Computer-aided analysis of mechanical systems
Computer-aided analysis of mechanical systems
Computer aided kinematics and dynamics of mechanical systems. Vol. 1: basic methods
Computer aided kinematics and dynamics of mechanical systems. Vol. 1: basic methods
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Kinematic and Dynamic Simulation of Multibody Systems: The Real Time Challenge
Kinematic and Dynamic Simulation of Multibody Systems: The Real Time Challenge
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
On the calibration of simplified vehicle crash models
Structural and Multidisciplinary Optimization
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The design of components, or particular safety devices, for road vehicles often requires that many analyses are performed to appraise different solutions. Multibody approaches for structural crashworthiness provide alternative methodologies to the use of detailed finite element models reducing calculation time by several orders of magnitude while providing results with the necessary quality. The drawbacks on the use of multibody approaches for crashworthiness are the cumbersome model development process and difficulty of model validation. This work proposes an optimization procedure to assist multibody vehicle model development and validation. First the topological structure of the multibody system is devised representing the structural vehicle components and describing the most relevant mechanisms of deformation. The uncertainty in the model development resides on the constitutive behavior of the plastic hinges used to represent the structural deformations of the vehicle, identified by using the classic multibody approach. The constitutive relations are identified by using an optimization procedure based on the minimization of the deviation between the observed response of the vehicle model and a reference response, obtained by experimental testing or using more detailed models of the vehicle. The identification is supported by a genetic optimization algorithm that enables the characterization of several initial sets of parameters that characterize the plastic hinge constitutive relations in the vicinity of an Edgeworth-Pareto front. Afterwards, a deterministic optimization algorithm is used to identify the accurate minimum. The procedure is demonstrated by the identification of the multibody model of a large family car suitable for front impact.