Abstract

This paper illustrates the performance of several representative implicit A-stable time integration methods with algorithmic dissipation for multibody system dynamics, formulated as a set of mixed implicit first-order differential and algebraic equations. The integrators include the linear multi-step methods with two to four steps, the single-step reformulations of the linear multi-step methods, and explicit first-stage, singly diagonally-implicit Runge–Kutta methods. All methods are implemented in the free, general-purpose multibody solver MBDyn. Their formulations and implementation are presented. According to the comparison from linear analysis and numerical experiments, some general conclusions on the selection of integration schemes and their implementation are obtained. Although all of these methods can predict reasonably accurate solutions, the specific advantages that each of them has in different situations are discussed.

Highlights

  • Multibody system dynamics problems can be typically formulated as a set of DifferentialAlgebraic Equations (DAEs), often in semi-explicit form

  • Reformulation usually consists of some sort of index reduction that can convert DAEs into Ordinary Differential Equations (ODEs), and allows the problems to be solved using relatively conventional methods

  • Initial-value problems in MBDyn are formulated as a set of implicit first-order DAEs, whose general form is r (y, y, t) = 0, y(t0) = y0 (1)

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Summary

Introduction

Multibody system dynamics problems can be typically formulated as a set of DifferentialAlgebraic Equations (DAEs), often in semi-explicit form. The time integration methods employed include the linear multi-step methods [23, 24, 36], their equivalent single-step methods [36], and stiffly-accurate, explicit firststage, singly diagonally-implicit Runge–Kutta (ESDIRK) methods [21, 35]. These methods are implemented in the free general-purpose multibody dynamics analysis software MBDyn1 [24].

Formulation
Linear multi-step methods
Equivalent single-step methods
Implementation
Prediction
Correction
Generalization and extension
Linear analysis
Numerical experiments
Andrew’s squeezer mechanism
Conclusions
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