Dynamic Response Optimization of Complex Multibody Systems in a Penalty Formulation using Adjoint Sensitivity

TL;DR

This paper develops an adjoint sensitivity analysis method for complex multibody systems within a penalty formulation, enabling efficient gradient-based optimization for large-scale mechanical systems, demonstrated on a five-bar mechanism and a vehicle model.

Contribution

It introduces a novel adjoint sensitivity approach for multibody systems in a penalty formulation, improving computational efficiency and accuracy for design optimization.

Findings

Successfully applied to a five-bar mechanism for sensitivity analysis.

Demonstrated capability on a 14-DOF vehicle model for optimization.

Enhanced efficiency over finite difference methods.

Abstract

Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization. A very important first step towards design optimization is sensitivity analysis of multibody system dynamics. Dynamic sensitivities are often calculated by means of finite differences. Depending of the number of parameters involved, this procedure can be computationally expensive. Moreover, in many cases, the results suffer from low accuracy when real perturbations are used. The main contribution to the state-of-the-art brought by this study is the development of the adjoint sensitivity approach of multibody systems in the context of the penalty formulation. The theory developed is demonstrated on one academic case study, a five-bar mechanism, and on one real-life system, a 14-DOF vehicle model. The five-bar mechanism is used to illustrate the sensitivity approach derived in this paper. The full vehicle model is used to demonstrate the capability of the new approach developed to perform sensitivity analysis and gradient-based optimization for large and complex multibody systems with respect to multiple design parameters.