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IRK vs structural integrators for real-time applications in MBS

  • D. Dopico
  • U. Lugris
  • M. Gonzalez
  • J. Cuadrado
Article

Abstract

Recently, the authors have developed a method for real-time dynamics of multibody systems, which combines a semi-recursive formulation to derive the equations of motion in dependent relative coordinates, along with an augmented Lagrangian technique to impose the loop closure conditions The following numerical integration procedures, which can be grouped into the socalled structural integrators, were tested trapezoidal rule, Newmark disstpative schemes, HHT rule, and the Generalized-α family It was shown that, for large multibody systems, Newmark dissipative was the best election since, provided that the adequate parameters were chosen, excellent behavioi was achieved in terms of efficiency and lobustness with acceptable levels of accuracy In the present paper, the performance of the described method in combination with another group of integrators, the Implicit Runge-Kutta family (IRK), is analyzed The purpose is to clarify which kind of IRK algorithms can be more suitable for real-time applications, and to see whether they can be competitive with the already tested structural family of integrators The final objective of the work is to provide some practical criteria for those interested in achieving real-time performance for large and complex multibody systems

Key Words

Multibody Systems Dynamics Real-Time Simulation Numencal Integration Structural Integrators IRK 

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References

  1. Ascher, U M and Petzold, L R, 1998, “Computer Methods for Oidinary Differential Equations and Differential Algebraic Equations,” Philadelphia Society for Industrial and Applied MathematicsGoogle Scholar
  2. Cuadrado, J and Dopico, D, 2003, “Penalty, Semirecursive and Hybrid Methods for MBS Real-Time Dynamics in the Context of Structural Integrators,”Proceedings of Multibody Dynamics, Lisbon, PortugalGoogle Scholar
  3. Garcia de Jalon, J and Bayo, E, 1994, “Kinematic and Dynamic Simulation of Multibody Systems,” Springer-VerlagGoogle Scholar
  4. Geradm, M and Cardona, A, 2001, “Flexible Multibody Dynamics A Finite Element Sppioach,” WileyGoogle Scholar
  5. Hairer, E and Wanner, G, 1996, “Solving Ordinary Differential Equations II Stiff and Differential Algebraic Problems,” Springer-VerlagGoogle Scholar
  6. Iltis Data Package, 1990, IAVSD Workshop, Hei bertov, CzechoslovakiaGoogle Scholar
  7. Lambert, J D, 1997, “Numerical Methods for Ordinary Differential Systems,” WileyGoogle Scholar
  8. Meijaaid, J P, 2003, “Application of Runge-Kutta-Rosenbrock Methods to the Analysis of Flexible Multibody Systems,”Multibody System Dynamics, Vol 10, pp 263–288CrossRefGoogle Scholar
  9. Negrut, D, Haug, E J and German, H C, 2003, “An implicit Runge-Kutta Method for Integration of Differential Algebraic Equations of Multibody Dynamics,”Multibody System Dynamics, Vol 9, pp 121–142MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers (KSME) 2005

Authors and Affiliations

  • D. Dopico
    • 1
  • U. Lugris
    • 1
  • M. Gonzalez
    • 1
  • J. Cuadrado
    • 1
  1. 1.Laboratory of Mechanical EngineeringUniversity of La CoruñaFerrolSpain

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