On using model approximation techniques for better understanding of models implemented in Modelica

Modelica enables rapid development of detailed models of heterogeneous and complex systems. However, resulting models are as complicated as reality itself and therefore it may be hard to identify causes for model behavior or verify that model behaves correctly. A traditional engineering approach is to use intuition and experience to identify important parts of the model with the highest impact on model behavior for specific scenario. Numerous model order reduction and simplification techniques (i.e., metrics used by these methods) have been developed to automatically estimate important parts of the models for a certain scenario and thus alleviate reliance on subjective factors, i.e., intuition and past experience. In this paper are discussed model order reduction and simplification techniques (e.g., metrics used by these techniques for rankings of elements) which are applicable to wide range of Modelica models built from already available libraries. Modelica models are translated to set of differential-algebraic equations and for the latter there are numerous tools for model order reduction already available. However, these tools are not designed for helping users understand the model’s behavior and the reduced model may be hard to understand by the user because the structure of the original model is lost. Hierarchical decomposition of the model must be presereved and if the model is developed with a graphical schematics then elements (nodes) of the schematics must be ranked. Therefore we adapted energy-based metrics used in ranking of bond-graphs’ elements to much more losely defined Modelica’s schematics, so they can be used complementary with ranking methods that work with equations.