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.

OpenModelica is currently the most complete opensource Modelica-and FMI-based modeling, simulation, optimization, and model-based 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-ore architectures. The environment is based on Modelica and uses an extended version of Modelica for its implementation. This overview paper intends to give an up-to-date brief description of the capabilities of the system, and the main vision behind its development.

Nonlinear model predictive control (NMPC) has become increasingly important for today's control engineers during the last decade. In order to apply NMPC a nonlinear optimal control problem (NOCP) must be solved which in general needs high computational effort.State-of-the-art solution algorithms are based on multiple shooting or collocation algorithms, which are required to solve the underlying dynamic model formulation. This paper describes a general discretization scheme applied to the dynamic model description which can be further concretized to reproduce the multiple shooting or collocation approach. Furthermore, this approach can be refined to represent a total collocation method in order to solve the underlying NOCP much more efficiently. Further speedup of optimization has been achieved by parallelizing the calculation of model specific parts (e.g. constraints, Jacobians, etc.) and is presented in the coming sections.The corresponding discretized optimization problem has been solved by the interior optimizer Ipopt. The proposed parallelized algorithms have been tested on different applications. As industrial relevant application an optimal control of a Diesel-Electric power train has been investigated. The modeling and problem description has been done in Optimica and Modelica. The simulation has been performed using OpenModelica. Speedup curves for parallel execution are presented.

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New multi-core CPU and GPU architectures promise high computational power at a low cost if suitable computational algorithms can be developed. However, parallel programming for such architectures is usually non-portable, low-level and error-prone. To make the computational power of new multi-core architectures more easily available to Modelica modelers, we have developed the ParModelica algorithmic language extension to the high-level Modelica modeling language, together with a prototype implementation in the OpenModelica framework. This enables the Modelica modeler to express parallel algorithms directly at the Modelica language level. The generated code is portable between several multi-core architectures since it is based on the OpenCL programming model. The implementation has been evaluated on a benchmark suite containing models with matrix multiplication, Eigen value computation, and stationary heat conduction. Good speedups were obtained for large problem sizes on both multi-core CPUs and GPUs. To our knowledge, this is the first high-performing portable explicit parallel programming extension to Modelica.