基于GWO⁃KRG近似模型的乘员约束系统可靠性优化设计

doi:10.19562/j.chinasae.qcgc.2021.06.010

Abstract

Abstract:

In order to enhance the safety performance of the occupant restraint system， the parameter optimization technology for surrogate model is applied to the reliability optimization design of the restraint system in this paper. Firstly， a simulation model for the driver?side restraint system of a vehicle is established and verified by real vehicle crash test. Then， the grey wolf optimization （GWO） algorithm is used to optimize the correlation parameters of Kriging （KRG） model， so a high?accuracy GWO?KRG surrogate model is obtained. Finally， based on GWO?KRG surrogate model， a reliability optimization is conducted on the restraint system. The results show that GWO?KRG surrogate model can provide more accurate predicted response， and after reliability optimization the safety performance of the restraint system is improved with its reliability also guaranteed.

Key words: occupant restraint system, grey wolf optimization, Kriging surrogate model, reliability optimization design

Xianguang Gu,Menglin Gao,Xiaole Wang,Yuezhu Huang. Reliability Optimization Design of Occupant Restraint System Based on GWO⁃KRG Surrogate Model[J].Automotive Engineering, 2021, 43(6): 870-876.

Figures/Tables 12

References 13

1	World Health Organization. Global status report on road safety 2018［R］. WHO， 2018.
2	肖志，叶映台，李伟平，等. 汽车侧面碰撞中乘员约束系统的可靠性优化［J］. 湖南大学学报（自然科学版）， 2011， 38（9）： 39-43.
	XIAO Z， YE Y T， LI W P， et al. Reliability optimization of occupant restraint system in side impact tests［J］. Journal of Hunan University （Natural Sciences）， 2011， 38（9）： 39-43.
3	ZHU P， ZHANG Y， CHEN G. Metamodeling development for reliability⁃based design optimization of automotive body structure［J］. Computers in Industry， 2011， 62（7）： 729-741.
4	GU X G， LU J W. Reliability-based robust assessment for multiobjective optimization design of improving occupant restraint system performance［J］. Computers in Industry， 2014， 65（8）： 1169-1180.
5	李铁柱，李光耀，陈涛，等. 基于Kriging近似模型的汽车乘员约束系统稳健性设计［J］. 机械工程学报， 2010， 46（22）： 123-129.
	LI T Z， LI G Y， CHEN T， et al. Robustness design of occupant restraint system based on Kriging model［J］. Journal of Mechanical Engineering， 2010， 46（22）： 123-129.
6	郑建洲，陈有松，吕斌斌，等. 基于Kriging模型的座椅子系统安全性能优化研究［J］. 汽车工程， 2019， 41（11）： 1301-1307.
	ZHENG J Z， CHEN Y S， LV B B， et al. A study on safety performance optimization of seat subsystem based on Kriging model［J］. Automotive Engineering， 2019， 41（11）： 1301-1307.
7	陈志英，任远，白广忱，等. 粒子群优化的Kriging近似模型及其在可靠性分析中的应用［J］. 航空动力学报， 2011， 26（7）： 1522-1530.
	CHEN Z Y， REN Y， BAI G C， et al. Particle swarm optimized Kriging approximate model and its application to reliability analysis［J］. Journal of Aerospace Power， 2011， 26（7）： 1522-1530.
8	王剑波，缪建家，阚毓锋，等. 基于粒子群算法优化Kriging模型的桥梁钢筋锈蚀可靠度分析［J］. 北方交通， 2019 （8）：20-25.
	WANG J B， MIAO J J， KAN Y F， et al. Reliability analysis of bridge steel corrosion based on Kriging method improved by particle swarm optimization algorithm［J］.Northern Communications， 2019 （8）： 20-25.
9	付宜利，封海波，孙建勋，等. 机电产品管路自动敷设的粒子群算法［J］. 机械工程学报， 2007， 43（11）： 194-199.
	FU Y L， FENG H B， SUN J X， et al. Automatic pipe⁃routing particle swarm optimization algorithm in electromechamical products［J］. Chinese Journal of Mechanical Engineering， 2007， 43（11）： 194-199.
10	MIRJALILI S， MIRJALILI S M， LEWIS A. Grey wolf optimizer［J］. Advances in Engineering Software， 2014， 69（3）： 46-61.
11	王兰梅，陈崇成，叶晓燕，等. 网络搜索数据和GWO⁃SVR模型的旅游短期客流量预测［J］. 福州大学学报（自然科学版）， 2019， 47（5）： 598-603.
	WANG L M， CHEN C C， YE X Y， et al. Forecasting short-term tourist flow based on web search data and GWO⁃SVR model［J］. Journal of Fuzhou University（Natural Science Edition）， 2019， 47（5）： 598-603.
12	张海洋，胡帅帅，周大永，等. 基于PSO-SVR近似模型的乘员约束系统稳健性优化［J］. 汽车工程， 2020， 42（4）： 462-467.
	ZHANG H Y ， HU S S， ZHOU D Y， et al. Robust optimization of occupant restraint system based on PSO⁃SVR approximation model［J］. Automotive Engineering， 2020， 42（4）： 462-467.
13	VIANO D C， AREPALLY S. Assessing the safety performance of occupant restraint systems［J］. Society of Automotive Engineers Transactions， 1990， 43（43）： 1407-1413.

损伤指标	仿真结果	试验结果	相对误差
HIC₃₆	976.78	905.68	7.85%
C_3ms/g	52.66	49.71	5.91%
C_comp/mm	31.37	30.85	1.69%
FFCL/N	1 365.03	1 465.33	6.84%
FFCR/N	1 632.21	1 765.89	7.57%

设计变量	初始值	取值范围
设计变量	初始值	上限	下限
x₁	0.64	0.8	0.4
x₂/s	0.022	0.030	0.012
x₃/mm	37	55	30
x₄	1	1.2	0.8
x₅/L	52.5	60	40

损伤指标	初始值	目标值
HIC₃₆	976.78	≤650
C_3ms/g	52.66	≤50
C_comp/mm	31.365	≤30
FFCL/N	1 365.0	≤2000
FFCR/N	1 632.2	≤2000
WIC	0.820	最小

最优值	HIC₃₆	C_3ms/ g	C_comp/ mm	FFCL/ N	FFCR/ N	WIC
θ₁	0.10	0.69	5.08	1.38	3.98	1.97
θ₂	0.10	0.32	18.57	1.61	2.70	7.05
θ₃	5.29	8.42	20	18.50	12.74	8.58
θ₄	0.10	6.40	20	1.08	16.21	1.37
θ₅	4.09	18.56	20	9.16	8.23	7.64

损伤指标	精度指标	RSM	RBF	KRG	PSO? KRG	GWO?KRG
HIC₃₆	MRE/%	6.951	3.118	5.923	2.778	2.754
HIC₃₆	R²	0.949	0.989	0.965	0.994	0.994
C_3ms/g	MRE/%	3.341	3.095	7.026	2.439	2.061
C_3ms/g	R²	0.988	0.991	0.668	0.987	0.988
C_comp/mm	MRE/%	7.318	6.405	2.378	1.691	1.488
C_comp/mm	R²	0.762	0.223	0.994	0.916	0.951
FFCL/N	MRE/%	3.847	8.146	9.358	2.663	2.528
FFCL/N	R²	0.865	0.909	0.920	0.891	0.900
FFCR/N	MRE/%	6.332	5.326	5.622	6.109	4.327
FFCR/N	R²	0.794	0.898	0.840	0.915	0.911
WIC	MRE/%	3.345	5.384	6.076	2.307	2.317
WIC	R²	0.989	0.968	0.949	0.989	0.990

Reliability Optimization Design of Occupant Restraint System Based on GWO⁃KRG Surrogate Model

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Figures/Tables 12

References 13

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损伤指标	初始设计	确定性优化设计		可靠性优化设计
损伤指标	初始设计	预测值	可靠度	预测值	可靠度
HIC₃₆	976.78	291.09	87.15%	572.96	100%
C_3ms/g	52.66	44.99	99.95%	48.66	100%
C_comp/ mm	31.37	26.36	76.26%	27.99	95%
FFCL/ N	1 365.03	1 256.77	100%	1 347.12	100%
FFCR/ N	1 632.21	1 863.25	100%	1 759.32	100%
WIC	0.82	0.45		0.57

设计变量	初始设计	确定性优化设计	可靠性优化设计
x₁	0.64	0.687	0.582
x₂/s	0.022	0.014 8	0.018 8
x₃/mm	37.0	51.5	47.4
x₄	1.0	1.016	1.062
x₅/L	52.5	44.9	50.3

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