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OpenModelica
OpenModelica
OpenModelica
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OpenModelica
DeveloperOpen Source Modelica Consortium (OSMC)
Stable release
1.22.2 / 21 February 2024; 20 months ago (2024-02-21)
Repositorygithub.com/OpenModelica/OpenModelica
Written inC, C++, MetaModelica
Operating systemLinux, Windows and OS X
TypeDynamic simulation and optimization
LicenseOSMC Public License, EPL, GPL (free software)
Websitewww.openmodelica.org

OpenModelica[1][2] is a free and open source environment based on the Modelica modeling language for modeling, simulating, optimizing and analyzing complex dynamic systems. This software is actively developed by Open Source Modelica Consortium,[3] a non-profit, non-governmental organization. The Open Source Modelica Consortium is run as a project of RISE SICS East AB in collaboration with Linköping University.

OpenModelica is used in academic and industrial environments. Industrial applications include the use of OpenModelica along with proprietary software in the fields of power plant optimization,[4] automotive[5] and water treatment.[6]

Tools and Applications

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OpenModelica Compiler (OMC)

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OpenModelica Compiler (OMC) is a Modelica compiler, translating Modelica to C code, with a symbol table containing definitions of classes, functions, and variables. Such definitions can be predefined, user-defined, or obtained from libraries. The compiler also includes a Modelica interpreter for interactive usage and constant expression evaluation. The subsystem also includes facilities for building simulation executables linked with selected numerical ODE or DAE solvers. The OMC is written in MetaModelica,[7] a unified equation-based semantical and mathematical modeling language and is bootstrapped.

OpenModelica Connection Editor (OMEdit)

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OpenModelica Connection Editor[8][9] is an open source graphical user interface for creating, editing and simulating Modelica models in textual and graphical modes. OMEdit communicates with OMC through an interactive API, requests model information and creates models/connection diagrams based on the Modelica annotations. The implementation is based on C++ and the Qt library.

OpenModelica Shell (OMShell)

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OpenModelica Shell (OMShell) is an interactive command-line interface that parses and interprets commands and Modelica expressions for evaluation, simulation, plotting, etc. The session handler also contains simple history facilities, and completion of file names and certain identifiers in commands.

OpenModelica Notebook (OMNotebook)

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OpenModelica Notebook (OMNotebook), is a light-weight Mathematica-style editor for Modelica that implements interactive WYSIWYG realization of Literate Programming, a form of programming where programs are integrated with documentation in the same document.

OMNotebook is primarily used for teaching and allows to mix hierarchically structured text with cells containing Modelica models and expressions. These can be evaluated, simulated and plotted with the results displayed directly in the OMNotebook.

OpenModelica Python Interface (OMPython)

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OMPython is a Python interface enabling users to access the modeling and simulation capabilities of OpenModelica from Python. It uses CORBA (omniORB) or ZEROMQ to communicate with the OpenModelica scripting API.

OpenModelica Matlab Interface (OMMatlab)

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OMMatlab is a Matlab interface that provides access the modeling and simulation capabilities of OpenModelica from matlab. It uses ZEROMQ to communicate with the OpenModelica compiler API.

Modelica Development Tooling (MDT)

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MDT is an Eclipse plugin that integrates the OpenModelica compiler with Eclipse. It provides an editor for advanced text based model editing with code assistance. MDT interacts with the OpenModelica Compiler through an existing CORBA based API and is used primarily in the development of the OpenModelica compiler.

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

OpenModelica

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OpenModelica is a free and open-source integrated environment based on the for modeling, simulating, optimizing, and analyzing complex dynamic systems. It supports equation-based, object-oriented modeling of physical systems across domains such as mechanical, electrical, thermal, and , enabling the creation of reusable component libraries and hierarchical models. Intended for both industrial and academic use, OpenModelica provides tools for interactive simulation, debugging, 3D visualization, and code generation for embedded systems, making it suitable for , , and product development. Development of OpenModelica began in 1997 at as an extension of earlier on equation-based languages, with active open-source work resuming in 2002 and the formation of the (OSMC) in December 2007 to coordinate efforts. The OSMC, a non-profit with over 30 organizational members including universities, institutes, and companies, as well as numerous individual members, maintains the project under an , releasing major versions periodically—such as 1.6 in 2010 with graphical editing support, 1.16 in 2020 featuring improved frontend performance and 4.0 compatibility, and 1.25 in 2025 with enhanced graphical user interface support and 4.1 compatibility. As the first complete open-source implementation of and the (FMI) standards, it includes key components like the OpenModelica (OMC) for and , OMEdit for graphical modeling, and scripting interfaces in languages such as Python and Julia. Notable for its scalability, OpenModelica can handle large-scale models with hundreds of thousands of equations using sparse solvers and supports advanced features like parallel code generation for multi-core processors and FMI-based co-simulation for integrating models from other tools. The environment runs on multiple platforms including Windows, macOS, and , with ongoing development focusing on enhanced library coverage, web-based interfaces, and integration with UML/SysML for system workflows. Community resources, including interactive tutorials and a web-based workbench, facilitate learning and collaboration, positioning OpenModelica as a key tool in advancing practices.

Overview

Definition and Purpose

OpenModelica is a comprehensive, free, open-source implementation of the language, providing an environment for equation-based, object-oriented of complex physical systems. , as the underlying language, facilitates the description of multidomain dynamical models through declarative equations rather than procedural code. The primary purpose of OpenModelica is to enable interactive modeling, compilation, , optimization, and of Modelica-based models, serving industrial, research, and educational applications with a fully open that avoids proprietary dependencies. Its short-term goals focus on delivering an efficient computational environment supporting numerical algorithms for tasks such as design and optimization, while long-term objectives include functioning as a complete of the language to aid in its standardization and evolution. In scope, OpenModelica covers most features of the Modelica Standard Library versions 3.2.3, 4.0.0, and 4.1.0, including extensions for (FMI) co-simulation and model reduction techniques to enhance and efficiency in large-scale simulations. As the first open-source Modelica environment, it was initiated specifically to provide a , supporting interactive execution of most Modelica language elements and fostering into advanced simulation methods.

Key Features

OpenModelica supports interactive execution of most Modelica expressions, algorithms, and functions through session handlers such as OMShell, enabling , debugging, and evaluation without full compilation. This interactive capability facilitates iterative model development by allowing users to test components in real-time, including simulation setup and result analysis directly within the environment. The compilation process in OpenModelica translates Modelica models into efficient C code, which is then linked with runtime libraries and numerical solvers like DASSL or CVODE for simulation across various platforms. It includes support for parallel code generation using , enabling multi-core execution to accelerate simulations of complex systems. OpenModelica integrates the (MSL) versions 3.2.3, 4.0.0, and 4.1.0, with automatic dependency resolution via annotations and tools for generating library coverage reports to assess compliance and testing completeness. Porting guides and utilities assist in adapting libraries from other environments to OpenModelica, ensuring broad compatibility. For extensibility, OpenModelica provides FMI 2.0 import and export capabilities for model exchange and co-simulation with external tools, supporting seamless integration in multi-physics workflows. It also handles hybrid modeling by managing continuous dynamics alongside discrete events, such as in cyber-physical systems. Performance enhancements include of systems through manipulation, which optimizes large-scale models by reducing and identifying dependencies. tools, including interactive tracing and variable inspection, aid in extensive models, as noted by industrial users for time savings in development. As of October 2025, OpenModelica version 1.25.5 features an advanced frontend with improved accuracy and auto-completion support in integrated editors, enhancing usability for complex model authoring.

Background

Modelica Language Fundamentals

is a freely available, object-oriented, declarative designed for describing complex physical systems across multiple domains, such as mechanical, electrical, thermal, and hydraulic systems. It employs an equation-based approach where models are defined through mathematical relationships rather than procedural code, allowing for high-level, reusable descriptions of system behavior. This language facilitates the creation of libraries of components that can be composed hierarchically to simulate large-scale, heterogeneous systems. Central to Modelica are its key concepts of components, which are implemented as classes containing equations that govern their behavior and connectors that define interfaces for . These classes support object-oriented principles including for extending base models, encapsulation to hide internal details, and reusability through , enabling efficient development of extensible libraries. The handles continuous-time dynamics via differential and algebraic equations, discrete-time events through when-clauses, and hybrid systems by integrating both paradigms, thus supporting simulations of cyber-physical systems with mixed behaviors. A hallmark of Modelica is its acausal modeling paradigm, in which equations are specified declaratively without prescribing the order of computation or signal flow directions. This allows solvers to simultaneously resolve the entire , often through symbolic manipulation to reduce complexity, optimize structure, and generate efficient code for numerical . The language includes standard elements such as multi-dimensional arrays for vectorized operations, pure and impure functions for computational tasks, algorithmic sections for imperative code within models, and interfaces to external C code for integrating legacy or performance-critical routines. Modelica has evolved significantly since its initial version 1.0 released in September 1997, progressing through version 1.4 in December 2000, version 2.0 in January 2002, version 3.2 in March 2010 (adding stream support), and version 3.3 revision 1 in 2011 (introducing operators). Subsequent releases include version 3.4 in July 2017 (enhanced expandable connectors and annotations), version 3.5 in February 2021 (advanced and improved synchronous features), version 3.6 in March 2023 (refinements to clocked modeling and better support for parallel execution), and version 3.7-dev as of November 2025 (ongoing developments in formal semantics and integration with modern computing paradigms). To illustrate the declarative style, consider a simple spring-mass-damper system where the position ss, vv, mm, damping coefficient cc, and spring constant kk satisfy the equations: m⋅der(v)=−c⋅v−k⋅s,der(s)=v.\begin{align*} m \cdot \mathrm{der}(v) &= -c \cdot v - k \cdot s, \\ \mathrm{der}(s) &= v. \end{align*} These equations define the relationships without specifying how to solve for derivatives, leaving that to the simulation tool.

Development History

OpenModelica's development originated in the late 1990s at the Programming Environment Laboratory (PELAB) at in , led by Peter Fritzson, with initial work beginning in 1997 on a frontend for a core subset of 1.0, resulting in the first flattening frontend release in 1998. This effort positioned OpenModelica as the pioneering open-source , focusing on interactive execution of expressions, algorithms, and functions to support research, teaching, and industrial applications. Early development emphasized building a comprehensive environment for model-based development, with the project pausing briefly after 1998 before resuming around 2002, leading to the first public prototypes. The first official open-source release, version 1.3.1, occurred in November 2005 under the BSD license, introducing support for hybrid simulation and parsing of the full Modelica 2.2 specification. Subsequent milestones included version 1.6.0 in November 2010, which achieved full flattening of the Modelica Standard Library (MSL) 3.1 (excluding Media/Fluid) and introduced the OMEdit graphical editor for model creation and . Version 1.9.1 in January 2014 added a dynamic model , dynamic optimization capabilities using , and support for FMI 2.0 model exchange, enhancing interoperability. By version 1.16.0 in October 2020, the new frontend became the default, supporting 100% of MSL 3.2.3 and introducing parallel evaluation for improved performance. The latest stable release as of November 2025 is version 1.25.5 from October 2025, with development toward 1.26.0 focusing on GUI enhancements and further library coverage. Institutional support has been pivotal, with funding from European Union projects such as EUROSYSLIB (2007–2010), which advanced multi-domain modeling libraries and real-time simulation integration; MODELISAR (2008–2011), which drove the development of the Functional Mock-up Interface (FMI) standard for model exchange; and OPENPROD (2009–2012), an ITEA2 initiative that boosted collaborative model-driven systems development. These efforts, along with later projects like MODRIO (2013–2016), fostered collaboration among universities, research institutions, and industry partners, enabling OpenModelica's growth into a robust toolset. OpenModelica evolved from a basic interactive in its early years to a full integrated environment by 2010, incorporating graphical , , and optimization features. Governance shifted in December 2007 with the formation of the Open Source Modelica Consortium (OSMC), a non-profit initially comprising seven members, which coordinates development, licensing under OSMC-PL, and community contributions. By 2025, OpenModelica has attracted hundreds of contributors, reflecting sustained research in formal semantics, optimization techniques, and extensions like ModelicaML for requirements-driven , with recent updates in 2025 enhancing UML/SysML-to- transformations.

Core Architecture

OpenModelica Compiler (OMC)

The OpenModelica Compiler (OMC) serves as the central engine for translating source code into executable C code suitable for simulation, enabling the processing of complex physical system models defined in the acausal, equation-based language. It operates through a multi-stage that handles , semantic verification, symbolic transformations, and code synthesis, ultimately producing simulation-ready artifacts. This compilation process ensures that hierarchical, object-oriented models are resolved into flat, solvable systems of differential-algebraic equations (DAEs) while preserving the declarative nature of the input. The frontend stage begins with parsing, where the Modelica source is tokenized and converted into an abstract syntax tree (AST) represented in MetaModelica, a functional extension of Modelica used for the compiler's own implementation. This is followed by scoping and semantic analysis to detect errors such as type mismatches or undefined variables, culminating in instantiation, which expands class hierarchies and replaces modifiers to generate an internal SCode representation and then a DAE. A key algorithm here is flattening, which expands inherited and component structures into a single set of scalar equations and variables, eliminating the model hierarchy for subsequent processing; for instance, it handles 100% of models in the Modelica Standard Library (MSL) 3.2.3, 4.0.0, and 4.0.1 as of November 2025. The New Frontend Architecture (NFA), default since version 1.17.0 and further refined in version 1.24.0 (released October 2025), enhances this stage with significant speed improvements for large models (e.g., reducing compilation time for a 10,000-body solar system model from 314.8 seconds to 34.12 seconds, approximately 9-fold) and better error handling for typing loops via instance trees and phased processing. In the middle-end, symbolic manipulation optimizes the DAE through preprocessing, such as constant propagation and alias elimination, followed by equation sorting for causalization. Core algorithms include matching, which pairs unknowns with equations using methods like PFPlusExt to form a block lower triangular structure, and tearing, which addresses algebraic loops by selecting iteration variables and residual equations for iterative solution, with options like omcTearing or minimal tearing to minimize computational cost. Index reduction tackles high-index DAEs (beyond index 1) by techniques such as dummy derivatives or the , differentiating algebraic constraints to yield a semi-explicit form solvable by standard integrators; this is crucial for systems like electrical circuits where implicit dependencies arise. For example, consider a simple first-order model:

model SimpleDecay Real x(start=1, fixed=true); parameter Real a = 1; [equation](/page/Equation) der(x) = -a * x; end SimpleDecay;

model SimpleDecay Real x(start=1, fixed=true); parameter Real a = 1; [equation](/page/Equation) der(x) = -a * x; end SimpleDecay;

Flattening yields the DAE form x˙+ax=0\dot{x} + a x = 0, which is already index 1 with no algebraic loops, so matching directly assigns the to xx as the , requiring no tearing. In more complex cases with loops (e.g., 0=f(y,z)0 = f(y, z), 0=g(z,x)0 = g(z, x)), tearing might select zz as the tear variable, reformulating as z=h(x,y)z = h(x, y) iteratively solved within the block, reducing the size and enabling efficient . The backend performs code generation and optimization, translating the sorted DAE into code (default) or C++ via modules like partitioning for independent subsystems and simplification for nonlinear solvers. Outputs include standalone source files, compiled executables for direct simulation, and Functional Mock-up Units (FMUs) compliant with FMI for model exchange and co-simulation, generated using commands like buildModelFMU(). Optimizations such as tearing-based sparsity enhance solver efficiency. Performance features encompass parallelization through for multi-threaded evaluation and independent system solving (e.g., via --jacobianThreads), supporting large models with over 3.2 million equations compiled in minutes as of November ; MPI integration is available for distributed simulations in experimental modes. Debugging aids include equation printing and DAE dumps via flags like --showDaeGeneration or --tearingdump, allowing inspection of transformation states. OMC's implementation in MetaModelica facilitates self-extension, as the can process its own source for custom transformations, with plans to migrate full MetaModelica support to the NFA for greater scalability.

Runtime and Simulation Environment

The in OpenModelica generates C code from the flattened and optimized model representation, which is then compiled into an executable binary and linked with a containing utility functions and numerical solvers for integration and event handling. This C runtime forms the core execution framework, enabling efficient simulation of differential-algebraic equations (DAEs) by interfacing with external libraries for linear algebra operations, such as and BLAS, to handle matrix computations during solving. OpenModelica supports a range of solvers tailored to different problem types, with DASSL serving as the default for implicit (BDF) integration of stiff DAEs, offering adaptive order from 1 to 5 and compatibility with both dense and sparse linear solvers like KLU. The IDA solver, part of the SUNDIALS suite, provides similar BDF capabilities with enhanced options for nonlinear iterations and sparse handling, making it suitable for large-scale models. For non-stiff systems and explicit methods, fixed-step Runge-Kutta solvers are available through the GBODE integrator, which supports orders up to 14 and multi-rate partitioning for . Hybrid system support includes event detection via zero-crossing functions, allowing seamless transitions between continuous and discrete behaviors. The simulation workflow begins with model instantiation and compilation to C code, followed by linking into a binary executable that runs with selectable step methods—variable-step for adaptive accuracy in DASSL/IDA or fixed-step for deterministic Euler/Runge-Kutta simulations. Execution outputs results in formats like CSV for tabular data export or feeds into plotting tools such as OMPlot for visualization of time-series variables. Extensions enhance the runtime for advanced scenarios, including OMSimulator for FMI-based distributed co-simulation, which orchestrates multiple FMUs over ordinary or modeling (TLM) connections to enable large-scale virtual prototyping. Model reduction techniques, such as those integrated via dynamic optimization, allow parameter estimation and problems to streamline complex models during runtime. As of November 2025, prototypes for GPU acceleration using and have been introduced to parallelize simulations for large-scale systems, improving performance in compute-intensive integrations. Debugging features support interactive stepping through algorithmic code via the Algorithmic Debugger, launched prior to simulation, and real-time variable monitoring to inspect states and outputs during execution. Event handling in OpenModelica relies on an iterative algorithm for zero-crossings, particularly for when statements that trigger discrete updates based on conditions like when {condition} then ... end when. Zero-crossing functions, derived from these conditions (e.g., z=sign(y−53)z = \text{sign}(y - 53) for y>53y > 53), are monitored during integration; a crossing from negative to positive (or vice versa, depending on direction) prompts root-finding to locate the exact event time tet_e in the interval [ti,ti+1][t_i, t_{i+1}]. The algorithm proceeds as follows:
  1. During (e.g., via DASSL/IDA), evaluate zero-crossing functions at tentative steps.
  2. If a zero is detected, invoke root-finding (e.g., bisection or solver-integrated methods like DASRT) to refine tet_e, disabling the crossing post-detection by setting it to -1 to avoid solver issues.
  3. At tet_e, fire the event: update discrete variables from when-clause assignments, resolve the restart problem for consistent initial values using the DAE solver.
  4. Re-check all zero-crossings; if new ones arise, iterate until no further events are triggered (event queue empty), ensuring stability.
This process handles synchronous events and prevents chattering, with options like -noRootFinding to disable for testing, and has been enhanced with modern SUNDIALS integrations since early versions.

User Interfaces and Tools

Graphical Editors

OpenModelica provides graphical user interfaces to facilitate model creation, editing, and , with OMEdit serving as the primary (IDE) for visual modeling. OMEdit enables users to build and modify models through a drag-and-drop interface, supporting diagram-based editing where components from libraries are placed and connected to form hierarchical models. It integrates seamlessly with the OpenModelica Compiler for and , offering a unified workspace that combines graphical, textual, and tools. OMEdit's diagram editor allows for intuitive model construction using shapes, lines, and connectors to represent physical systems, such as mechanical or electrical components. The Libraries Browser displays available classes in a , enabling users to filter and drag components like resistors or capacitors directly onto the canvas. A with and autocompletion supports direct code modifications alongside graphical views. For setup, users configure parameters including stop time, tolerances, and solver options (e.g., DASSL) via a dedicated dialog, with batch simulations possible for parameter sweeps. Additionally, OMEdit supports 3D visualization of models using , activated during animated simulations to render dynamic scenes. In 2025, OMEdit introduced a new leveraging a (DFD) frontend, developed under contracts like DFD Bosch and DFD LBL, to enhance usability through improved data flow representation and integration with systems such as . This update streamlines model editing workflows by providing better frontend parsing and visual feedback. OMEdit also facilitates parameter tuning post-simulation via the Variables Browser, allowing real-time adjustments and re-runs without rebuilding the model. A typical workflow in OMEdit involves selecting components from the Libraries Browser, connecting them in the diagram view, setting initial parameters, and running simulations to generate plots or animations. For instance, to build a simple electrical circuit like an RC series circuit: (1) Drag a (Resistor from Modelica.Electrical.Analog.Basic) and (Capacitor from the same library) onto the diagram; (2) Connect the positive pin of the to the , the to the , and the back to the source's negative pin using connector lines; (3) Set resistance to 1 kΩ and to 1 μF in the component parameters; (4) Configure simulation with a stop time of 0.01 seconds and default tolerances; (5) Simulate to plot voltage across the , observing the charging curve. This process highlights OMEdit's efficiency in iterative model development. Complementing OMEdit, OMNotebook offers an interactive notebook environment for literate modeling, where users combine Modelica code, equations, documentation, and simulation results in a single, executable document. It supports hierarchical sections with editing, allowing real-time evaluation of code cells (via Shift+Return) and inline plotting of results using tools like Ptplot. While primarily text-oriented, OMNotebook integrates with graphical outputs and is ideal for tutorials or exploratory modeling, such as embedding equations and plots in educational notebooks like DrModelica.

Interactive and Scripting Interfaces

OpenModelica provides interactive and scripting interfaces that enable users to execute Modelica commands, evaluate expressions, and automate workflows through command-line and notebook-based environments. These tools facilitate , , and without relying on graphical interfaces, allowing for efficient model development and analysis directly in a terminal or structured format. OMShell serves as the primary interactive shell for OpenModelica, functioning as a to the OpenModelica Compiler (OMC). It parses and interprets Modelica expressions, loads models, runs s, and supports plotting and optimization tasks interactively. Key features include command history for revisiting previous inputs, auto-completion for file names and identifiers, and variable inspection via functions like val(variable, time) to retrieve values at specific times. Users can execute commands, such as system("cat filename") for file operations, and handle multi-line inputs by loading scripts or pasting . OMShell also incorporates scripting elements, such as for-loops, while-loops, and if-statements, enabling procedural within sessions. For instance, a basic workflow might involve loading the Modelica with loadModel(Modelica, {"3.2.3"}), simulating a model like simulate(Modelica.Mechanics.MultiBody.Examples.Elementary.Pendulum, stopTime=10), and visualizing results with plot(x). These capabilities make OMShell suitable for prototyping models, interactively equations by testing expressions on-the-fly, and performing of simulations or parameter sweeps. A unique aspect of OMShell is its use of CORBA for remote access, allowing it to operate in client-server mode with options like --interactive=corba for distributed execution across machines. This facilitates collaborative or scenarios. Additionally, OMShell integrates with Python through the OMPython library, enabling hybrid workflows where Python scripts can invoke OMShell commands for Modelica-specific tasks, such as linearization or FMI export, enhancing automation in mixed-language environments. OMNotebook extends scripting capabilities into an interactive format, supporting with a combination of text, code cells, and embedded visualizations. It allows users to evaluate expressions and simulations directly within cells, with features like , command completion (via Ctrl+Space), and scalable plotting for results. Scripting in OMNotebook automates sequences of operations, such as running multiple simulations and generating reports, while integrating narrative explanations for documentation. Common use cases include educational tutorials for teaching concepts and interactive exploration of model behaviors through cell-based evaluation (e.g., Shift+Enter to execute). Example usage mirrors OMShell commands but within notebook cells, such as embedding simulate(HelloWorld, outputFormat="csv", stopTime=4) followed by plotAll() to display outputs. This environment is particularly valuable for in a structured, reproducible manner, with support for formatting and image insertion to enhance readability.

Development and Integration Tools

Modelica Development Tooling (MDT)

The Modelica Development Tooling (MDT) is a suite of Eclipse-based plugins designed to facilitate advanced development of projects within the OpenModelica ecosystem. It integrates the OpenModelica Compiler (OMC) directly into the (IDE), enabling developers to edit, compile, and simulate models and libraries in a unified workflow. Primarily targeted at tool developers and those working on medium- to large-scale projects, MDT provides essential features for handling complex, multi-file codebases, including support for both standard and MetaModelica. Key components of MDT include a syntax-aware editor, a project builder, and a tailored for algorithmic Modelica code. The editor offers , automatic indentation (via Ctrl+I), and real-time syntax checking with error markers for quick identification of issues during coding. is available in two modes: dot-notation suggestions for classes and packages, and popup assistance for function arguments, drawing directly from OMC's parsing capabilities to suggest valid identifiers and signatures, such as Real sin(SI.Angle u). The project builder automates compilation using OMC commands, such as generating Makefiles for building entire projects or individual models. The supports line-based breakpoints, stepping through execution, and inspection of variables, particularly useful for debugging algorithmic sections like when-equations or functions, where developers can pause on specific lines to examine evaluations. MDT's features extend to library browsing and project organization, allowing navigation through class hierarchies and the via Eclipse's outline view. It integrates seamlessly with OMC for on-the-fly compilation and launches from within the IDE, using the MDT Console for interactive commands like plotting results or running . While full refactoring tools remain under development as Eclipse extensions, MDT leverages the IDE's native capabilities for basic code navigation, such as "Go to Definition" (Ctrl+click) and hover tooltips for variable information. is handled through project configurations, where developers can include external libraries by setting paths to OMC's load directories. The typical workflow in MDT supports multi-file project handling by organizing code into Eclipse projects and packages, with wizards for creating new models, functions, or packages while enforcing Modelica restrictions (e.g., no partial classes in certain contexts). Version control integration occurs via Eclipse's built-in support for systems like SVN and , allowing developers to commit changes to repositories directly from the IDE. Automated testing can be incorporated by scripting OMC commands in project Makefiles or using the console for batch simulations, though MDT itself focuses on development rather than dedicated test frameworks. MDT was introduced around 2006, with the last updates occurring around 2019. For example, setting up a workspace for a custom involves first configuring the OPENMODELICAHOME to point to the OMC installation, then launching and switching to the Modelica perspective. Create a new Modelica via File > New > Modelica , which generates a basic structure with a package.mo file. Add custom library files by right-clicking the project and selecting New > Modelica Class, specifying the type (e.g., package or model). Configure build settings in the project properties to include library paths and custom OMC flags, such as -d=initialization for diagnostic output. Build the project using > Build All, which invokes OMC to compile the library, and verify via the Problems view for any errors before simulating a test model from the MDT Console with commands like simulate(MyLibrary.TestModel). This process enables iterative development of reusable components within a controlled IDE environment.

External Language Interfaces

OpenModelica provides external language interfaces to facilitate integration with popular programming environments, enabling users to script, manipulate, and analyze models without relying solely on native tools. These interfaces primarily consist of OMPython, OMMatlab, and OMJulia, which allow seamless interaction with the OpenModelica (OMC) for tasks such as model loading, parameter adjustment, execution, and result extraction. Both leverage the OMC's to support hybrid workflows, where domain-specific modeling in combines with computational capabilities in Python, , or Julia. OMPython serves as the primary Python API for OpenModelica, offering a free, open-source interface for interactive scripting of simulations, model manipulation, and post-simulation analysis. It enables users to leverage Python's ecosystem, including libraries like for numerical processing of simulation results and for visualization. Key features include loading models or libraries, setting simulation parameters, running simulations with customizable options, and extracting variables such as continuous states or parameters as arrays for further analysis. For instance, the ModelicaSystem class simplifies workflows by encapsulating model instantiation, simulation, and result retrieval in a single object. OMPython also supports (FMI) interoperability by allowing calls to export models as FMUs or import external FMUs into simulations, promoting tool chaining in multi-physics environments. Implemented using for asynchronous communication between Python and the OMC server, OMPython replaces earlier CORBA-based approaches with a more efficient, portable protocol that supports non-blocking operations and cross-platform deployment. This setup allows embedding OMPython in environments like Jupyter notebooks, where users can interactively develop and execute code alongside Python scripts—for example, in educational tools or web-based modeling platforms. A basic usage example initializes a session and loads the Modelica Standard Library as follows:

python

from OMPython import OMCSessionZMQ omc = OMCSessionZMQ() omc.sendExpression('loadModel([Modelica](/page/Modelica))')

from OMPython import OMCSessionZMQ omc = OMCSessionZMQ() omc.sendExpression('loadModel([Modelica](/page/Modelica))')

Advanced interactions, such as simulating a model and plotting results, build on this foundation, with full details available in the official documentation. OMMatlab provides an analogous interface for users, enabling calls to OpenModelica from scripts to create hybrid workflows that blend 's declarative modeling with 's matrix-oriented computations. It supports core operations like loading models via the ModelicaSystem constructor (e.g., specifying a .mo file and class name), setting parameters through arrays of strings, executing s with options for output files or flags, and retrieving quantities such as variables or parameters. Like OMPython, OMMatlab facilitates FMI support by with OMC commands for FMU and integration, allowing models to interoperate with / s. OMMatlab relies on for its connection to the OMC, ensuring high-performance messaging similar to OMPython, and requires an OpenModelica installation for operation. This interface is particularly useful for engineers transitioning from toolboxes to , as it maintains familiar scripting patterns while accessing OpenModelica's advanced compilation and simulation capabilities. Detailed usage, including test commands and enhanced features, is documented in the . OMJulia provides a similar interface for Julia users, offering a free, open-source for interactive scripting and model manipulation within the Julia environment. It enables leveraging Julia's capabilities for tasks like execution, parameter optimization, and result analysis. Key features include session management, model loading, running, and data extraction, with support for ZeroMQ-based communication to the OMC server. OMJulia facilitates hybrid workflows, such as combining models with Julia packages for scientific computing. A basic example initializes a session as follows:

julia

using OMJulia omc = OMJulia.OMCSession() omc.sendExpression("loadModel([Modelica](/page/Modelica))")

using OMJulia omc = OMJulia.OMCSession() omc.sendExpression("loadModel([Modelica](/page/Modelica))")

Advanced usage includes simulating models and processing results, with documentation providing full API details. As of OpenModelica 1.25.5 (October 2025), OMJulia supports integration with the latest features.

Applications and Extensions

Modeling and Simulation Use Cases

OpenModelica facilitates of multi-physics systems across diverse domains, including mechanical systems such as , electrical circuits, thermal management in HVAC, and chemical processes involving . These capabilities leverage the Modelica Standard Library (MSL), which provides over 1,400 component models for integrating physical phenomena without domain-specific recoding. Key case studies demonstrate OpenModelica's versatility in practical simulations. For , the Modelica.Mechanics.MultiBody library enables 3D modeling of mechanical systems, such as ground vehicles incorporating mass, aerodynamic drag, and for analyzing handling and stability. In power systems, co-simulation via the (FMI) supports integration of grid-connected inverters and renewable sources, allowing distributed simulation of large-scale electrical networks with tools like OMSimulator. The benefits of OpenModelica in these applications stem from its reusable component libraries, which significantly reduce development time by enabling modular assembly of complex models from pre-validated elements. Additionally, the acausal modeling paradigm inherent to allows for flexible what-if analyses, where changes to system parameters or configurations can be explored without rewriting equations, facilitating rapid iteration in design phases. Industrial adoption highlights OpenModelica's impact in real-world scenarios. In the automotive sector, and employ OpenModelica for engine and vehicle modeling to optimize fuel consumption prior to manufacturing. For aerospace applications, OpenModelica supports modeling of through compatible Modelica libraries that include and control surfaces. In energy applications, OpenModelica simulates optimization using the WindPowerPlants library, which integrates turbine, generator, and control models to evaluate power output under varying wind conditions. A unique aspect of OpenModelica is its support for the MSL's extensive model repository, encompassing over 1,400 components for multi-domain integration. As of 2025, emerging applications include AI-integrated modeling via ModelicaML, which extends UML for generating executable Modelica code, combined with tools like ModelicaGym for interfacing simulations with reinforcement learning environments such as OpenAI Gym. As of OpenModelica 1.25 (2025), enhanced FMI 3.0 support improves co-simulation for multi-domain applications like renewable energy grids. A representative workflow in OpenModelica involves simulating a using the library. First, assemble the model by connecting components such as pipes, valves, and the itself from the MSL, defining parameters like properties and flow rates via the Modelica.Media package. Next, configure initial conditions and settings in the OpenModelica Compiler (OMC), such as stop time and integration method (e.g., DASSL for stiff systems). Run the to generate results, visualizing outputs like profiles over time using integrated plotting tools. Finally, perform sensitivity analysis by parameter sweeps—altering variables like inlet or flow rates—and re- to assess impacts on efficiency, all within the runtime environment for iterative refinement.

Optimization and Analysis Tools

OpenModelica provides specialized tools for model optimization and , enabling engineers to refine complex models for improved performance and insight. Central to these capabilities is OMOptim, a designed for optimization tasks such as , configuration, and dynamic control optimization. OMOptim integrates seamlessly with the OpenModelica , allowing users to define optimization problems directly within models, including multi-objective functions and constraints evaluated at simulation endpoints. It supports meta-heuristic algorithms like SPEA2Adapt for sweeps. Gradient-based methods such as (SQP) and integration with solvers like for are available through OpenModelica's built-in dynamic optimization features. For advanced dynamic optimization, particularly trajectory problems, OpenModelica employs built-in capabilities that formulate problems (OCPs) using Modelica annotations or the Optimica language extensions. These tools transform the model into a (NLP) problem, minimizing an objective such as a functional while adhering to differential constraints. The typical is: min⁡u J(u)=∫t0tfL(x,u) dt\min_{u} \, J(u) = \int_{t_0}^{t_f} L(x,u) \, dt subject to the dynamic equations x˙=f(x,u),x(t0)=x0,\dot{x} = f(x,u), \quad x(t_0) = x_0, along with path and terminal constraints, solved via collocation methods with the IPOPT solver. This approach is particularly useful for applications like energy-efficient process control, where optimal input trajectories are derived over a time horizon. Analysis features in OpenModelica include structural singularity detection and generation to diagnose and resolve issues in differential-algebraic (DAE) systems. The performs index reduction and identifies structural singularities by analyzing equation-variable matching, with debug flags enabling dumps of the conversion from analytical to structural forms. , constructed during , reveal causal relationships and cyclic dependencies, aiding in multimode model analysis where conditional blocks form block-triangular structures. Model reduction is supported through -based techniques in the , prioritizing high-impact reductions like fast state elimination to accelerate simulations without significant accuracy loss. Sensitivity analysis is facilitated by the OMSens module, which computes parameter sensitivities using forward methods with the Sundials/IDA solver or algorithmic differentiation (AD) for efficient computation in optimization loops. AD techniques, implemented via tools like ADModelica, differentiate Modelica equations symbolically to support applications such as parameter fitting and . These analyses can identify influential parameters in large-scale models, enhancing robustness. Integration of these tools occurs through user interfaces like OMEdit for GUI-based setup and OMPython for scripting automated workflows, producing outputs such as reduced-order models for faster subsequent simulations. For instance, a reduced model from equation-based reduction can be exported and reused in design iterations, maintaining structural integrity while cutting computational demands by orders of magnitude in select cases.

Community and Licensing

Open Source Modelica Consortium (OSMC)

The Open Source Modelica Consortium (OSMC) was established on December 4, 2007, in , , as a non-profit, dedicated to coordinating the long-term development, funding, and standardization of OpenModelica, an open-source implementation of the language. Initially founded with seven organizational members, OSMC has grown to support industrial and academic usage of OpenModelica by managing resources, promoting collaboration, and ensuring the tool's evolution aligns with standards. OSMC's organizational structure is led by an as the highest governing body, where only organizational members hold voting rights, and a board of 5 to 11 members that oversees strategic decisions. The board includes a chairperson, vice-chairperson, director (elected for four years), and vice-director (elected for two years), with representation balanced across non-profits, small/medium enterprises, large companies, industry, and academia to prevent dominance by any single entity or country (limited to 45% of organizational members). As of 2025, Francesco Casella of Politecnico di Milano serves as director, Peter Fritzson of as vice-director, Rüdiger Franke of ABB AG as chairman, and Oliver Lenord of GmbH as vice-chairman, alongside other members from institutions like EDF and VTT. By January 2025, OSMC comprised 55 organizational members, including academic institutions such as , Politecnico di Milano, and , as well as industry partners like ABB, Robert Bosch GmbH, and EDF. Individual members, numbering over 100, further contribute expertise from diverse fields. OpenModelica's source code is distributed under the GNU Affero General Public License version 3 (AGPLv3), ensuring free access and modification while requiring derivative works to remain . The OSMC Public License provides additional flexibility, including modes like OSMC-Internal-EPL and OSMC-External-EPL that permit integration with proprietary code for members at Level 1 or 2, alongside binary distributions that mandate source availability to support commercial use without compromising openness. OSMC's key activities include hosting annual events such as the OpenModelica Workshop to foster community collaboration, funding targeted enhancements through Directly Funded Development (DFD) contracts where up to 30% of budgets support maintenance, testing, and documentation, and maintaining overall quality via robust testing infrastructure. Membership fees—ranging from €7,300 for large companies to gold/silver sponsorships starting at €10,000/€20,000—along with in-kind contributions (minimum equivalent to five times the annual fee), enable these efforts. Contributions to OpenModelica are governed by clear policies requiring transfer to OSMC upon , with perpetual licenses granted back to contributors; guidelines are detailed in the project's to ensure compatibility and . Bug tracking occurs through issues, prioritizing fixes under Maintenance and Support Agreements for subscribers (minimum €6,000 annually), while nightly builds for Windows and Linux platforms facilitate and testing to uphold reliability.

Contributions and Future Development

OpenModelica's development relies on contributions from numerous individuals, primarily affiliated with Linköping University and international partners through the Open Source Modelica Consortium (OSMC). Key figures include Adrian Pop and Martin Sjölund, who have led advancements in the compiler framework and debugging tools, respectively. The project's contributor list encompasses software developers, designers, and documentation specialists from academic and industrial backgrounds, fostering a collaborative ecosystem. Contributions are facilitated through a structured process centered on the project's repository, where pull requests are the preferred method for submitting changes. Developers are encouraged to review the CONTRIBUTING.md guidelines before proposing modifications, ensuring alignment with project standards. Additionally, mailing lists serve as forums for discussions on usage, implementation, and mutual advice, with around 100-200 messages annually. Common areas of contribution include enhancements to the compiler, expansion of standard libraries, and improvements to user . Looking ahead, OpenModelica's roadmap emphasizes achieving full support for Modelica 4.0, building on ongoing work in meta-programming and language extensions. Research efforts are directed toward integrating and AI for model optimization, as demonstrated in frameworks combining Modelica simulations with ML models. Scalability remains a key challenge, with recent advancements enabling simulation of models exceeding 10 million equations through array-preserving compilation techniques. The project is also expanding compliance with FMI 3.0, with a subset already implemented for co-simulation and full support in development. Active development is maintained through regular releases and , with major versions issued periodically to incorporate fixes and features. The 2025 OpenModelica Workshop and participation in international Modelica Conferences highlight ongoing collaboration and knowledge sharing among users and developers. Future goals include further research in to enhance model reliability, alongside efforts for ModelicaML to support advanced modeling paradigms.

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