/09/Modelon_Energy_Process_Hero_770_650_v1.svg
- **Modified:** 2024-09-25



---

### Automotive System Simulation Solutions
- **URL:** https://modelon.com/industries/automotive-system-simulation-software/
- **Description:** Find out how our cloud-native platform, Modelon Impact, enables accurate automotive modeling and simulation for vehicle systems and sub-systems.
- **Modified:** 2024-09-25



---

### Aerospace System Simulation Solutions
- **URL:** https://modelon.com/industries/aerospace-systems-modeling-and-simulation-software/
- **Description:** Learn how Modelon’s cloud-native platform, Modelon Impact, enables accurate physical modeling and simulation for aerospace systems and sub-systems.
- **Modified:** 2024-09-25



---

### Extra General Meeting 2024-09-19
- **URL:** https://modelon.com/egm2024sep/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Thursday, September 19, at IDEON Science Park in Lund.



Shareholders in Modelon AB...
- **Modified:** 2024-08-19

An Extraordinary General Meeting in Modelon AB (publ) will be held on Thursday, September 19, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn@modelon.com no later than September 13, 2024. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals are available for download below.

·Notice and agenda for Extra General Meeting ([Swedish/English](https://modelon.com/wp-content/uploads/2024/08/Notice-EGM-240919.pdf) pdf)

·Annual report 2023 ([Swedish](https://modelon.com/wp-content/uploads/2024/03/Modelon-AR-2023.pdf) pdf)

·Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/08/Fullmaktsformular-240919.pdf), [English pdf](https://modelon.com/wp-content/uploads/2024/08/Proxy-form-240919.pdf))

·Short biography of [Mikael Bluhme](https://modelon.com/people/mikael-bluhme/) representing Roosgruppen AB, third largest owner with 10.26% in votes and capital after the July rights issue and suggested share conversion.

---

### Extra General Meeting 2024-06-07
- **URL:** https://modelon.com/egm2024jun-2/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, June 7th, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ) can...
- **Modified:** 2024-08-02

An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, June 7th, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn @ modelon.com no later than May 31st, 2024. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals for the extraordinary general meeting are available for download below.

- Notice and agenda for Extra General Meeting ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Modelon-Kallelse-EGM-240607.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2024/05/Modelon-Notice-EGM-240607.pdf))
- Annual report 2023 ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/03/Modelon-AR-2023.pdf))
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Fullmaktsformular-240607.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2024/05/Proxy-form-240607.pdf))
- Board of Directors’ report pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/05/Board-of-Directors-report-240521.pdf))
- Auditor’s statement pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/05/Auditors-statement-240521.pdf))
- Auditor’s review of Q1 interim report ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Q1-Revisorns-granskningsrapport-240521.pdf)/ [English pdf](https://modelon.com/wp-content/uploads/2024/05/Q1-Auditors-Review-report-240521.pdf))

---

### Extra General Meeting 2024-08-16
- **URL:** https://modelon.com/egm2024aug/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, August 16, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ)...
- **Modified:** 2024-08-02

An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, August 16, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn @ modelon.com no later than Aug 12, 2024. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals for the extraordinary general meeting are available for download below.

- Notice and agenda for Extra General Meeting ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/08/Notice-EGM-240816.pdf))
- Annual report 2023 ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/03/Modelon-AR-2023.pdf))
- Proxy form ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/08/Proxy-form-240816.pdf))
- Board of Directors’ report pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/08/Board-of-Directors-report-240731.pdf))
- Auditor’s statement pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/08/Auditors-statement-240731.pdf))

---

### Technology Licensing
- **URL:** https://modelon.com/technology-licensing/
- **Modified:** 2024-07-03



---

### Open Source
- **URL:** https://modelon.com/open-source/
- **Description:** To practice our commitment to open-source technology, Modelon has developed open-source tools and resources that are available to everyone. Learn about them...
- **Modified:** 2024-07-03

To practice our commitment to open-source technology, Modelon has developed open-source tools and resources that are available to everyone. Learn about them below.  

#### JModelica.Org

JModelica is an open-source Modelica compiler.

Modelon is making the following changes to JModelica.org: Assimulo, PyFMI and FMI Library are being moved to github. All other parts of the JModelica.org platform, including the Modelica compiler and optimization capabilities, are discontinued as publicly available open source.

The platform is under active development and continues to be available for academic and commercial use.

[ADD BUTTON]: Get JModelica.Org (lead to a separate form)

#### PyFMI 

PyFMI is a package for loading and interacting with Functional Mock-Up Units (FMUs) both for Model Exchange and Co-Simulation, which are compiled dynamic models compliant with the Functional Mock-Up Interface (FMI), see here for more information. 

FMI is a standard that enables tool independent exchange of dynamic models on binary format. Several industrial simulation platforms support the export of FMUs, including, Dymola, JModelica.org, OpenModelica and SimulationX, see here for a complete list. PyFMI offers a Python interface for interacting with FMUs and enables for example loading of FMU models, setting of model parameters and evaluation of model equations. 

PyFMI is available as a stand-alone package or as part of the JModelica.org distribution. Using PyFMI together with the Python simulation package Assimulo adds industrial grade simulation capabilities of FMUs to Python. 

[ADD BUTTON] Get PyFMI (lead to a separate form)

---

### Disclaimer
- **URL:** https://modelon.com/investor-relations/disclaimer/
- **Description:** The information contained in this section of Modelon AB’s (publ) (the “Company”) website contains information in connection with the Company’s offering of...
- **Modified:** 2024-06-17

The information contained in this section of Modelon AB’s (publ) (the “Company”) website contains information in connection with the Company’s offering of shares with pre-emption rights for existing shareholders. This information may not be accessed by residents of certain countries based on applicable securities law regulations.

NOT FOR DISTRIBUTION, DIRECTLY OR INDIRECTLY, WHOLLY OR PARTLY, IN THE UNITED STATES OF AMERICA (INCLUDING ITS TERRITORIES AND POSSESSIONS), ANY STATE OF THE UNITED STATES INCLUDING THE DISTRICT OF COLUMBIA, AUSTRALIA, CANADA, HONG KONG, JAPAN, NEW ZEALAND, SINGAPORE, SWITZERLAND, SOUTH AFRICA, SOUTH KOREA OR ANY OTHER JURISDICTION WHERE TO DO SO WOULD BE PROHIBITED BY APPLICABLE LAW.

This website and the information contained herein is not intended for, and may not be accessed by, or distributed or disseminated to, persons resident or physically present in the United States of America (including its territories and possessions), any state of the United States including the District of Columbia (the **“United States”**), Australia, Canada, Hong Kong, Japan, New Zealand, Singapore, Switzerland, South Africa, South Korea and do not constitute an offer to sell or the solicitation of an offer to purchase or acquire, any shares in the Company in any of the above mentioned jurisdictions or in any other jurisdiction in which such offer or solicitation would be unlawful prior to registration or qualification under the securities laws of such jurisdiction. The shares in the Company referred to on this website have not been, and will not be, registered under the Securities Act of 1933, as amended (the **“Securities Act”**), and may not be offered or sold in the United States absent registration or an exemption from registration under the Securities Act.

All persons residing outside of the above mentioned jurisdictions who wish to access the documents contained on this website should first ensure that they are not subject to local laws or regulations that prohibit or restrict their right to access this website, or require registration or approval for any acquisition of securities by them. The Company assumes no responsibility if there is a violation of applicable law and regulations by any person.

If you are not permitted to view materials on this website or are in any doubt as to whether you are permitted to view these materials, please exit this website.

Access to electronic versions of these materials is being made available on this website by the Pareto Securities in good faith and for information purposes only.

---

By clicking “Confirm” you confirm and certify that:

- You are not a resident of or physically present in any of the above jurisdictions or any other restricted jurisdiction and are not a U.S. person (as defined in Regulation S under the Securities Act); and
- you are either:

  1. a resident of, or physically present in, Sweden;
  2. if resident or physically present in a Member State of the European Economic Area other than Sweden, a qualified investor as defined in Regulation (EU) 2017/1129 of the European parliament and of the council or Article 2(e) of the Prospectus Regulation (EU) 2017/1129 as it forms part of UK domestic law by virtue of the European Union (Withdrawal) Act 2018 (as amended and supplemented from time to time); or
  3. otherwise authorized to access this information pursuant to applicable laws or regulations.

The information contained in this section of the website is restricted and is not for release, publication or distribution,

            directly or indirectly, in whole or in part in, into or from the United States of America (including its territories and possessions),

            any state of the United States including the District of Columbia, Australia, Canada, Hong Kong, Japan, New Zealand, Singapore, Switzerland,

            South Africa, South Korea or any jurisdiction where to do so might constitute a violation of the local

            securities laws or regulations of such jurisdiction.

[Back to Investor Relations](/investor-relations/)

Confirm

          Don’t confirm

};
        // Implements the MFN-JS Loader javascript file
        if(!window._MFN) {
            var s =  document.createElement("script");
            s.type = "text/javascript";
            s.src = "https://widget.datablocks.se/api/rose/assets/js/mfn/mfn-js-loader-v0.1.0.js";
            s.async = true;
            document.getElementsByTagName("body")[0].appendChild(s);
        }
        window._MFN = {
            // The selector of the element where the content of the
            // single news item should end up
            outlet: '#mfn-container',

// The type of view
            type: 'disclaimer',

// Default language of the news item shown
            lang: 'en',

// 'selected' uses locale from lang, other options are 'en', 'sv'
            // and so on
            l10nLang: 'selected',

single_view_url: '/share-offering-2024-06-17/',
        }

---

### Request Demo General [New Template]
- **URL:** https://modelon.com/request-demo/
- **Modified:** 2024-05-28



---

### Extra General Meeting 2024-06-07
- **URL:** https://modelon.com/egm2024jun/
- **Description:** An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, June 7, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ) can...
- **Modified:** 2024-05-22

An Extraordinary General Meeting in Modelon AB (publ) will be held on Friday, June 7, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn @ modelon.com no later than May 31, 2024. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals for the extraordinary general meeting are available for download below.

- Notice and agenda for Extra General Meeting ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Modelon-Kallelse-EGM-240607.pdf), [English pdf](https://modelon.com/wp-content/uploads/2024/05/Modelon-Notice-EGM-240607.pdf))
- Annual report 2023 ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/03/Modelon-AR-2023.pdf))
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Fullmaktsformular-240607.pdf), [English pdf](https://modelon.com/wp-content/uploads/2024/05/Proxy-form-240607.pdf))
- Board of Directors’ report pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/05/Board-of-Directors-report-240521.pdf))
- Auditor’s statement pursuant to Ch. 13 sec. 6 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/05/Auditors-statement-240521.pdf))
- Auditor’s review report of Q1 interim report ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/05/Q1-Revisorns-granskningsrapport-240521.pdf), [English pdf](https://modelon.com/wp-content/uploads/2024/05/Q1-Auditors-Review-report-240521.pdf))

---

### Industrial Decarbonization – Guide
- **URL:** https://modelon.com/industrial-decarbonization/
- **Description:** Using Modelon technology, industrial site planners and energy providers can plan decarbonization investments and optimize decarbonized sites.
- **Image:** https://modelon.com/wp-content/uploads/2024/03/Guide_Industrial_Decarbonization_Hero_v6.jpg
- **Modified:** 2024-04-25



---

### Annual General Meeting 2024
- **URL:** https://modelon.com/agm2024/
- **Description:** The Annual General Meeting in Modelon AB (publ) will be held on Tuesday, May 14, at IDEON Science Park in Lund.



Shareholders in Modelon AB (publ) can...
- **Modified:** 2024-04-15

The Annual General Meeting in Modelon AB (publ) will be held on Tuesday, May 14, at IDEON Science Park in Lund.

Shareholders in Modelon AB (publ) can register to attend via email to [jonas.eborn@modelon.com](mailto:jonas.eborn@modelon.com) no later than May 7, 2024. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.

All documents and proposals for the annual meeting are available for download below.​

- Notice and agenda for Annual General Meeting ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/04/Notice-annual-meeting-2024.pdf))​
- Annual report 2023 ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/03/Modelon-AR-2023.pdf))​
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2024/04/Proxy-form-240514.pdf))​
- Full terms for employee options of series 2023/2027:1 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/04/12A-Terms_Warrants_series2024_2028_1.pdf))​
- Full terms for personnel options of series 2023/2027:2 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2024/04/13A-Terms_Warrants_series2024_2028_2.pdf))​

---

### Heating, Cooling, and Climate Change
- **URL:** https://modelon.com/heating-cooling-and-climate-change/
- **Description:** Physics-based dynamic models can help engineers develop HVAC systems that reduce energy demand while ensuring comfort amid today's climate changes.
- **Image:** https://modelon.com/wp-content/uploads/2024/02/Guide_HVAC_Hero_v4.jpg
- **Modified:** 2024-03-05

#### Designing for Climate-related Heating and Cooling Challenges

---

### Top Content
- **URL:** https://modelon.com/top-content/
- **Modified:** 2024-03-05

[![carbon neutrality and modelon](https://modelon.com/wp-content/uploads/2023/01/Modelon_Social_500_2022_v1.jpg)](https://modelon.com/top-content/)

##### Driving Towards Carbon Neutrality With System Simulation

In this case study, learn how Honda Honda uses Modelon Impact to design and evaluate a carbon neutral assembly plant with confidence.

[Read More](https://modelon.com/support/becoming-carbon-neutral-with-system-simulation-honda/)

[![](https://modelon.com/wp-content/uploads/2022/07/Modelon_Trends_in_Energy_2022_500.jpg)](https://modelon.com/top-content/)

##### Trends in Energy Technologies for 2023

This blog describes the trends and observations of innovative energy technologies in 2023 and beyond. With over 20 years of experience in model-based engineering, Modelon’s Energy and Process Industry Director, Stéphane Velut, shares his views on energy technologies trends, observations & insights – with a focus on what he is seeing in the industry and how simulation software is enabling world-leading organizations to get ahead. 

[Read More](https://modelon.com/blog/energy-technologies-trends-2023/)

[![physical system simulation - blog 1](https://modelon.com/wp-content/uploads/2022/06/Modelon_System_Simulation_500_v2-1.jpg)](https://modelon.com/top-content/)

##### What is Physical System Simulation?

Physical system simulation is an elusive concept –yet on your journey to virtualize the product design process, there is no way around it. In the first blog of the Success with Simulationseries,PieterDermont, Modelon’s Senior Business Development Director, covers what physical system simulation is, what it is not(hint, it is not“systems engineering”), and what it can do for your organization.

[Read More](https://modelon.com/blog/system-simulation-what-is-physical-system-simulation/)

[![](https://modelon.com/wp-content/uploads/2022/06/Blog_Steady_State_500_v2-2.jpeg)](https://modelon.com/top-content/)

##### Steady State and Dynamic Simulation: What is the difference?

In this blog post, Modelon’s senior simulation engineer, Clément Coïc, summarizes the difference between steady-state and dynamic simulation.

[Read More](https://modelon.com/blog/steady-state-and-dynamic-simulation-what-is-the-difference/)

[![](https://modelon.com/wp-content/uploads/2023/01/Collins-Aerospace-Video-Testimonial-630x630.png)](https://modelon.com/top-content/)

##### Modernizing Modeling at Collins Aerospace

Gregory Leaper, Senior Manager of the Aero-Thermal Fluids group at Collins Aerospace, decided to upgrade his team’s models and workflows to Modelica after years of using Fortran. Listen as Greg talks about his experience in working with Modelon to transition his team and models to a modern modeling language.

[Read More](https://modelon.com/support/modernizing-modeling-at-collins-aerospace/)

---

### Sitemap
- **URL:** https://modelon.com/sitemap/
- **Modified:** 2024-02-21



---

### Speaker Submission Complete
- **URL:** https://modelon.com/innovate2024/speaker-submission-complete/
- **Modified:** 2023-12-31



---

### Registration Complete
- **URL:** https://modelon.com/innovate2024/registration-complete/
- **Modified:** 2023-12-31



---

### Heat Pump Landing V2
- **URL:** https://modelon.com/heat-pump-landing-v2/
- **Modified:** 2023-10-30



---

### Tutorial Session Registration
- **URL:** https://modelon.com/tutorial-session-registration/
- **Modified:** 2023-09-14



---

### Comprehensive System Models
- **URL:** https://modelon.com/modelon-impact-old/comprehensive-system-models/
- **Modified:** 2023-07-28



---

### Model-Based Development Seminar Series with Modelon Impact
- **URL:** https://modelon.com/model-based-development-workshop-series-2023/
- **Description:** This workshop series will provide hands-on practice for Modelica fundamentals, application methods, details of Modelica, and more!
- **Modified:** 2023-06-01



---

### FMI Toolbox
- **URL:** https://modelon.com/fmi-toolbox/
- **Description:** Learn how the FMI Toolbox for MATLAB and Simulink supports major workflows in control systems from design to optimization and MIL/SIL/HIL testing.
- **Modified:** 2023-05-16



---

### FMI Toolbox Coder Add-on
- **URL:** https://modelon.com/fmi-toolbox/fmi-toolbox-coder-add-on/
- **Modified:** 2023-04-26



---

### Annual General Meeting 2023
- **URL:** https://modelon.com/agm2023/
- **Description:** The Annual General Meeting in Modelon AB (publ) will be held on Tuesday, May 16, at IDEON Science Park in Lund.​



Shareholders in Modelon AB (publ) can...
- **Modified:** 2023-04-14

The Annual General Meeting in Modelon AB (publ) will be held on Tuesday, May 16, at IDEON Science Park in Lund.​

Shareholders in Modelon AB (publ) can register to attend via email to jonas.eborn@modelon.com no later than May 9, 2023. Registration should include shareholders name, personal or organization number, address, phone number, and number of shares represented. See full rules for attendance in the announcement below.​

All documents and proposals for the annual meeting are available for download below.​

- Notice and agenda for Annual General Meeting ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2023/04/Notice-annual-meeting-2023.pdf))​
- Annual report 2022 ([Swedish pdf](https://modelon.com/wp-content/uploads/2023/03/Modelon-AR-2022.pdf))​
- Proxy form ([Swedish pdf](https://modelon.com/wp-content/uploads/2023/04/Proxy-form-230516.pdf))​
- Full terms for employee options of series 2023/2027:1 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2023/04/12A-Terms_Warrants_series2023_2027_1.pdf))​
- Full terms for personnel options of series 2023/2027:2 ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2023/04/13A-Terms_Warrants_series2023_2027_2.pdf))​
- Board of directors’ report pursuant to Ch. 14 sec. 8 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2023/04/Board-of-Directors-report-230414_signed.pdf))​
- Auditor’s statement pursuant to Ch. 14 sec. 8 of the Swedish Companies Act ([Swedish/English pdf](https://modelon.com/wp-content/uploads/2023/04/Auditors-statement-ModelonAB-230414.pdf))​

---

### Aerospace System Design with Modelon Impact: On-Demand Webinar
- **URL:** https://modelon.com/webinar-modelon-impact-aerospace-2020/
- **Description:** https://www.youtube.com/watch?v=1JxpKOaknGo&feature=youtu.be
- **Modified:** 2023-03-10



---

### Events
- **URL:** https://modelon.com/company/events/
- **Modified:** 2022-11-15



---

### Test
- **URL:** https://modelon.com/test/
- **Modified:** 2022-11-14



---

### EXTRA GENERAL MEETING
- **URL:** https://modelon.com/egm2022nov/
- **Description:** Extra General Meeting 2022-11-29



Shareholders in Modelon AB (publ) can register to vote in the Extra General Meeting to be held on Tuesday, November 29,...
- **Modified:** 2022-11-14

##### Extra General Meeting 2022-11-29

Shareholders in Modelon AB (publ) can register to vote in the Extra General Meeting to be held on Tuesday, November 29, 2022. The meeting will be done with postal voting procedures according to temporary regulations in the Swedish Company Act to facilitate the implementation of general meetings.
All documents and proposals are available for download below.

- [Announcement and agenda for Extra General Meeting](https://modelon.com/wp-content/uploads/2022/11/Notice-extra-general-meeting-Modelon-221103-1.pdf)
- [Postal voting form](https://modelon.com/wp-content/uploads/2022/11/Postal-voting-form-221129-2.pdf)
- [Proxy form](https://modelon.com/wp-content/uploads/2022/11/Proxy-form-221129-3.pdf)
- [Full terms for warrants of series 2022/2026:1 (Swedish/English pdf)](https://modelon.com/wp-content/uploads/2022/11/7A_Terms-Warrants-series-2022_2026_1-4.pdf)
- [Full terms for warrants of series 2022/2026:2 (Swedish/English pdf)](https://modelon.com/wp-content/uploads/2022/11/8A_Terms-Warrants-series-2022_2026_2-5.pdf)
- [Annual report 2022](https://modelon.com/wp-content/uploads/2022/03/Modelon-AR-2021-1.pdf)
- [Board of directors’ repost pursuant to Ch. 14 sec. 8 of the Swedish Companies Act](https://modelon.com/wp-content/uploads/2022/11/Board-of-directors-report-221028-7.pdf)
- [Auditor’s statement pursuant to Ch. 14 sec. 8 of the Swedish Companies Act](https://modelon.com/wp-content/uploads/2022/11/Auditors-statement-221028-8.pdf)

---

### Sign up for our Newsletter
- **URL:** https://modelon.com/sign-up-for-our-newsletter/
- **Modified:** 2022-09-01

First Name(Required)

Last Name(Required)

Email(Required)

Job Title(Required)

Company Name(Required)

Phone

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAnguillaAntarcticaAntigua and BarbudaArgentinaArmeniaArubaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBonaire, Sint Eustatius and SabaBosnia and HerzegovinaBotswanaBouvet IslandBrazilBritish Indian Ocean TerritoryBrunei DarussalamBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaChristmas IslandCocos IslandsColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCook IslandsCosta RicaCroatiaCubaCuraçaoCyprusCzech RepublicCôte d'IvoireDenmarkDjiboutiDominicaDominican RepublicEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEswatini (Swaziland)EthiopiaFalkland IslandsFaroe IslandsFijiFinlandFranceFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabonGambiaGeorgiaGermanyGhanaGibraltarGreeceGreenlandGrenadaGuadeloupeGuamGuatemalaGuernseyGuineaGuinea-BissauGuyanaHaitiHeard and McDonald IslandsHoly SeeHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsle of ManIsraelItalyJamaicaJapanJerseyJordanKazakhstanKenyaKiribatiKuwaitKyrgyzstanLao People's Democratic RepublicLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacauMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMartiniqueMauritaniaMauritiusMayotteMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMontserratMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew CaledoniaNew ZealandNicaraguaNigerNigeriaNiueNorfolk IslandNorth KoreaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPitcairnPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaRéunionSaint BarthélemySaint HelenaSaint Kitts and NevisSaint LuciaSaint MartinSaint Pierre and MiquelonSaint Vincent and the GrenadinesSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSouth GeorgiaSouth KoreaSouth SudanSpainSri LankaSudanSurinameSvalbard and Jan Mayen IslandsSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTimor-LesteTogoTokelauTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTurks and Caicos IslandsTuvaluUS Minor Outlying IslandsUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.Wallis and FutunaWestern SaharaYemenZambiaZimbabweÅland Islands

Industry(Required)

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC and RefrigerationIndustrialTelecommunicationsAcademiaOther

I consent to my submitted data being collected and stored.*(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I'd like to join Modelon's newsletter

Yes

---

### Press Release
- **URL:** https://modelon.com/investor-relations/press-release/
- **Modified:** 2022-08-22

window._MFN = {

// The selector of the element where the content of the
        // single news item should end up
        outlet: '#container',

// The type of view
        type: 'singleview',

// Default language of the news item shown
        lang: 'all',

// 'selected' uses locale from lang, other options are 'en', 'sv' eg.
        // and so on
        l10nLang: 'en',

// Feed ID, provided by MFN
        feed_id: '07c7a020-b9be-4e5a-96cb-398cb39918ce',

// If it should show date
        show_date: true,

// Show primary image
        show_primary_image: true,

// Optionally add a prefix to the tab title (Meta description)
        title_prefix: 'Press release / ',

// Example of configuring locale and time zone,
        // Swedish timezone is default if date_setting is not set

date_setting: {
            locale: 'sv-SE', // eg. for US 'en-US',
            option: {
                month: 'numeric', // or 'long', 'short'
                year: 'numeric', // or '2-digit'
                day: 'numeric', // or '2-digit'
                timeZone: 'Europe/Stockholm' // eg. 'America/New_York'
            }
        },

// If you want to implement your own custom date formatter
        // you can add your own function
        // format_date: function(date) {
        //     return date.toLocaleTimeString('sv-SE', {
        //         month: 'long',
        //         year: 'numeric',
        //         day: 'numeric',
        //         timeZone: 'Europe/Stockholm'
        //     });
        // },

// Example of implementing your own HTML for a news item
        // post_processor: function(current, item) {
        //  console.log(item)
        //     return '

' + item.content.title + '

'
        // },

// 'default' or eg [{tag: ':regulatory'}]
        show_tags: [{tag: ':regulatory'},{tag: 'sub:report:interim'},{tag: 'sub:report:annual'}],

// Should most likely be true
        use_proxied_attachment_urls: true,

// Show attachments
        show_attachments: true,

// if attachments should be displayed as thumbnails (Default as links)
        // (To ensure high quality thumbnails on high resolution devices, we recommend using max size using CSS)
        show_attachment_thumbnail: true,

// Enable if you want block the 'singleview' and instead to forward to the 'disclaimer' page
        // when the user tries to visit the 'singleview' page without first approving the disclaimer
        //disclaimer_redirect_tag: 'cus:disclaimer',
        //disclaimer_redirect_url: 'disclaimer.html',

}

---

### Functional Mock-Up Interface (FMI): What is it and why is it important?
- **URL:** https://modelon.com/functional-mock-up-interface-fmi/
- **Description:** The Functional Mockup Interface (FMI) standard is the industry open-source standard for exchanging models between modeling and simulation tools. Learn more.
- **Modified:** 2022-08-18



---

### Annual General Meeting 2021
- **URL:** https://modelon.com/agm2021/
- **Description:** ANNUAL GENERAL MEETING 2021



Shareholders in Modelon AB (publ) can register to vote in the Annual General Meeting to be held on Monday, May 31. Due to the...
- **Modified:** 2022-08-18

##### ANNUAL GENERAL MEETING 2021

Shareholders in Modelon AB (publ) can register to vote in the Annual General Meeting to be held on Monday, May 31. Due to the current pandemic restrictions, it is not possible to host a public meeting, and the annual meeting will instead be done with postal voting procedures according to §22 of the Swedish Act (2020:198) on temporary exceptions to facilitate the implementation of general meetings.

- Announcement and agenda for Annual General Meeting ([Swedish PDF](https://modelon.com/wp-content/uploads/2021/05/Kallelse-arsstamma-2021-Modelon.pdf))
- 2020 Annual Report ([Swedish PDF](https://modelon.com/wp-content/uploads/2021/05/Modelon-A%CC%8AR-2020-1.pdf))
- Postal voting form ([Swedish PDF](https://modelon.com/wp-content/uploads/2021/05/Formular-postrostning-210531.pdf))
- Proxy form ([Swedish PDF)](https://modelon.com/wp-content/uploads/2021/05/Fullmaktsformular-210531.pdf)

---

### EXTRA GENERAL MEETING
- **URL:** https://modelon.com/extra-general-meeting/
- **Description:** EXTRA GENERAL MEETING 2021-09-28



Shareholders in Modelon AB (publ) can register to vote in the Extra General Meeting to be held on Tuesday, September 28,...
- **Modified:** 2022-08-18

##### EXTRA GENERAL MEETING 2021-09-28

Shareholders in Modelon AB (publ) can register to vote in the Extra General Meeting to be held on Tuesday, September 28, 2021. Due to the current pandemic restrictions, it is not possible to host a public meeting, and the annual meeting will instead be done with postal voting procedures according to §22 of the Swedish Act (2020:198) on temporary exceptions to facilitate the implementation of general meetings.

- Announcement and agenda for Extra General Meeting ([Swedish PDF](https://modelon.com/wp-content/uploads/2021/09/Kallelse-extra-stamma-Modelon-210831.pdf), [English PDF](https://modelon.com/wp-content/uploads/2021/09/Notice-extra-general-meeting-Modelon-210831.pdf))
- Postal voting form ([Swedish PDF](https://modelon.com/wp-content/uploads/2021/09/Postal-voting-form-210928.pdf))
- Proxy form ([Swedish PDF)](https://modelon.com/wp-content/uploads/2021/09/Proxy-form-210928.pdf)

---

### Annual General Meeting 2022
- **URL:** https://modelon.com/agm2022/
- **Description:** Annual General Meeting 2022-05-18



Shareholders in Modelon AB (publ) can register to vote in the Annual General Meeting to be held on Wednesday, May 18. The...
- **Modified:** 2022-08-18

#### Annual General Meeting 2022-05-18

Shareholders in Modelon AB (publ) can register to vote in the Annual General Meeting to be held on Wednesday, May 18. The annual meeting will be done with postal voting procedures according to temporary regulations in the Swedish Company Act to facilitate the implementation of general meetings.

- Announcement and agenda for Annual General Meeting ([Swedish PDF](https://modelon.com/wp-content/uploads/2022/04/Kallelse-arsstamma-2022-Modelon.pdf))
- 2021 Annual Report ([Swedish PDF](https://modelon.com/wp-content/uploads/2022/03/Modelon-AR-2021-1.pdf))
- Postal voting form ([Swedish PDF](https://modelon.com/wp-content/uploads/2022/04/Formular-postrostning-220518.pdf))
- Proxy form ([Swedish PDF](https://modelon.com/wp-content/uploads/2022/04/Fullmaktsformular-220518.pdf))

---

### Leadership Copy
- **URL:** https://modelon.com/company/leadership-copy/
- **Modified:** 2022-08-04



---

### Legal & Trademarks
- **URL:** https://modelon.com/legal-trademarks/
- **Description:** Legal Notice



Responsible for Internet website contents of modelon.com (alias www.modelon.se and www.modelon.de):



Modelon ABIdeon...
- **Modified:** 2022-07-20

###### Legal Notice

Responsible for Internet website contents of [modelon.com](https://modelon.com/) (alias [www.modelon.se](http://www.modelon.se/) and [www.modelon.de](http://www.modelon.de/)):

**Modelon AB
**Ideon Science Park
Scheelevägen 17
SE-223 70 Lund, Sweden

Phone: [+46462862200](tel:+46462862200)
Email: [info@modelon.com](mailto:info@modelon.com)
Registration number: 556672-3010

Liability Notice: Despite carefully verifying the contents, we do not assume any liability for the contents of links to external websites. The owners of these external sites are solely responsible for the contents of these sites.

##### GENERAL CONDITIONS OF USE FOR THE MODELON WEBSITE

###### 1. Content

All offers are not-binding and without obligation. Parts of the pages or the complete publication including all offers and information might be extended, changed or partly or completely deleted by the author without separate announcement.

###### 2. Referrals and links

The author is not responsible for any contents linked or referred to from his pages – unless he has full knowledge of illegal contents and would be able to prevent the visitors of his site from viewing those pages. If any damage occurs by the use of information presented there, only the author of the respective pages might be liable, not the one who has linked to these pages. Furthermore the author is not liable for any postings or messages published by users of discussion boards, guestbooks or mailinglists provided on his page.

###### 3. Copyright

The author intended not to use any copyrighted material for the publication or, if not possible, to indicate the copyright of the respective object.

The copyright for any material created by the author is reserved. Any duplication or use of objects such as diagrams, sounds or texts in other electronic or printed publications is not permitted without the author’s agreement.

###### 4. Privacy policy

If the opportunity for the input of personal or business data (email addresses, name, addresses) is given, the input of these data takes place voluntarily. The use and payment of all offered services are permitted – if and so far technically possible and reasonable – without specification of any personal data or under specification of anonymizated data or an alias. View our Privacy Policy [here](https://modelon.com/privacy-policy/).

###### 5. Legal validity of this disclaimer

This disclaimer is to be regarded as part of the internet publication which you were referred from. If sections or individual terms of this statement are not legal or correct, the content or validity of the other parts remain uninfluenced by this fact.

##### TRADEMARKS

Modelon Impact™, OPTIMICA®, FMI Toolbox™, JModelica.org™, Air Conditioning Library®, Electric Power Library™, Engine Dynamics Library™, Fuel Cell Library™,  Heat Exchanger Library™, Hydraulics Library®, Hydro Power Library™, Liquid Cooling Library™, Pneumatics Library®, Thermal Power Library™, Vapor Cycle Library™, Vehicle Dynamics Library® are trademarks of Modelon AB.

Modelica® is a trademark of Modelica Association.

Dymola®, Isight®, Abaqus®, Reqtify®, ControlBuild®, CATIA®, SIMULIA® are trademarks of Dassault Systèmes.

MATLAB®, Simulink®, Simscape® are trademarks of The MathWorks, Inc.

SimulationX® is a trademark of ITI.

MapleSim®, Maple® are trademarks of MapleSoft.

TestWeaver®, Silver® are trademarks of QTronic.

CarMaker® is a trademark of IPG Automotive.

##### THIRD PARTY TERMS

Certain Modelon Licensed Programs (as defined in the license agreement applicable between Licensee and Modelon for the Licensed Programs) either may contain third party software components or may be third party software products licensed to Licensee to be used in connection with or within the Licensed Programs and subject to specific terms. The specific terms and conditions applicable to those third party software components or third party software products not developed by or for a Modelon Company are available on request. 

##### OPEN SOURCE SOFTWARE

The Licensed Programs may include open source software components. Whenever an attribution notice is required by the licensor, such open source software is identified in the Documentation and notices in the Licensed Programs themselves. The warranty and Support Services provided by Company under the License Agreement apply to all such open source software and are provided by Company and not by the original licensor. The original licensor of the open source software provides it on an “as is” basis and without any liability whatsoever to Licensee.

---

### Privacy Policy
- **URL:** https://modelon.com/privacy-policy/
- **Description:** WHO WE ARE



This privacy policy describes how Modelon AB (“Modelon” or “we”) collects and processes your personal data whenever you visit our website or...
- **Modified:** 2022-07-20

##### **WHO WE ARE**

This privacy policy describes how Modelon AB (“**Modelon**” or “**we**”) collects and processes your personal data whenever you visit our website or voluntarily submit your personal data to us, for example by registering for our newsletters or contact us through our website. Personal data means all types of information which can, directly or indirectly, be used to identify you as a living physical person, including online identifiers.

Contact information to Modelon is included at the end of this privacy policy.

##### **WHAT PERSONAL DATA WE COLLECT**

###### **Contact forms**

When you contact us from our contact forms (for example when you subscribe to our newsletter, request support or have other inquiries), we may (depending on context) collect and process the following personal data:

- your name;
- your e-mail address;
- your phone number;
- your country of residence;
- your employer, if you are representing a company that does or is interested in doing business with Modelon;
- your fields of interests (if submitted by you).

We may also ask for information that allows us to validate your inquiry, request specific information related to the reason you have contacted us, or contact you upon resolution of your inquiry for marketing or business purposes.

###### **Cookies**

If you have an account and you log in to this website, we will set a temporary cookie to determine if your browser accepts cookies. This cookie contains no personal data and is discarded when you close your browser.

When you log in, we will also set up several cookies to save your login information and your screen display choices. Login cookies last for two days, and screen options cookies last for a year. If you select “Remember Me”, your login will persist for two weeks. If you log out of your account, the login cookies will be removed.

If you do not want Modelon to place or read cookies on your equipment, you have the possibility of opting out of cookies by changing your browser settings, where you can choose which cookies that should be allowed, blocked or deleted. How to do so depends on what kind of browser you are using. Be aware that if you are using multiple browsers you will have to delete the cookies in all of them. As a user, you also need to be aware that the website might not function optimally if you delete or block cookies.

###### **Embedded content from other websites**

Articles on this site may include embedded content (e.g. videos, images, articles, etc.). Embedded content from other websites behaves in the exact same way as if you have visited the other website.

These websites may collect data about you, use cookies, embed additional third-party tracking, and monitor your interaction with that embedded content, including tracing your interaction with the embedded content if you have an account and are logged in to that website. Personal data collected by third party websites, which may include such things as location data or contact details, is governed by their privacy practices. We therefor encourage you to learn about the privacy practices of those third parties.

###### **Web server logs/analytics**

As is true of most websites, we gather certain information automatically through your use of the website. This information may include Internet protocol (IP) addresses, browser type, Internet service provider (ISP), referring or exit pages, the files viewed on the site (e.g., HTML pages, graphics, etc.), operating system and date/time stamp to analyze trends in the aggregate and administer the site. We use analytical software to help us understand this information.

###### **How we use personal data.**

Modelon will only use the personal data we collect as reasonably necessary for the following purposes:

- to ensure the functionality of our website, to analyze the use of our website and to improve and further develop the website;
- to inform our continued product development of our products and our services;
- to communicate with you from time to time in response to your requests for information or as may be relevant to your inquiries with us;
- to send marketing communications related to the products and services we provide if you have opted in for such communications;
- as required by applicable law or legal requirements pertaining to records retention or for internal administrative purposes; or
- as specifically authorized by you in writing.

###### **The legal grounds for our processing of your personal data**

The legal ground for our processing of your personal data when you visit our website is based on our legitimate interest to ensure the functionality, content and features of the website and to further improve and develop our website.

The legal ground for our processing of your personal data when you contact us through our website in a support matter or because you or your employer is interested in our products and services is based on our legitimate interest to respond to your questions and inform about our business, products and services.

The legal ground for our processing of your personal data if you subscribe to our newsletters is based on your documented consent.

###### **Who we share your data with**

As further described below, we will only share certain personal information with:

- our trusted third party service providers and vendors; and
- for legal reasons.

###### **Sharing with Third Party Service Providers and Vendors**

Occasionally, we enter into contracts with carefully selected third parties so that they can assist us in servicing you (for example, website development or access to advertising assets) or to assist us in our own marketing and advertising activities (including providing us with analytic information and search engine optimization services). Our contracts with such third parties prohibit them from using any of your personal information for any purpose beyond the purpose for which it was shared.

###### **Sharing within the context of a Legal Process**

If legally required to do so, or if we have a good faith belief that such disclosure is reasonably necessary, we may disclose your personal information to courts of law, law enforcement authorities and other relevant third parties, such as internet service providers, to conduct an investigation, respond to a third party or law enforcement subpoena or court order, bring legal action, prevent harm to others or pursue other relief when you or a third party are or may be:

- violating our terms and conditions of use;
- causing injury or other harm to, or otherwise violating the property or other legal rights, of us, other users, or third parties; or
- violating federal, state, local, or other applicable law.

This disclosure can include transferring your information to the U.S. and other countries outside the European Economic Area. To the extent we are legally permitted to do so, it is our policy to notify you in the event that we are required to provide your personal information to third parties in connection with a subpoena.

###### **How long we retain your data**

If you have registered to our newsletter, we will retain your personal data for as long as you do not opt out of receiving further communication from us. If you otherwise contact us from our contact forms, we will retain your personal data for as long as reasonably necessary in order to respond your inquiry and follow-up on any actions required by Modelon as a consequence of your inquiry.

If you register on our website, we will also store the personal information that you provide in your user profile for as long as your account remains active. You can see, edit, or delete your personal information at any time (except your username). Our website administrators can also see and edit that information if needed.

##### **HOW WE PROTECT YOUR DATA**

###### **How we protect your data**

We have put in place reasonable measures and appropriate procedures for implementing this...

---

### Products
- **URL:** https://modelon.com/products/
- **Modified:** 2022-07-15



---

### Modelica Introduction
- **URL:** https://modelon.com/support-learning/training/modelica-introduction/
- **Modified:** 2022-07-15



---

### Learning
- **URL:** https://modelon.com/support-learning/
- **Modified:** 2022-07-14



---

### Webinar: Integrating Carbon Capture & Storage Technology Using Simulation
- **URL:** https://modelon.com/webinar-integrating-carbon-capture-storage-technology-using-simulation/
- **Description:** https://youtu.be/ad2HsuQhL-o
- **Modified:** 2022-07-13



---

### Vehicle Electrification On-Demand Webinar
- **URL:** https://modelon.com/vehicle-electrification-webinar-on-demand/
- **Description:** https://youtu.be/m5S5lS-m6xI
- **Modified:** 2022-07-11



---

### Modelon Impact Introduction: On-Demand Webinar
- **URL:** https://modelon.com/modelon-impact-webinar-2020/
- **Description:** https://youtu.be/dj2ofvxHa5Y
- **Modified:** 2022-07-11



---

### Aircraft Engine Design – Multi-Point Design and Optimization: On-Demand Webinar
- **URL:** https://modelon.com/webinar-aircraft-engine-design/
- **Description:** https://www.youtube.com/embed/zX9a9v2FQDQ
- **Modified:** 2022-07-11



---

### Hydrogen Fuel Technologies: On-Demand Webinar
- **URL:** https://modelon.com/webinar-hydrogen-fuel-technologies/
- **Description:** https://www.youtube.com/watch?v=92Ad4mVI-dc&t=1s
- **Modified:** 2022-07-11



---

### Case Studies
- **URL:** https://modelon.com/support-learning/resources/case-studies/
- **Modified:** 2022-07-08



---

### Impact Category
- **URL:** https://modelon.com/impact-category/
- **Modified:** 2022-07-05



---

### Get PyFMI
- **URL:** https://modelon.com/get-pyfmi/
- **Modified:** 2022-07-05

Comments

This field is for validation purposes and should be left unchanged.

First Name(Required)

Last Name(Required)

Job Title(Required)

Company or Institution Name(Required)

Company or Institution Email Address(Required)

Phone(Required)

Industry

Select IndustryAerospaceAutomotive and MobilityEnergy and ProcessHVAC and RefrigerationIndustrialTelecommunicationsAcademiaOther

Country(Required)

Select CountryAfghanistanAlbaniaAlgeriaAmerican SamoaAndorraAngolaAntigua and BarbudaArgentinaArmeniaAustraliaAustriaAzerbaijanBahamasBahrainBangladeshBarbadosBelarusBelgiumBelizeBeninBermudaBhutanBoliviaBosnia and HerzegovinaBotswanaBrazilBruneiBulgariaBurkina FasoBurundiCambodiaCameroonCanadaCape VerdeCayman IslandsCentral African RepublicChadChileChinaColombiaComorosCongo, Democratic Republic of theCongo, Republic of theCosta RicaCôte d'IvoireCroatiaCubaCuraçaoCyprusCzech RepublicDenmarkDjiboutiDominicaDominican RepublicEast TimorEcuadorEgyptEl SalvadorEquatorial GuineaEritreaEstoniaEthiopiaFaroe IslandsFijiFinlandFranceFrench PolynesiaGabonGambiaGeorgiaGermanyGhanaGreeceGreenlandGrenadaGuamGuatemalaGuineaGuinea-BissauGuyanaHaitiHondurasHong KongHungaryIcelandIndiaIndonesiaIranIraqIrelandIsraelItalyJamaicaJapanJordanKazakhstanKenyaKiribatiNorth KoreaSouth KoreaKosovoKuwaitKyrgyzstanLaosLatviaLebanonLesothoLiberiaLibyaLiechtensteinLithuaniaLuxembourgMacedoniaMadagascarMalawiMalaysiaMaldivesMaliMaltaMarshall IslandsMauritaniaMauritiusMexicoMicronesiaMoldovaMonacoMongoliaMontenegroMoroccoMozambiqueMyanmarNamibiaNauruNepalNetherlandsNew ZealandNicaraguaNigerNigeriaNorthern Mariana IslandsNorwayOmanPakistanPalauPalestine, State ofPanamaPapua New GuineaParaguayPeruPhilippinesPolandPortugalPuerto RicoQatarRomaniaRussiaRwandaSaint Kitts and NevisSaint LuciaSaint Vincent and the GrenadinesSaint MartinSamoaSan MarinoSao Tome and PrincipeSaudi ArabiaSenegalSerbiaSeychellesSierra LeoneSingaporeSint MaartenSlovakiaSloveniaSolomon IslandsSomaliaSouth AfricaSpainSri LankaSudanSudan, SouthSurinameSwazilandSwedenSwitzerlandSyriaTaiwanTajikistanTanzaniaThailandTogoTongaTrinidad and TobagoTunisiaTurkeyTurkmenistanTuvaluUgandaUkraineUnited Arab EmiratesUnited KingdomUnited StatesUruguayUzbekistanVanuatuVatican CityVenezuelaVietnamVirgin Islands, BritishVirgin Islands, U.S.YemenZambiaZimbabwe

How can we help?

I consent to my submitted data being collected and stored.(Required)

[View Privacy Policy](/privacy-policy/)

Yes

I would like to subscribe to Modelon's quarterly e-newsletter.

Yes

---

### Senior Management
- **URL:** https://modelon.com/investor-relations/senior-management/
- **Modified:** 2021-10-26



---

### Library
- **URL:** https://modelon.com/library/
- **Modified:** 2021-10-22



---

### News & Blog
- **URL:** https://modelon.com/news-blog/
- **Modified:** 2021-10-12