Title of article :
Parametric analysis and multiobjective optimization for functionally graded foam-filled thin-wall tube under lateral impact
Author/Authors :
Fang، نويسنده , , Jianguang and Gao، نويسنده , , Yunkai and Sun، نويسنده , , Guangyong and Zhang، نويسنده , , Yuting and Li، نويسنده , , Qing، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Pages :
11
From page :
265
To page :
275
Abstract :
Foam-filled thin-walled tubes have proven an ideal energy absorber in automotive industry for its extraordinary energy-absorbing ability and lightweight potential. Unlike existing uniform foam (UF), this paper introduces functionally graded foam (FGF) to fill into the thin-walled structure subjected to lateral impact loading, where different configurations of foam grading (axial FGF and two transverse FGFs) are considered. To systematically investigate the bending behavior of this novel structure, numerical model is established using nonlinear finite element analysis code LS-DYNA and then is validated against the experiment. Through parametric study, it is found that the FGF tube absorbs more energy but may produce larger force than the UF counterpart. In addition, various parameters have a considerable effect on the crashworthiness performance of the FGF filled tube. Finally, multiobjective optimizations of UF and FGF filled columns are conducted, aiming to improve the specific energy absorption (SEA) and reduce the maximum impact force simultaneously, based upon the multiobjective particle optimization (MOPSO) algorithm and Kriging modeling technique. The optimization results show that all the FGF filled tubes can produce better Pareto solutions than the ordinary UF counterpart. Furthermore, the axial FGF tube provides better energy absorption characteristics than the two types of transverse FGF tubes.
Keywords :
Functionally graded foam (FGF) , Multiobjective Optimization , Kriging model , Energy absorption , Crashworthiness , Three-point bending
Journal title :
Computational Materials Science
Serial Year :
2014
Journal title :
Computational Materials Science
Record number :
1692876
Link To Document :
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