TY - Generic
T1 - Using SPARK as a solver for Modelica
T2 - Proc. of the 3rd SimBuild Conference
Y1 - 2008/08//
A1 - Michael Wetter
A1 - Philip Haves
A1 - Michael A. Moshier
A1 - Edward F. Sowell
AB - Modelica is an object-oriented acausal modeling language that is well positioned to become a de-facto standard for expressing models of complex physical systems. To simulate a model expressed in Modelica, it needs to be translated into executable code. For generating run-time efficient code, such a translation needs to employ algebraic formula manipulations. As the SPARK solver has been shown to be competitive for generating such code but currently cannot be used with the Modelica language, we report in this paper how SPARK's symbolic and numerical algorithms can be implemented in OpenModelica, an open-source implementation of a Modelica modeling and simulation environment. We also report benchmark results that show that for our air flow network simulation benchmark, the SPARK solver is competitive with Dymola, which is believed to provide the best solver for Modelica.
JF - Proc. of the 3rd SimBuild Conference
CY - Berkeley, CA, USA
UR - http://www.ibpsa.us/simbuild2008/technical_sessions/SB08-DOC-TS03-1-Wetter.pdf
U2 - LBNL-634E
ER -
TY - CONF
T1 - Using SPARK as a Solver for Modelica
T2 - SimBuild 2008
Y1 - 2008/07//
A1 - Michael Wetter
A1 - Philip Haves
A1 - Michael A. Moshier
A1 - Edward F. Sowell
JF - SimBuild 2008
CY - Berkeley, CA, USA
ER -
TY - Generic
T1 - Graph-theoretic Methods in Simulation Using SPARK
T2 - High Performance Computing Symposium of the Advanced Simulation Technologies Conference
Y1 - 2004/04//
A1 - Edward F. Sowell
A1 - Michael A. Moshier
A1 - Philip Haves
AB - This paper deals with simulation modeling of nonlinear, deterministic, continuous systems. It describes how the Simulation Problem Analysis and Research Kernel (SPARK) uses the mathematical graph both to describe models of such systems, and to solve the embodied differential-algebraic equation systems (DAEs). Problems are described declaratively rather than algorithmically, with atomic objects representing individual equations and macro objects representing larger programming entities (submodels) in a smooth hierarchy. Internally, in a preprocessing step, graphs are used to represent the problem at the level of equations and variables rather than procedural, multi-equation blocks. Benefits obtained include models that are without predefined input and output sets, enhancing modeling flexibility and code reusability, and relieving the modeler from manual algorithm development. Moreover, graph algorithms are used for problem decomposition and reduction, greatly reducing solution time for wide classes of problems. After describing the methodology the paper presents results of benchmark tests that quantify performance advantages relative to conventional methods. In a somewhat contrived nonlinear example we show O performance as opposed
JF - High Performance Computing Symposium of the Advanced Simulation Technologies Conference
T3 - Society for Modeling Simulation International
CY - Arlington, VA
ER -
TY - Generic
T1 - Graph-Theoretic Methods in Simulation Using SPARK
T2 - High Performance Computing Symposium of the Advanced Simulation Technologies Conference (Society for Modeling Simulation International)
Y1 - 2004/04//
A1 - Edward F. Sowell
A1 - Michael A. Moshier
A1 - Philip Haves
A1 - Dimitri Curtil
JF - High Performance Computing Symposium of the Advanced Simulation Technologies Conference (Society for Modeling Simulation International)
CY - Arlington, Virginia, USA
ER -