The paper presents a contribution to a Modelica benchmark suite. Basic ideas for a tool independent benchmark suite based on Python scripting along with models for testing the performance of Modelica compilers regarding large systems of equation are given. The automation of running the benchmark suite is demonstrated followed by a selection of benchmark results to determine the current limits of Modelica tools and how they scale for an increasing number of equations.
This paper presents a survey on matching algorithms which are required to translate Modelica Models. Several implementations of matching algorithms are benchmarked on a set of physical models from mechanical systems in ODE and DAE representation. The major part of algorithms is based on the Augmenting Paths Method and one algorithm is based on the Push-Relabel Method. The algorithms are implemented in the programming language C and MetaModelica. In addition two cheap matching algorithms are used to jump-start the advanced matching process.
This paper presents a graph theoretical interpretation of the well-known O(n) algorithm for Multibody systems. It enables Modelica compilers to solve for the unknown accelerations of a Multibody model without the need of inverting a dense mass matrix which would require O(n 3 ) operations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.