Nowadays, industrial products are getting increasingly complex, and time-to-market is significantly shorter. Modeling and simulation tools for cyber-physical systems need to keep up with the increased complexity. This paper presents OpenModelica.jl, a modular and extensible Modelica compiler framework in Julia targeting ModelingToolkit.jl and supporting Variable Structured Systems. We extended the Modelica language with three new operators to support continuous-time mode-switching and reconfiguration via recompilation at runtime. Therefore, our compiler supports the Modelica language and variable structure systems via the aforementioned extensions. To our knowledge, there are no other Modelica tools available that support both standard Modelica and variable structure systems. We evaluated our framework using a standardized benchmark suite, in terms of simulation, compilation and recompilation performance. The results concerning compilation and simulation time performance were compared with the results of running the existing OpenModelica compiler with the same set of models. A custom benchmark was devised to estimate the cost in terms of recompilation when simulating variable structure systems. The performance experiments showed that OpenModelica.jl is currently about four times slower in terms of compilation time when compiling a transmission line model with tens of thousands of equations and variables. The difference in simulation performance between the two compilers was negligable. Furthermore, the impact of recompilation during the simulation was usually small compared with the simulation time for long simulations. The results are promising for a prototype, and we outline approaches to further improve both compilation and simulation performance as future research. 

OpenModelica is a unique large-scale integrated open-source Modelica- and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica-UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.

Recently the Julia language has become an option for scientific computing. As of 2020, efforts exist to provide libraries that emulate the equation-based modeling features provided by Modelica or otherwise provide such functionality in Julia. The issue with these approaches is that investment in standardization and libraries would be lost unless standard-complacency is guaranteed. We believe that it is possible to combine features from both by implementing such a compiler in Julia. We argue that this approach would open additional opportunities. One such being the handling of variable structure systems (VSS) within the framework of a Modelica standard-compliant compiler. The other being a proposed compiler architecture reminiscent of LLVM for equation-based object-oriented languages. Using the OpenModelica Compiler as a baseline, we verified the fidelity of our implementation by simulating a selected set of models. While there are performance penalties, we argue that improvements to the frontend would mitigate these issues.

The equation-based object-oriented Modelica language allows easy composition of models from components. It is very easy to create very large parametrized models using component arrays of models. Current open-source and commercial Modelica tools can with ease handle models with a hundred thousand equations and a thousand states. However, when the system size goes above half a million (or more) equations the tools begin to have problems with scalability. This paper presents the new frontend of the OpenModelica compiler, designed with scalability in mind. The new OpenModelica frontend can handle much larger systems than the current one with better time and memory performance. The new frontend was validated against large models from the ScalableTestSuite library and Modelica Standard Library, with good results.

The need for integrating system modeling with advanced tool capabilities is becoming increasingly pronounced. For example, a set of simulation experiments may give rise to new data that are used to systematically construct a series of new models, e.g. for further simulation and design optimization. Such combined symbolic-numeric capabilities have been pioneered by dynamically typed interpreted languages such as Lisp and Mathematica. Such capabilities are also relevant for advanced modeling and simulation applications but lacking in the standard Modelica language. Therefore, this is a topic of long-running design discussions in the Modelica Design group. One contribution in this direction is MetaModelica, that has been developed to extend Modelica with symbolic operations and advanced data structures, while preserving safe engineering practices through static type checking and a compilation-based efficient implementation. Another recent effort is Modia, implemented using the Julia macro mechanism, making it dynamically typed but also adding new capabilities. The Julia language has appeared rather recently and has expanded into a large and fast-growing ecosystem. It is dynamically typed, provides both symbolic and numeric operations, advanced data structures, and has a just-intime compilation-based efficient implementation. Despite independent developments there are surprisingly many similarities between Julia and MetaModelica. This paper presents MetaModelica and its environment as a large case study, together with a short comparison to Julia. Since Julia may be important for the future Modelica, some integration options between Modelica tools and Julia are also discussed, including a possible approach for implementing MetaModelica (and OpenModelica) in Julia.

We are investigating ways of introducing just-in-time compilation in a standard-compliant Modelica compiler, the Open-Modelica compiler (OMC). The main motivations are enabling extensions to support dynamically varying model structure, faster compilation, and faster recompilation of models after changes. We are investigating two approaches.

The first approach is to adapt the low-level OpenModelica intermediate representation (IR) before code generation to be compatible with LLVM. In that way we can avoid generating intermediate C-code and instead generate LLVM IR in memory for just-in-time compilation (JIT).

The second approach is to translate OMC itself written in MetaModelica to Julia, and thereby gain access to the JIT capabilities of LLVM. Another benefit of the second approach is the access to the Julia ecosystem, including a rich set of libraries for numerical computing.

We have done a preliminary investigation of both approaches, with measurements on a selected sample of algorithms, and discovered that compilation-time of generated Julia code is slower compared to generating LLVM IR directly. We conclude that providing a standard-compliant Modelica compiler which supports a dynamically varying model structure is feasible and possible, and we believe that such a compiler can be provided by using Julia or MetaModelica.