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-compliance 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 objectoriented 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.

This paper presents current work on our Modelica Compiler framework in Julia: OpenModelica.jl. 1 We provide a brief overview of this novel framework and its features, and we also present the latest addition to the possible backend options. We target ModelingToolkit.jl (MTK), a framework for symbolic-numerical computation and scientific machine learning. We evaluated the performance of our new backend using the ScalableTestsuite, a benchmark suite for Modelica Compilers. In our experiment, we demonstrate that MTK can be used as a backend with competitive simulation performance. In addition, using the scientific machine learning features of the Modeling toolkit, we were able to approximate models in the Scal-ableTestsuite using surrogate techniques and how such techniques can be used to accelerate the solving of nonlinear algebraic loops during tearing.Based on our experiments, we propose using this new framework to automatically generate surrogate components of a Modelica model during the simulation to increase performance. The experimental work presented here provides one of the first investigations concerning the integration of the symbolic-numerical abilities of Julia within a Modelica tool.

Many algorithms related to Modelica-based simulations heavily rely on the efficient provision of Jacobian matrices. Besides the accuracy of the derivative information, the performance of the derivative evaluation is also of great interest, since it can have a large share in the total simulation time. In this paper, we propose two complementary approaches basing on identification of constant parts and parallelization to accelerate Jacobian evaluation. Furthermore, the implementations of these techniques in the open-source Modelica tool OpenModelica are discussed. The gained speedup in Jacobian evaluation is demonstrated on benchmark models of the Scal-ableTestSuite.