Why More R&D Teams Are Choosing Modelica Over Traditional Simulation Tools

What is Modelica?

Modelica is a modeling language designed to simulate how real-world machines and systems behave. Unlike traditional programming, which requires step-by-step instructions, Modelica lets you write the equations that govern physical behavior—like Newton’s law of cooling, Ohm’s law, or Bernoulli’s equation.

This makes it uniquely suited to modeling complex, multi-domain systems that change over time.

Why More Teams Are Making the Shift

R&D teams in industries like automotive, aerospace, and renewable energy are under pressure to simulate more complex, multi-domain systems—before physical prototypes are built.

This shift toward model-based systems engineering (MBSE) is driving demand for tools that are:

• Physics-driven, not just signal-based
• Reusable and composable
• Compatible with automation pipelines

Modelica is uniquely positioned to meet these needs, and that’s why it’s gaining traction among many engineering groups.

What can you do with it?

Modelica shines when you’re dealing with systems that span multiple physical domains—like thermal, electrical, mechanical, and fluid systems all working together.

Here are a few real-world examples:

• Heating and cooling processes in industrial systems
• Electric vehicles with tightly coupled electrical, thermal, and mechanical subsystems
• Solar power systems that integrate physical dynamics with demand forecasts

These are problems where traditional block-based tools or hand-coded simulations quickly become fragile or inefficient. Modelica handles them natively and scalably.

Why not just use Python, Mathematica, or something similar?

Python or Mathematica are incredible general-purpose languages—I use them too. But when it comes to modeling physical systems, Modelica is purpose-built.

Here’s what Modelica offers that others don't:

• Automatic enforcement of physics—conservation of mass, energy, etc.
• Declarative modeling—write equations in any order; the solver figures it out
• Hybrid modeling—handle continuous and discrete events seamlessly

Python or Mathematica are great for automation, post-processing, and deployment—but Modelica is where you model the system itself.

What I like about Modelica

• I can build my own modular libraries, with clean icons and documentation—great for clients, teams, and teaching.
• I’m not locked into one tool—Modelica works with Wolfram System Modeler, Dymola, OpenModelica and others.
• I can export FMUs (Functional Mock-up Units) and use them in other simulation tools.

This gives me flexibility and power to solve client problems across domains and platforms.

How I use Modelica in my work

• Create synthetic data to train machine learning models
• Build interactive dashboards that clients use for design, analysis, and training
• Design and validate control systems in simulation before deployment
• Test new system concepts for performance and economic feasibility
• Compare real-world sensor data with simulations for early fault detection and predictive maintenance

I pair this with Wolfram Language for symbolic analysis and Python for automation, reporting, and deployments.

Thinking about using Modelica?

Modelica isn’t just a modeling language—it’s a better way to reason about real systems.

If your team is building complex, simulation-heavy technology, and you're tired of toolchain limitations, it might be time to upgrade your workflow.