A Comparative study on multiobjective reliable and robust optimization for crashworthiness design of vehicle structure

• Published in: Structural and multidisciplinary optimization Vol. 48; no. 3; pp. 669 - 684
• Main Authors: Gu, Xianguang, Sun, Guangyong, Li, Guangyao, Mao, Lichen, Li, Qing
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2013; Springer Nature B.V
• Subjects: Classification; Comparative studies; Computational Mathematics and Numerical Analysis; Computer simulation; Crashworthiness; Deceleration; Design optimization; Energy absorption; Engineering; Engineering Design; Fuel consumption; Genetic algorithms; Impact strength; Industrial Application; Intrusion; Metamodels; Monte Carlo simulation; Multiple objective analysis; Parameter uncertainty; Pareto optimization; Product design; Robust design; Robustness; Sorting algorithms; Theoretical and Applied Mechanics; Surrogate metamodel; Robust optimization; Reliable optimization; Multiobjective optimization; Crashworthiness
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-013-0921-x

Design optimization without considering uncertainties of system variables and parameters can be problematic in real life. In order to take into account the effect of uncertainties, reliable and robust design schemes have proven effective, but limited studies have been reported to compare their difference in a multiobjective framework. This paper takes a typical vehicle structure subject to offset frontal crashing scenario as an example to compare reliable and robust designs with their deterministic counterpart. The thicknesses of some key components of vehicle frontal structures were selected as design variables, the vehicle weight and energy absorption as the objectives, deceleration and firewall intrusion as the constraints. The deterministic multiobjective optimization problem was first solved by adopting Design of Experimental (DOE), metamodels and Non-dominated Sorting Genetic Algorithm II (NSGA-II). Take into account the uncertainties, a Monte Carlo Simulation (MCS) is adopted to generate random distributions of the objective and constraint functions for each design. For the reliability-based optimization the desired reliabilities of 90 %, 95 % and 99 % are considered, respectively. For the robustness-based optimization, two different formulation strategies are adopted. The optimization showed that the reliable and robust Pareto fronts are shifted away from their deterministic counterpart due to uncertainties. The different Pareto fronts yielded from the deterministic, reliable and robust designs are compared to provide some quantitative insights into how to apply these different design schemes for resolving uncertainty problems. It is shown that, compared with the baseline design, the optimizations enhance the crashworthiness of vehicle, though more conservative solutions could have been generated from the reliable and robust optimizations.