Reliability-based multiobjective optimization for automotive crashworthiness and occupant safety |
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Authors: | Kaushik Sinha |
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Affiliation: | (1) DaimlerChrysler Research and Technology, Bangalore, India |
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Abstract: | This paper presents a methodology for reliability-based multiobjective optimization of large-scale engineering systems. This
methodology is applied to the vehicle crashworthiness design optimization for side impact, considering both structural crashworthiness
and occupant safety, with structural weight and front door velocity under side impact as objectives. Uncertainty quantification
is performed using two first order reliability method-based techniques: approximate moment approach and reliability index
approach. Genetic algorithm-based multiobjective optimization software GDOT, developed in-house, is used to come up with an
optimal pareto front in all cases. The technique employed in this study treats multiple objective functions separately without
combining them in any form. It shows that the vehicle weight can be reduced significantly from the baseline design and at
the same time reduce the door velocity. The obtained pareto front brings out useful inferences about optimal design regions.
A decision-making criterion is subsequently invoked to select the “best” subset of solutions from the obtained nondominated
pareto optimal solutions. The reliability, thus computed, is also checked with Monte Carlo simulations. The optimal solution
indicated by knee point on the optimal pareto front is verified with LS-DYNA simulation results. |
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Keywords: | Reliability-based multiobjective optimization Uncertainty quantification FORM Nondominated points GDOT Pareto optimal solution Knee point Automotive crashworthiness Occupant safety Side impact Monte Carlo simulation |
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