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  • 标题:Application of Genetic Algorithm on Parameter Optimization of Three Vehicle Crash Scenarios
  • 本地全文:下载
  • 作者:Bernard B. Munyazikwiye ; Hamid R. Karimi ; Kjell G. Robbersmyr
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2017
  • 卷号:50
  • 期号:1
  • 页码:3697-3701
  • DOI:10.1016/j.ifacol.2017.08.564
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractThis paper focuses on the development of mathematical models for vehicle frontal crashes. The models under consideration are threefold: a vehicle into barrier, vehicle-occupant and vehicle to vehicle frontal crashes. The first model is represented as a simple spring-mass-damper and the second case consists of a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The third model consists of a collision of two vehicles represented by two masses moving in opposite directions. The springs and dampers in the models are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a genetic algorithm (GA) approach is proposed for estimating the parameters of vehicles front structure and restraint system for vehicle-occupant model. Finally, using the existing test-data, it is shown that the obtained models can accurately reproduce the real crash test data.
  • 关键词:KeywordsModelingvehicle-occupantfrontal crashparameters estimationgenetic algorithm
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