基于多工况关联的无人车辆车身结构轻量化优化设计

doi:10.12382/bgxb.2022.1301

摘要/Abstract

摘要：

未来战场的多样化对特种无人车辆的环境适应性提出了更高的要求。为满足特种无人车辆的多工况使用、高机动能力与低成本研制等要求,采用多工况关联设计与轻量化优化的思路对无人车桁架车身结构进行优化设计。考虑到多工况的车身结构设计变量多、设计空间大,面临仿真次数过多的问题,提出基于多工况关联的车身结构轻量化优化方法,利用设计变量区间缩减策略减小设计空间;引入高斯过程代理模型替换仿真分析实现结构设计方案性能的快速评估;结合遗传算法实现方案的优化。实验结果表明,最终优化方案在通过车身刚度强度和模态等多学科性能仿真验证的情况下,质量比初始方案降低14.12%,比只用高斯过程优化设计的方案降低8.87%。

关键词: 无人车辆, 车身结构轻量化, 多工况关联, 设计空间缩减, 代理模型, 优化设计

Abstract:

The higher requirements are put forward for the environmental adaptability of special unmanned vehicles in the future battlefield. In order to meet the requirements of multi-working conditions, high maneuverability and low-cost development of special unmanned vehicles, the truss body structure of unmanned vehicles is optimized by the idea of multi-working conditions correlation design and lightweight optimization. However, considering that there are many design variables, large design space and too many simulation times in multi-working conditions, a lightweight optimization method of body structure based on multi-working conditions correlation is proposed; In the proposed method, the strategy of reducing design variables interval is used to reduce the design space. Gaussian process surrogate model is introduced to replace the simulation analysis for realizing the rapid performance evaluation of structural design scheme, and the optimization of the scheme is realized using genetic algorithm. The experimental results show that the mass of the final optimization scheme is 14.12% lower than that of the initial scheme and 8.87% lower than that of the scheme optimized only by Gaussian process under the condition of multi-disciplinary performance simulation verification, such as stiffness, strength and mode.

Key words: unmanned vehicle, lightweight body structure, multi-working conditions correlation, design space reduction, surrogate model, design optimization

中图分类号:

TJ819

李作轩, 贾良跃, 郝佳, 王超, 王国新, 明振军, 阎艳. 基于多工况关联的无人车辆车身结构轻量化优化设计[J]. 兵工学报, 2023, 44(11): 3529-3542.

LI Zuoxuan, JIA Liangyue, HAO Jia, WANG Chao, WANG Guoxin, MING Zhenjun, YAN Yan. Lightweight Optimization Design of Unmanned Vehicle Body Structure Based on Multi-working Conditions Correlation[J]. Acta Armamentarii, 2023, 44(11): 3529-3542.

图/表 24

图1 某无人车辆桁架车身结构模型

Fig.1 Truss body structure model of an unmanned vehicle

表1 无人车桁架式车身结构模型与多工况环境的参数

Table 1 Parameters of truss body structure model of unmanned vehicle and multi-working environment

车身结构、工况环境及车身材料	设计参数	取值范围
	L/mm	3620~3820
	W/mm	1300~1500
	H/mm	620~820
	T/mm	2~6
车身结构	L₁/mm	60~80
	W₁/mm	40~60
	L₂/mm	40~60
	W₂/mm	20~40
	L₃/mm	20~40
	W₃/mm	10~20
		冲击工况
		制动工况
		转弯工况
工况环境	7种不同工况类型C	弯曲工况
		扭转1轮工况
		扭转2轮工况
		射击工况
车身材料	材料编号M	碳素钢、铝合金、钛合金

表2 不同材料性能参数

Table 2 Property parameters of different materials

材料名称	质量密度/(t·mm^-3)	弹性模量/MPa	泊松比
碳素钢	7.80×10^-9	2.01×10⁵	0.26
铝合金	2.78×10^-9	0.70×10⁵	0.26
钛合金	4.50×10^-9	1.10×10⁵	0.34

图2 车身结构静态载荷施加

Fig.2 Static load applied on body structure

图3 不同工况载荷施加

Fig.3 Load application under different working conditions

表3 7种不同工况约束和载荷

Table 3 Constraints and loads in 7 different working conditions

工况类型	约束方向				载荷施加
工况类型	左侧前轮	右侧前轮	左侧后轮	右侧后轮	载荷施加
冲击工况	X,Y,Z	X,Y,Z	X,Y,Z	X,Y,Z	6g的Y轴方向全局载荷
制动工况	X,Y,Z	X,Y,Z	Y,Z	Y,Z	0.8g的X轴方向全局载荷
转弯工况	X,Y,Z	X,Y	X,Y,Z	X,Y	0.7g的Z轴方向全局载荷
弯曲工况	X,Y	X,Y	Y,Z	Y,Z	3g的Y轴方向全局载荷
扭转1轮工况	X,Y,Z	X,Y,Z	X,Y	X,Y,Z	6g的Y轴方向全局载荷
扭转2轮工况	X,Y,Z	X,Y,Z	X,Y	X,Y	3g的Y轴方向全局载荷
射击工况	X,Y,Z	X,Y,Z	X,Y,Z	X,Y,Z	3g的Y轴方向载荷外,还需要在顶端射击开口处施加30kN压力

图4 方法总体步骤

Fig.4 Overall steps of optimization method

图5 设计空间动态缩减

Fig.5 Design space dynamic reduction

图6 构建GP代理模型

Fig.6 Construction of Gaussian process surrogate model

图7 变量区间缩减方法

Fig.7 Variable interval reduction method

图8 基于GA的多工况车身结构优化

Fig.8 Multi-condition body structure optimization based on genetic algorithm

表4 GA超参数

Table 4 GA superparameters

超参数名称	取值
种群数目	500
遗传代数	500
交叉概率	0.7
变异概率	0.2
精英策略	每代保留最好的1个点

表5 代理模型车身最大应力预测误差

Table 5 Predicted maximum stress errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	5.07	5.07	$4.97_$	$98_$	98
制动工况	7.79	4.29	$4.22_$	$100_$	98
转弯工况	4.97	6.25	$4.24_$	$100_$	98
弯曲工况	$4.43_$	6.55	5.13	$100_$	98
扭转1轮工况	7.28	7.51	$5.52_$	$100_$	98
扭转2轮工况	$3.31_$	11.40	4.80	$100_$	78
射击工况	$4.67_$	11.90	5.51	$96_$	90

表5 代理模型车身最大应力预测误差

Table 5 Predicted maximum stress errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	5.07	5.07	$4.97_$	$98_$	98
制动工况	7.79	4.29	$4.22_$	$100_$	98
转弯工况	4.97	6.25	$4.24_$	$100_$	98
弯曲工况	$4.43_$	6.55	5.13	$100_$	98
扭转1轮工况	7.28	7.51	$5.52_$	$100_$	98
扭转2轮工况	$3.31_$	11.40	4.80	$100_$	78
射击工况	$4.67_$	11.90	5.51	$96_$	90

表6 代理模型车身最大位移预测误差

Table 6 Predicted maximum displacement errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	$0.63_$	0.63	6.72	$98_$	98
制动工况	0.94	$0.82_$	6.72	$100_$	92
转弯工况	0.81	$0.76_$	4.73	78	98
弯曲工况	$0.65_$	1.29	7.23	90	$100_$
扭转1轮工况	1.91	$1.27_$	5.58	90	$94_$
扭转2轮工况	$0.41_$	1.95	2.89	$100_$	96
射击工况	$0.16_$	1.99	4.66	$100_$	86

表6 代理模型车身最大位移预测误差

Table 6 Predicted maximum displacement errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	$0.63_$	0.63	6.72	$98_$	98
制动工况	0.94	$0.82_$	6.72	$100_$	92
转弯工况	0.81	$0.76_$	4.73	78	98
弯曲工况	$0.65_$	1.29	7.23	90	$100_$
扭转1轮工况	1.91	$1.27_$	5.58	90	$94_$
扭转2轮工况	$0.41_$	1.95	2.89	$100_$	96
射击工况	$0.16_$	1.99	4.66	$100_$	86

表7 代理模型车身质量预测误差

Table 7 Predicted body mass errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	$0_$	0	0.64	$100_$	100
制动工况	$0_$	0	0.62	$96_$	86
转弯工况	$0.01_$	0.09	1.20	$100_$	98
弯曲工况	$0.01_$	0.04	0.75	$100_$	100
扭转1轮工况	$0_$	0.01	0.88	$100_$	90
扭转2轮工况	$0_$	2.08	1.07	96	$100_$
射击工况	$0_$	0.54	1.18	96	$100_$

表7 代理模型车身质量预测误差

Table 7 Predicted body mass errors of the surrogate model

工况类型	预测误差/%			预测正确率/%
工况类型	RDS-GP	GP	ANN	RDS-GP	GP
冲击工况	$0_$	0	0.64	$100_$	100
制动工况	$0_$	0	0.62	$96_$	86
转弯工况	$0.01_$	0.09	1.20	$100_$	98
弯曲工况	$0.01_$	0.04	0.75	$100_$	100
扭转1轮工况	$0_$	0.01	0.88	$100_$	90
扭转2轮工况	$0_$	2.08	1.07	96	$100_$
射击工况	$0_$	0.54	1.18	96	$100_$

图9 代理模型预测误差变化趋势

Fig.9 Changing trend of predicted errors of the surrogate model

表8 不同车身结构优化方案结果对比

Table 8 Comparison of different optimal body structure schemes

参数变量及优化目标		RDS-GP	GP	ANN
	L	3620.01	3620.00	3621.17
	W	1300.00	1318.95	1300.00
	H	620.00	720.00	620.01
	L₁	60.00	65.81	75.83
	W₁	51.26	50.00	52.20
参数变量	L₂	51.26	50.00	52.20
	W₂	36.69	39.28	39.98
	L₃	36.69	39.28	39.98
	W₃	15.00	14.68	19.98
	T	5.00	5.07	4.00
	M	碳素钢	碳素钢	碳素钢
优化目标	Mass	445.45	486.88	380.93

表9 代理模型预测和仿真模型评估对比

Table 9 Comparison of surrogate model prediction and simulation model evaluation

工况类型及优化目标		RDS-GP		GP		ANN
工况类型及优化目标		代理模型	仿真模型	代理模型	仿真模型	代理模型	仿真模型
冲击工况	Str	75.65	73.19	70.20	87.27	93.81	82.50
	Dis	2.28	2.26	2.00	2.39	1.92	2.51
制动工况	Str	62.84	60.02	58.04	59.84	73.47	69.59
	Dis	1.71	1.71	1.56	1.87	1.66	1.94
转弯工况	Str	62.84	59.59	60.33	59.32	71.65	69.28
	Dis	1.69	1.69	1.42	1.84	1.46	1.90
弯曲工况	Str	73.36	66.06	61.57	65.61	76.44	75.32
	Dis	2.03	2.01	1.80	2.15	2.04	2.27
扭转1轮	Str	105.83	90.63	91.22	101.24	97.81	102.84
工况	Dis	3.14	3.22	3.76	3.27	3.05	3.60
扭转2轮	Str	125.83	121.06	102.41	118.58	149.44	143.40
工况	Dis	3.92	3.93	3.89	3.90	4.01	4.81
射击工况	Str	66.76	68.44	68.67	67.75	90.39	78.45
	Dis	2.01	2.01	1.90	2.15	1.47	2.24
优化目标	Mass	445.45	445.44	486.88	485.46	380.93	379.60

表10 最优方案统计对比

Table 10 Statistical comparison of optimal schemes

参数变量及优化目标	RDS-GP		GP
参数变量及优化目标	最优方案均值	最优方案方差	最优方案均值	最优方案方差
L	3620.00	0.00	3635.61	1764.81
W	1300.00	0.00	1336.01	1135.66
H	620.00	0.00	708.92	691.78
L₁	60.00	0.00	64.53	5.13
W₁	51.32	1.10	48.76	1.29
L₂	51.32	1.10	48.76	1.29
W₂	36.77	0.13	39.29	0.14
L₃	36.77	0.13	39.29	0.14
W₃	15.85	3.93	14.01	2.53
T	5.00	0	5.14	0.07
M	碳素钢	碳素钢	碳素钢	碳素钢
Mass	446.35		489.78

图10 迭代次数对比

Fig.10 Comparison of iteration times

表11 桁架车身结构静力学仿真校验

Table 11 Static simulation check of truss body structure

工况类型	最大应力/MPa	最大位移/mm
冲击工况	71.78	2.25
制动工况	58.90	1.70
转弯工况	58.46	1.68
弯曲工况	64.80	2.00
扭转1轮工况	89.94	3.20
扭转2轮工况	120.66	3.91
射击工况	67.15	2.00

图11 桁架车身结构静力学仿真分析(放大46倍)

Fig.11 Static simulation analysis of truss body structure (46×)

表12 桁架车身结构动力学仿真校验

Table 12 Dynamic simulation check of truss body structure

阶数	固有频率/Hz
1	37.24
2	60.29
3	61.42
4	71.13
5	81.35
6	89.39
7	91.34
8	95.58
9	99.59
10	102.99

图12 桁架车身结构模态仿真分析(放大20倍)

Fig.12 Modal simulation analysis of truss body structure (20×)

参考文献 25

[1]	ZHU T J, WU Y, OUYANG Z, et al. Feasibility of approximate model optimization for lightweight design of vehicle body structure based on sequential quadratic programming algorithm[J]. Sensors and Materials, 2022, 34(9):3581-3591. doi: 10.18494/SAM4018 URL
[2]	张峰, 程建勇, 董立强, 等. 基于SFECONCEPT的某平台改款车型车身结构轻量化设计[J]. 机械设计, 2022, 39(11):106-111.
	ZHANG F, CHENG J Y, DONG L Q, et al. Lightweight design of body structure of a platform’s modified vehicle based on SFE CONCEPT[J]. Journal of Machine Design, 2022, 39(11): 106-111. (in Chinese)
[3]	XU S. Use of topology design exploration and parametric shape optimization process to development highly efficient and lightweight vehicle body structure[C]// Proceedings of GM Global CAE Conference. Detroit,MI,US: General Motors Corporation, 2007: 29-36.
[4]	PAN F, ZHU P, ZHANG Y. Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression[J]. Computers & Structures, 2010, 88(1/2): 36-44. doi: 10.1016/j.compstruc.2009.07.008 URL
[5]	MICHELL A G M. LVIII. The limits of economy of material in frame-structures[J]. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 1904, 8(47):589-597.
[6]	王显会, 许刚, 李守成, 等. 特种车辆车架结构拓扑优化设计研究[J]. 兵工学报, 2007, 28(8):903-908.
	WANG X H, XU G, LI S C, et al. Research of topoIogy optimization in the design of the chassis frame of special vehicles[J]. Acta Armamentarii, 2007, 28(8):903-908. (in Chinese)
[7]	LU S B, MA H G, XIN L, et al. Lightweight design of bus frames from multi-material topology optimization to cross-sectional size optimization[J]. Engineering Optimization, 2019, 51(6): 961-977. doi: 10.1080/0305215X.2018.1506770 URL
[8]	JUNG Y S, LIM S H, KIM J M, et al. Lightweight design of electric bus roof structure using multi-material topologyoptimisation[J]. Structural and Multidisciplinary Optimization, 2020, 61:1273-1285. doi: 10.1007/s00158-019-02410-8
[9]	TORSTENFELT B, KLARBRING A. Conceptual optimal design of modular car product families using simultaneous size, shape and topology optimization[J]. Finite Elements in Analysis & Design, 2007, 43(14):1050-1061.
[10]	LI S Q, FENG X Y. Study of structural optimization design on a certain vehicle body-in-white based on static performance and modal analysis[J]. Mechanical Systems and Signal Processing, 2020, 135:106405. doi: 10.1016/j.ymssp.2019.106405 URL
[11]	温晶晶, 吴斌, 刘承骛. 导弹整体式翼面骨架结构的拓扑优化设计[J]. 兵工学报, 2017, 38(1):81-88. doi: 10.3969/j.issn.1000-1093.2017.01.011
	WEN J J, WU B, LIU C W. Topology optimization design for frame structure of monolithic wing of missile[J]. Acta Armamentarii, 2017, 38(1): 81-88. (in Chinese)
[12]	DUAN L, XIAO N C, HU Z, et al. An efficient lightweight design strategy for body-in-white based on implicit parameterization technique[J]. Structural & Multidisciplinary Optimization, 2017, 55(5):1927-1943.
[13]	闫闯. 基于局部拓扑优化技术的纯电动汽车车架轻量化研究[D]. 成都: 电子科技大学, 2019:6-16.
	YAN C. Research on lightweight of pure electric vehicle frame based on local topology optimization technology[D]. Chengdu: University of Electronic Science and Technology of China, 2019:6-16. (in Chinese)
[14]	扶原放, 金达锋, 乔蔚炜. 多工况下微型电动车车身结构拓扑优化设计[J]. 机械设计, 2010(2):77-80.
	FU Y F, JIN D F, QIAO W W. Topological optimization design on the body structure of mini electric cars under multi-working conditions[J]. Journal of Machine Design, 2010(2):77-80. (in Chinese)
[15]	兰凤崇, 赖番结, 陈吉清, 等. 考虑动态特性的多工况车身结构拓扑优化研究[J]. 机械工程学报, 2014, 50(20):122-128. doi: 10.3901/JME.2014.20.122
	LAN F C, LAI P J, CHEN J Q, et al. Multi-case topology optimization of body structure considering dynamic characteristic[J]. Journal of Mechanical Engineering, 2014, 50(20):122-128. (in Chinese) doi: 10.3901/JME.2014.20.122
[16]	邹坤, 侯亮, 卜祥建, 等. 基于工况风险评估的叉车门架多工况拓扑优化[J]. 中国机械工程, 2019, 30(5):568-576,577.
	ZOU K, HOU L, BU X J, et al. Multi-working condition topology optimization of forklift door frames based on working condition risk assessments[J]. China Mechanical Engineering, 2019, 30(5):568-576,577. (in Chinese)
[17]	SHA L S, LIN A D, XI Q, et al. A topology optimization method for robot light-weight design under multi-working conditions and its application on upper-limb powered exoskeleton[C]// Proceedings of 2020 International Conference on Artificial Intelligence and Electromechanical Automation.Tianjin,China:IEEE, 2020:17-22.
[18]	LIEU Q X, NGUYEN K T, DANG K D, et al. An adaptive surrogate model to structural reliability analysis using deep neural network[J]. Expert Systems with Applications, 2022, 189:116104. doi: 10.1016/j.eswa.2021.116104 URL
[19]	JIA L Y, ALIZADEH R, HAO J, et al. A rule-based method for automated surrogate model selection[J]. Advanced Engineering Informatics, 2020, 45:101123. doi: 10.1016/j.aei.2020.101123 URL
[20]	GARG A, BELARBI M O, TOUNSI A, et al. Predicting elemental stiffness matrix of FG nanoplates using Gaussian Process Regression based surrogate model in framework of layerwise model[J]. Engineering Analysis with Boundary Elements, 2022, 143:779-795. doi: 10.1016/j.enganabound.2022.08.001 URL
[21]	谢然, 兰凤崇, 陈吉清, 等. 满足可靠性要求的轻量化车身结构多目标优化方法[J]. 机械工程学报, 2011, 47(4):117-124.
	XIE R, LAN F C, CHEN J Q, et al. Multi-objective optimization method in car-body structure light-weight design with reliability requirement[J]. Journal of Mechanical Engineering, 2011, 47(4):117-124. (in Chinese)
[22]	史国宏, 陈勇, 杨雨泽, 等. 白车身多学科轻量化优化设计应用[J]. 机械工程学报, 2012, 48(8):110-114.
	SHI G H, CHEN Y, YANG Y Z, et al. BIW architecture multidisciplinary light weight optimization design[J]. Journal of Mechanical Engineering, 2012, 48(8):110-114. (in Chinese)
[23]	XIONG F, ZOU X H, ZHANG Z G, et al. A systematic approach for multi-objective lightweight and stiffness optimization of a car body[J]. Structural and Multidisciplinary Optimization, 2020, 62(6):3229-3248. doi: 10.1007/s00158-020-02674-5
[24]	XIONG F, WANG D F, MA Z D, et al. Structure-material integrated multi-objective lightweight design of the front end structure of automobile body[J]. Structural and Multidisciplinary Optimization, 2018, 57(2):829-847. doi: 10.1007/s00158-017-1778-1 URL
[25]	ZHANG Y, XU X J. Relative cooling power modeling of lanthanum manganites using Gaussian process regression[J]. RSC Advances, 2020, 10(35):20646-20653. doi: 10.1039/d0ra03031g pmid: 35517747