Automating Dynamic Decoupling in Object-Oriented Modelling and Simulation Tools

Alessandro Vittorio Papadopoulos
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy

Alberto Leva
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Italy

Ladda ner artikel

Ingår i: Proceedings of the 5th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools; April 19; University of Nottingham; Nottingham; UK

Linköping Electronic Conference Proceedings 84:5, s. 37-44

Visa mer +

Publicerad: 2013-03-27

ISBN: 978-91-7519-621-3 (print)

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


This manuscript presents a technique that allows Equationbased Object-Oriented Modelling Tools (EOOMT) to exploit Dynamic Decoupling (DD) for partitioning a complex model into “weakly coupled” submodels. This enhances simulation efficiency; and is naturally keen to parallel integration or co-simulation. After giving an overview of the problem and of related work; we propose a method to automate DD by means of a novel structural analysis of the system – called “cycle analysis” – and of a mixed-mode integration method. Also; some considerations are exposed on how the presented technique can be integrated in EOOMT; considering as representative example a Modelica translator. Simulation tests demonstrate the technique; and the realised implementation is released as free software.


dynamic decoupling; model partitioning; efficient simulation code generation


[1] A. Antoulas. Approximation of large-scale dynamical systems; volume 6 of Advances in Design And Control. SIAM; 2005.

[2] A. Bartolini; A. Leva; and C. Maffezzoni. A process simulation environment based on visual programming and dynamic decoupling. Simulation; 71(3):183–193; 1998.

[3] F. Casella; A. Leva; and C.Maffezzoni. Dynamic simulation of a condensation plate column by dynamic decoupling. In Proc. EUROSIM ’98; Espoo 1998; pages 368–374; 1998.

[4] F. Casella and C. Maffezzoni. Exploiting weak interactions in object-oriented modeling. Simulation News Europe; 22:8–10; 1998.

[5] F. Cellier and E. Kofman. Continuous system simulation. Springer; 2006.

[6] J. Chen and S.-M. Kang. Model-order reduction of nonlinear MEMS devices through arclength-based Karhunen- Loeve decomposition. In The 2001 IEEE Int. Symp. on Circuits and Systems; volume 3; pages 457–460; 2001.

[7] J. Chen; S.-M. Kang; J. Zou; C. Liu; and J. Schutt-Aine. Reduced-order modeling of weakly nonlinear MEMS devices with Taylor-series expansion and Arnoldi approach. J. of Microelectromechanical Systems; 13(3):441– 451; 2004.

[8] P. Fritzson. Principles of Object-Oriented Modeling and Simulation with Modelica 2.1. Wiley; 2003.

[9] L. Goldberg and G. Ann. Efficient algorithms for listing combinatorial structures; volume 5. Cambridge Univ Pr; 2009.

[10] M. Innocent; P. Wambacq; S. Donnay; H. Tilmans; W. Sansen; and H. De Man. An analytic volterra-seriesbased model for a mems variable capacitor. IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems; 22(2):124–131; 2003.

[11] S. Lall; J. Marsden; and S. Glavaski. A subspace approach to balanced truncation for model reduction of nonlinear control systems. Int. J. of Robust and Nonlinear Control; 12:519–535; 2002.

[12] L. Mikelsons and T. Brandt. Symbolic model reduction for interval-valued scenarios. In ASME Conf. Proc.; volume 49002; pages 263–272. ASME; 2009.

[13] L.Mikelsons and T. Brandt. Generation of continuously adjustable vehicle models using symbolic reduction methods. Multibody System Dynamics; 26:153–173; 2011.

[14] A. V. Papadopoulos; J. Åkesson; F. Casella; and A. Leva. Automatic partitioning and simulation of weakly coupled systems. Technical report; Politecnico di Milano; 2013.

[15] J. R. Phillips. Projection frameworks for model reduction of weakly nonlinear systems. In Proc. of the 37th Annual Design Automation Conf.; DAC ’00; pages 184–189; New York; NY; USA; 2000. ACM.

[16] J. Scherpen. Balancing for nonlinear systems. Systems & Control Letters; 21(2):143–153; 1993.

[17] A. Schiela and H. Olsson. Mixed-mode integration for realtime simulation. In Modelica Workshop 2000 Proc.; pages 69–75; 2000.

[18] M. Sjölund; R. Braun; P. Fritzson; and P. Krus. Towards efficient distributed simulation in modelica using transmission line modeling. In 3rd Int. workshop on Equation-Based Object-Oriented Modeling Languages and Tools; pages 71– 80; 2010.

[19] R. Tarjan. Depth-first search and linear graph algorithms. SIAM J. on Computing; 1(2):146–160; 1971.

[20] R. Tarjan. Enumeration of the elementary circuits of a directed graph. SIAM J. on Computing; 2(3):211–216; 1972.

Citeringar i Crossref