Reliability-based robust assessment for multiobjective optimization design of improving occupant restraint system performance

•A system approach is developed to optimize the occupant restraint system.•Three different optimization design methods are compared.•The optimization design result is validated by the physical test. Optimal performance of vehicle occupant restraint system (ORS) requires an accurate assessment of occ...

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Vydané v:Computers in industry Ročník 65; číslo 8; s. 1169 - 1180
Hlavní autori: Gu, Xianguang, Lu, Jianwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier B.V 01.10.2014
Elsevier
Elsevier Sequoia S.A
Predmet:
Applied physiology
Applied sciences
Biological and medical sciences
Computer science; control theory; systems
Data processing. List processing. Character string processing
Design of experiments
Design optimization
Exact sciences and technology
Ground, air and sea transportation, marine construction
Human physiology applied to population studies and life conditions. Human ecophysiology
Mathematical problems
Mechanical engineering. Machine design
Medical sciences
Memory organisation. Data processing
Monte Carlo simulation
Multiobjective optimization
Occupant restraint system
Optimization algorithms
Pareto optimum
Reliability-based robust design
Road transportation and traffic
Software
Studies
Support vector regression
Systems development
Transports. Aerospace. Diving. Altitude
Support vector regression
Occupant restraint system
Reliability-based robust design
Multiobjective optimization
Motor car
Evolutionary algorithm
Multiobjective programming
Modeling
Optimal strategy
Uncertain system
Passenger safety
Vector support machine
Swarm intelligence
Robustness
Deterministic approach
Taguchi method
Mathematical programming
Monte Carlo method
Dominating set
Traffic accident
Nonlinear problems
Regression analysis
Particle swarm optimization
Trauma
Engineering design
Optimal sampling
Experimental design
Large scale
Reliability
ISSN:0166-3615, 1872-6194
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Popis
Shrnutí:•A system approach is developed to optimize the occupant restraint system.•Three different optimization design methods are compared.•The optimization design result is validated by the physical test. Optimal performance of vehicle occupant restraint system (ORS) requires an accurate assessment of occupant injury values including head, neck and chest responses, etc. To provide a feasible framework for incorporating occupant injury characteristics into the ORS design schemes, this paper presents a reliability-based robust approach for the development of the ORS. The uncertainties of design variables are addressed and the general formulations of reliable and robust design are given in the optimization process. The ORS optimization is a highly nonlinear and large scale problem. In order to save the computational cost, an optimal sampling strategy is applied to generate sample points at the stage of design of experiment (DOE). Further, to efficiently obtain a robust approximation, the support vector regression (SVR) is suggested to construct the surrogate model in the vehicle ORS design process. The multiobjective particle swarm optimization (MPSO) algorithm is used for obtaining the Pareto optimal set with emphasis on resolving conflicting requirements from some of the objectives and the Monte Carlo simulation (MCS) method is applied to perform the reliability and robustness analysis. The differences of three different Pareto fronts of the deterministic, reliable and robust multiobjective optimization designs are compared and analyzed in this study. Finally, the reliability-based robust optimization result is verified by using sled system test. The result shows that the proposed reliability-based robust optimization design is efficient in solving ORS design optimization problems.
Bibliografia:SourceType-Scholarly Journals-1
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content type line 14
ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2014.07.003