Bernhard Thiele
German Aerospace Center (DLR), Institute for System Dynamics and Control, Wessling, Germany
Martin Otter
German Aerospace Center (DLR), Institute for System Dynamics and Control, Wessling, Germany
Sven Erik Matsson
Dassault Systèmes AB, Ideon Science Park, Lund, Sweden
Ladda ner artikel
http://dx.doi.org/10.3384/ecp14096381Ingår i: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden
Linköping Electronic Conference Proceedings 96:40, s. 381-393
Publicerad: 2014-03-10
ISBN: 978-91-7519-380-9
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
The demand to ever increase realism and scope of models routinely exceeds the currently available computing power and thus requires thoughts on improving simulation efficiency. This is especially true for real-time simulations; where fixed timing constraints do not allow to just “wait a bit longer”.
This paper presents a new approach in Modelica that allows a modeler to separate a model into different partitions for which individual solvers can be assigned. In effect; this allows to use multi-rate and multi-method time integration schemes that can contribute to improve the efficiency of a (real-time) simulation.
The first part of the paper discusses basic consideration relating to modular (real-)time integration. Afterwards; the implementation of a convenient Modelica library for the partitioning of physical models is briefly described. Finally; the presented library is used to partition a detailed six degree of freedom robot model for modular simulation. The simulation performance of that partitioned model is compared to the simulation performance achieved by using “conventional” global solvers.
Multi-rate / multi-method time integration; simulation; clocked discretized continuous-time partitions.
[1] Dennis S. Bernstein. The treatment of inputs in real-time digital simulation. SIMULATION, 33(2):65–68, 1979.
[2] Martin Busch. Zur effizienten Kopplung von Simulationsprogrammen. PhD thesis, Universität Kassel, 2012.
[3] Francois Cellier and Ernesto Kofman. Continuous System Simulation. Springer Science + Business Media, Inc., 2006.
[4] Hilding. Elmqvist, Sven Erik Mattsson, and Hans Olsson. New Methods for Hardware-in-the-Loop-Simulation of Stiff Models. In Martin Otter, Hilding Elmqvist, and Peter Fritzson, editors, 2nd Int. Modelica Conference, Oberpfaffenhofen, Germany, March 18–19 2002.
[5] Hilding Elmqvist, Martin Otter, and Françoise E. Cellier. Inline Integration: A New Mixed Symbolic/Numeric Approach for Solving Differential-Algebraic Equation Systems. In European Simulation Multiconference, 1995.
[6] R. Kübler and W. Schiehlen. Modular simulation in multibody system dynamics. Multibody System Dynamics, 4(2-3):107–127, 2000.
[7] Jim Ledin. Simulation Engineering. CMP Books, Lawrence, Kansas 66046, first edition, 2001.
[8] Modelica Association. Modelica—A Unified Object-Oriented Language for Systems Modeling v3.3. Standard Specification, May 2012. available at http://www.modelica.org/.
[9] Stefan Nowack and Georg Feil. Reduction of time delays in Runge-Kutta integration methods. SIMULATION, 48(1):24–27, 1987.
[10] Martin Otter, Bernhard Thiele, and Hilding Elmqvist. A Library for Synchronous Control Systems in Modelica. In Martin Otter and Dirk Zimmer, editors, 9th Int. Modelica Conference, Munich, Germany, September 2012.
[11] Anton Schiela and Hans Olsson. Mixed-mode Integration for Real-time Simulation. In Modelica Workshop 2000 Proceedings, pages 69–75, Lund, Sweden, 2000.
[12] Tom Schierz and Martin Arnold. Stabilized overlapping modular time integration of coupled differential-algebraic equations. Applied Numerical Mathematics, 62(10):1491 –
1502, 2012.
