Modelica Association Standards and Surrogate Modeling to Enable Multi-Fidelity Simulations


  • Olle Lindqvist
  • Robert Hällqvist
  • Raghu Chaitanya Munjulury



FMI, SSP, CFD, System Identification, Neural Networks, Co-simulation


System simulations are particularly useful when analyzing complex systems. Simulations are often cheaper and safer than physical tests of the actual system(s) of interest. Models can be additionally created for systems that do not exist to find solutions that are impossible to analyze experimentally in early life-cycle stages. Models used in system simulations require appropriate input data to give results with the required fidelity and, in the end, credibility. Integration is often challenging as each system commonly constitutes contributions from several engineering domains. Relying on relevant open standards for information exchange is seen as a means of mitigation. The results of the presented work encompass a developed methodology that allows Computational Fluid Dynamics (CFD) results to be integrated into a simulator using system identification and open standards. Reduced Order Models (ROMs) are generated based on results from a CFD analysis. These ROMs are coupled to lumped parameter system simulation models through the mechanisms of the System Structure and Parameterization (SSP) and Functional Mock-up Interface (FMI) standards. In addition, several important factors to consider before using the proposed methodology are presented. These include the intended use of the ROM, knowing the flow inside the system, what resources are available, and any potential licensing issues.