Improved explicit co-simulation methods incorporating relaxation techniques
- 69 Downloads
Abstract
Explicit co-simulation is an increasingly popular technique for solver coupling of multidisciplinary systems in a distributed environment. Compared to implicit approaches, explicit coupling schemes are simpler to implement, since explicit methods allow the synchronization of different subdomains at user-defined discrete communication points without a reinitialization of the subsystem solvers. However, a typical disadvantage of explicit schemes is the reduced stability behavior, which is introduced by errors resulting from the approximation of the coupling variables. In the manuscript at hand, improved explicit co-simulation methods incorporating relaxation techniques are considered for the case that the coupling of the subsystems is realized by constitutive laws. We discuss three decomposition techniques, namely force/force coupling, force/displacement coupling and displacement/displacement coupling. Two novel explicit coupling approaches are analyzed. Within the first approach, classical co-simulation methods based on Lagrange polynomials for extrapolating the coupling variables are modified by incorporating a straightforward relaxation approach using only the current and previous coupling variables. Applying the second approach, predicted coupling variables are generated by using a constant or linear acceleration approach for the coupling bodies. With the predicted, current and previous coupling variables, special approximation polynomials are generated also using a relaxation approach. The numerical stability and convergence characteristics will be analyzed for two explicit co-simulation approaches. It will be shown that stability and convergence can be notably improved by a proper choice of the relaxation parameters. The stabilized co-simulation techniques will be verified using several numerical examples.
Keywords
Explicit co-simulation Relaxation techniques Numerical stability Convergence behaviorNotes
Acknowledgements
This work is supported by National Nature Science Foundation of China (No.11902237), China Postdoctoral Science Foundation (No.2018M643622).
References
- 1.Peiret, A., González, F., Kövecses, J., Teichmann, M.: Multibody system dynamics interface modelling for stable multirate co-simulation of multiphysics systems. Mech. Mach. Theory 127, 52–72 (2018). https://doi.org/10.1016/j.mechmachtheory.2018.04.016 CrossRefGoogle Scholar
- 2.Van der Auweraer, H., Anthonis, J., De Bruyne, S., Leuridan, J.: Virtual engineering at work: the challenges for designing mechatronic products. Eng. Comput. 29, 389–408 (2013). https://doi.org/10.1007/s00366-012-0286-6 CrossRefGoogle Scholar
- 3.Liboni, G., Deantoni, J., Portaluri, A., Quaglia, D., Simone, R.D.: Beyond time-triggered co-simulation of cyber-physical systems for performance and accuracy improvements. In: Proceedings of the Rapido’18 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, pp. 1–8. ACM, Manchester (2018)Google Scholar
- 4.Neema, H., Gohl, J., Lattmann, Z., Sztipanovits, J., Karsai, G., Neema, S., Bapty, T., Batteh, J., Tummescheit, H., Sureshkumar, C.: Model-based integration platform for FMI co-simulation and heterogeneous simulations of cyber-physical systems. In: Proceedings of the 10th International Modelica Conference, March 10–12 2014, Lund, Sweden, pp. 235–245. Linköping University Electronic Press (2014)Google Scholar
- 5.Baumann, P., Mikelsons, L., Baumann, M.: Comparison of methods for integrating real-time systems in a co-simulation framework. In: IUTAM Symposium on Co-Simulation and Solver Coupling. Darmstadt, Germany (2017)Google Scholar
- 6.Stettinger, G., Horn, M., Benedikt, M., Zehetner, J.: Model-based coupling approach for non-iterative real-time co-simulation. In: European Control Conference (ECC) 2014, pp. 2084–2089 (2014)Google Scholar
- 7.Schaäfer, M., Yigit, S., Heck, M.: Implicit partitioned fluid-structure interaction coupling. In: ASME 2006 Pressure Vessels and Piping/ICPVT-11 Conference, pp. 105–114. American Society of Mechanical Engineers (2006)Google Scholar
- 8.Andersson, H., Nordin, P., Borrvall, T., Simonsson, K., Hilding, D., Schill, M., Krus, P., Leidermark, D.: A co-simulation method for system-level simulation of fluid-structure couplings in hydraulic percussion units. Eng. Comput. 33, 317–333 (2017). https://doi.org/10.1007/s00366-016-0476-8 CrossRefGoogle Scholar
- 9.Alioli, M., Morandini, M., Masarati, P.: Coupled multibody-fluid dynamics simulation of flapping wings. In: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V07BT10A014–V007BT010A014. American Society of Mechanical Engineers (2013)Google Scholar
- 10.Ambrósio, J., Pombo, J., Pereira, M.: Optimization of high-speed railway pantographs for improving pantograph-catenary contact. Theor. Appl. Mech. Lett. 3, 013006 (2013). https://doi.org/10.1063/2.1301306 CrossRefGoogle Scholar
- 11.Massat, J.-P., Laurent, C., Bianchi, J.-P., Balmes, E.: Pantograph catenary dynamic optimisation based on advanced multibody and finite element co-simulation tools. Veh. Syst. Dyn. 52, 338–354 (2014)CrossRefGoogle Scholar
- 12.Busch, M., Schweizer, B.: Coupled simulation of multibody and finite element systems: an efficient and robust semi-implicit coupling approach. Arch. Appl. Mech. 82, 723–741 (2012). https://doi.org/10.1007/s00419-011-0586-0 CrossRefzbMATHGoogle Scholar
- 13.Arnold, M., Clauss, C., Schierz, T.: Error analysis and error estimates for co-simulation in FMI for model exchange and co-simulation V2.0. Arch. Mech. Eng. 60, 75–94 (2013)CrossRefGoogle Scholar
- 14.Abel, A., Blochwitz, T., Eichberger, A., Hamann, P., Rein, U.: Functional mock-up interface in mechatronic gearshift simulation for commercial vehicles. In: Proceedings of the 9th International MODELICA Conference, pp. 775-780. Linköping University Electronic Press (2012)Google Scholar
- 15.González, F., Naya, M., Luaces, A., González, M.: On the effect of multirate co-simulation techniques in the efficiency and accuracy of multibody system dynamics. Multibody Syst. Dyn. 25, 461–483 (2011). https://doi.org/10.1007/s11044-010-9234-7 CrossRefzbMATHGoogle Scholar
- 16.González, F., González, M., Mikkola, A.: Efficient coupling of multibody software with numerical computing environments and block diagram simulators. Multibody Syst. Dyn. 24, 237–253 (2010)MathSciNetCrossRefGoogle Scholar
- 17.González, F., González, M., Cuadrado, J.: Weak coupling of multibody dynamics and block diagram simulation tools. In: ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 93–102. American Society of Mechanical Engineers (2009)Google Scholar
- 18.Tseng, F.C., Hulbert, G.M.: A gluing algorithm for network-distributed multibody dynamics simulation. Multibody Syst. Dyn. 6, 377–396 (2001). https://doi.org/10.1023/a:1012279120194 CrossRefzbMATHGoogle Scholar
- 19.Wang, J.Z., Ma, Z.D., Hulbert, G.M.: A gluing algorithm for distributed simulation of multibody systems. Nonlinear Dyn. 34, 159–188 (2003). https://doi.org/10.1023/B:NODY.0000014558.70434.b0 CrossRefzbMATHGoogle Scholar
- 20.Malczyk, P., Fraczek, J.: A divide and conquer algorithm for constrained multibody system dynamics based on augmented Lagrangian method with projections-based error correction. Nonlinear Dyn. 70, 871–889 (2012). https://doi.org/10.1007/s11071-012-0503-2 MathSciNetCrossRefGoogle Scholar
- 21.Schweizer, B., Li, P., Lu, D.: Explicit and implicit cosimulation methods: stability and convergence analysis for different solver coupling approaches. J. Comput. Nonlinear Dyn. 10, 051007–051012 (2015). https://doi.org/10.1115/1.4028503 CrossRefGoogle Scholar
- 22.Schweizer, B., Lu, D.: Semi-implicit co-simulation approach for solver coupling. Arch. Appl. Mech. 86, 1739–1769 (2014)CrossRefGoogle Scholar
- 23.Schweizer, B., Lu, D.: Predictor/corrector co-simulation approaches for solver coupling with algebraic constraints. J. Appl. Math. Mech. 95, 911–938 (2014). https://doi.org/10.1002/zamm.201300191 MathSciNetCrossRefzbMATHGoogle Scholar
- 24.Meyer, T., Li, P., Lu, D., Schweizer, B.: Implicit co-simulation method for constraint coupling with improved stability behavior. Multibody Syst. Dyn. (2018). https://doi.org/10.1007/s11044-018-9632-9 MathSciNetCrossRefGoogle Scholar
- 25.Arnold, M.: Stability of sequential modular time integration methods for coupled multibody system models. J. Comput. Nonlinear Dyn. 5, 1–9 (2010). https://doi.org/10.1115/1.4001389 CrossRefGoogle Scholar
- 26.Schierz, T., Arnold, M.: Stabilized overlapping modular time integration of coupled differential-algebraic equations. Appl. Numer. Math. 62, 1491–1502 (2012). https://doi.org/10.1016/j.apnum.2012.06.020 MathSciNetCrossRefzbMATHGoogle Scholar
- 27.Benedikt, M., Stettinger, G., Horn, M.: Sliding mode control for constraint stabilization in multi-body system dynamic analysis. In: IEEE Conference on Control Applications (CCA), 2014, pp. 1557–1562 (2014)Google Scholar
- 28.Benedikt, M., Watzenig, D., Hofer, A.: Modelling and analysis of the non-iterative coupling process for co-simulation. Math. Comput. Model. Dyn. Syst. 19, 451–470 (2013). https://doi.org/10.1080/13873954.2013.784340 MathSciNetCrossRefzbMATHGoogle Scholar
- 29.Sicklinger, S., Belsky, V., Engelmann, B., Elmqvist, H., Olsson, H., Wuchner, R., Bletzinger, K.U.: Interface Jacobian-based Co-Simulation. Int. J. Numer. Methods Eng. 98, 418–444 (2014). https://doi.org/10.1002/nme.4637 MathSciNetCrossRefzbMATHGoogle Scholar
- 30.Cadeau, T., Magoules, F.: Coupling the Parareal algorithm with the waveform relaxation method for the solution of differential algebraic equations. In: Proceedings of the 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES 2011), pp. 15–19 (2011). https://doi.org/10.1109/dcabes.2011.34
- 31.Lelarasmee, E., Ruehli, A.E., Sangiovanni-Vincentelli, A.L.: The waveform relaxation method for time-domain analysis of large scale integrated circuits. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 1, 131–145 (1982). https://doi.org/10.1109/TCAD.1982.1270004 CrossRefGoogle Scholar
- 32.Mohtat, A., Arbatani, S., Kövecses, J.: Enhancing the stability of co-simulation via an energy-leak monitoring and dissipation framework. In: IUTAM Symposium on Co-Simulation and Solver Coupling. Darmstadt, Germany (2017)Google Scholar
- 33.Sadjina, S., Kyllingstad, L.T., Skjong, S., Pedersen, E.: Energy conservation and power bonds in co-simulations: non-iterative adaptive step size control and error estimation. Eng. Comput. 33, 607–620 (2017). https://doi.org/10.1007/s00366-016-0492-8 CrossRefGoogle Scholar
- 34.Gu, B., Asada, H.H.: Co-simulation of algebraically coupled dynamic subsystems without disclosure of proprietary subsystem models. J. Dyn. Syst. Measure. Control-Trans. ASME 126, 1–13 (2004). https://doi.org/10.1115/1.1648307 CrossRefGoogle Scholar
- 35.Gensor, S., Benedikt, M.: Model-based pre-step stabilization method for non-iterative co-simulation. In: The 5th Joint International Conference on Multibody System Dynamics. Lisboa, Portugal (2018)Google Scholar
- 36.Haid, T., Stettinger, G., Watzenig, D., Benedikt, M.: A model-based corrector approach for explicit co-simulation using subspace identification. In: The 5th Joint International Conference on Multibody System Dynamics. Lisboa, Portugal (2018)Google Scholar
- 37.Schweizer, B., Lu, D., Li, P.: Co-simulation method for solver coupling with algebraic constraints incorporating relaxation techniques. Multibody Syst. Dyn. 36, 1–36 (2015). https://doi.org/10.1007/s11044-015-9464-9 MathSciNetCrossRefzbMATHGoogle Scholar
- 38.Li, P., Lu, D., Schmoll, R., Schweizer, B.: Explicit Co-simulation Approach with Improved Numerical Stability, pp. 153–201. Springer, Cham (2019)Google Scholar