Article(id=1157001743847481561, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001742186533107, articleNumber=null, orderNo=null, doi=10.19562/j.chinasae.qcgc.2024.08.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1710518400000, receivedDateStr=2024-03-16, revisedDate=1713369600000, revisedDateStr=2024-04-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1753780311786, onlineDateStr=2025-07-29, pubDate=1724515200000, pubDateStr=2024-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753780311786, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753780311786, creator=13701087609, updateTime=1753780311786, updator=13701087609, issue=Issue{id=1157001742186533107, tenantId=1146029695717560320, journalId=1146120084050784272, year='2024', volume='46', issue='8', pageStart='1335', pageEnd='1536', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=0, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1753780311389, creator=13701087609, updateTime=1756792467091, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1169635638933467651, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001742186533107, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1169635638933467652, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1157001742186533107, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1501, endPage=1510, ext={EN=ArticleExt(id=1157001744514375901, articleId=1157001743847481561, tenantId=1146029695717560320, journalId=1146120084050784272, language=EN, title=Optimization and Decomposition of Virtual Iteration Target Spectrum for Cabin of Commercial Vehicle, columnId=null, journalTitle=Automotive Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The load spectrum of commercial vehicle cab assembly is the key factor affecting the accuracy and computational efficiency of virtual fatigue prediction. In this article, key links such as road load spectrum collection and editing, high fidelity dynamic modeling, and virtual iteration are explored, in order to obtain accurate and efficient external point time-domain loads from the engineering application perspective. Firstly, the full path road load spectrum of the driver's cab assembly is collected from the actual vehicle in the test field, and the original data is normalized, split, and reassembled considering random errors to obtain a statistically strong total damage target in the test field. Then, using the principle of equal damage, 9 operating conditions and their number of cycles are optimized, which not only controls the error within 10%, but also increases the efficiency by 75%. Subsequently, based on the performance parameters of the measured damping components, a high fidelity rigid flexible coupling dynamic model of the cab is established, and the accuracy of the model is verified through a 7-channel road simulation bench in the cab. Finally, the load decomposition of the optimal operating conditions is completed through virtual iteration, with an iteration error of less than 10%. Based on the above optimization and decomposition of the external connection point load, the virtual fatigue calculation of the cab body is efficiently completed, and the failure of the cab welding points is accurately predicted, which has a high degree of consistency with the durability test results of the road simulation bench, providing strong technical support for the design and optimization of commercial vehicle cabins.

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商用车驾驶室总成载荷谱是影响虚拟疲劳预测精度与计算效率的关键。本文探究了道路载荷谱采集与编辑、高保真动力学建模以及虚拟迭代等重点环节,以期从工程应用角度获取准确、高效的外联点时域载荷。首先,在试验场实车采集了驾驶室总成全路径道路载荷谱,考虑随机误差对原始数据进行归一化拆分并重组,得到统计性较强的试验场总损伤目标;然后,利用等效损伤原则优选出9段工况及其循环数,不仅将误差控制在10%以内,同时时效提高了75%;随后,结合实测减振元件的性能参数,建立了高保真驾驶室刚柔耦合动力学模型,并通过驾驶室7通道道路模拟台架验证了模型精度;最后,通过虚拟迭代完成了优选工况的载荷分解,迭代误差小于10%。基于上述优选并分解得到的外联点载荷,高效完成了驾驶室本体虚拟疲劳计算,并准确预测了驾驶室焊点失效情况,与道路模拟台架耐久试验结果有较高的吻合度,为商用车驾驶室的设计和优化提供了有力的技术支撑。

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李安海,博士,副教授,E-mail:
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传感器类型 位置 传感器 通道
三向加速度 车架侧悬置点 4 12
三向加速度 驾驶室侧悬置点 4 12
三向加速度 驾驶室顶部 3 9
拉线位移 悬置位移 4 4
剪切应变 悬置翻转轴 1 1
单向应变 驾驶室本体 3 3
单向应变 悬置支架 4 4
小计 23 45
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测点及载荷类型

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传感器类型 位置 传感器 通道
三向加速度 车架侧悬置点 4 12
三向加速度 驾驶室侧悬置点 4 12
三向加速度 驾驶室顶部 3 9
拉线位移 悬置位移 4 4
剪切应变 悬置翻转轴 1 1
单向应变 驾驶室本体 3 3
单向应变 悬置支架 4 4
小计 23 45
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工况 强化路面循环 60 km/h紧急制动
测试组 01 02 03 01 02 03
满载 603 603 603 233 233 233
半载 301 301 301 77 77 77
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各组数据配比系数

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工况 强化路面循环 60 km/h紧急制动
测试组 01 02 03 01 02 03
满载 603 603 603 233 233 233
半载 301 301 301 77 77 77
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序号 通道 损伤目标
1 左前悬置/位移 1.18E-10
2 右前悬置/位移 1.06E-10
3 左后悬置/位移 2.31E-11
4 右后悬置/位移 2.00E-11
5 车架左前/加速度/x 1.34E-16
6 车架左前/加速度/y 1.43E-15
7 车架左前/加速度/z 5.58E-15
8 驾驶室底左前加速度/z 7.65E-18
9 驾驶室底右前/加速度/z 6.71E-18
10 驾驶室底左后/加速度/z 4.92E-18
11 驾驶室底右后/加速度/z 6.37E-18
12 驾驶室顶左前/加速度/z 1.15E-17
13 驾驶室顶右前/加速度/z 1.16E-17
14 驾驶室顶左后/加速度/z 2.13E-17
15 驾驶室顶右后/加速度/z 2.71E-17
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试验场总损伤目标值

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序号 通道 损伤目标
1 左前悬置/位移 1.18E-10
2 右前悬置/位移 1.06E-10
3 左后悬置/位移 2.31E-11
4 右后悬置/位移 2.00E-11
5 车架左前/加速度/x 1.34E-16
6 车架左前/加速度/y 1.43E-15
7 车架左前/加速度/z 5.58E-15
8 驾驶室底左前加速度/z 7.65E-18
9 驾驶室底右前/加速度/z 6.71E-18
10 驾驶室底左后/加速度/z 4.92E-18
11 驾驶室底右后/加速度/z 6.37E-18
12 驾驶室顶左前/加速度/z 1.15E-17
13 驾驶室顶右前/加速度/z 1.16E-17
14 驾驶室顶左后/加速度/z 2.13E-17
15 驾驶室顶右后/加速度/z 2.71E-17
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序号 路面 循环次数
1 满载(01)-扭曲路 2 000
2 满载(01)-振动路 2 165
3 满载(02)-坑洼路 1 337
4 满载(03)-振动路 2 250
5 满载(01)-紧急制动 2 231
6 半载(02)-坑洼路 4 410
7 半载(01)-水泥破损路 13 254
8 半载(03)-坑洼路 4 388
9 半载(02)-比利时路 7 662
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等效路面优选结果

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序号 路面 循环次数
1 满载(01)-扭曲路 2 000
2 满载(01)-振动路 2 165
3 满载(02)-坑洼路 1 337
4 满载(03)-振动路 2 250
5 满载(01)-紧急制动 2 231
6 半载(02)-坑洼路 4 410
7 半载(01)-水泥破损路 13 254
8 半载(03)-坑洼路 4 388
9 半载(02)-比利时路 7 662
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序号 位置/类型/方位 序号 位置/类型/方位
1 左前悬置/位移 13 车架右后加速度/z
2 右前悬置/位移 14 驾驶室底左前加速度/z
3 左后悬置/位移 15 驾驶室底右前/加速度/z
4 右后悬置/位移 16 驾驶室底左后/加速度/z
5 车架左前/加速度/x 17 驾驶室底右后/加速度/z
6 车架左前/加速度/y 18 驾驶室顶左前/加速度/z
7 车架左前/加速度/z 19 驾驶室顶右前/加速度/z
8 车架右前/加速度/x 20 驾驶室顶左后/加速度/z
9 车架右前/加速度/z 21 驾驶室顶右后/加速度/z
10 车架左后/加速度/x 22 驾驶室顶左前/加速度/x
11 车架左后/加速度/y 23 驾驶室顶右前/加速度/x
12 车架左后/加速度/z
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驾驶室迭代通道

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序号 位置/类型/方位 序号 位置/类型/方位
1 左前悬置/位移 13 车架右后加速度/z
2 右前悬置/位移 14 驾驶室底左前加速度/z
3 左后悬置/位移 15 驾驶室底右前/加速度/z
4 右后悬置/位移 16 驾驶室底左后/加速度/z
5 车架左前/加速度/x 17 驾驶室底右后/加速度/z
6 车架左前/加速度/y 18 驾驶室顶左前/加速度/z
7 车架左前/加速度/z 19 驾驶室顶右前/加速度/z
8 车架右前/加速度/x 20 驾驶室顶左后/加速度/z
9 车架右前/加速度/z 21 驾驶室顶右后/加速度/z
10 车架左后/加速度/x 22 驾驶室顶左前/加速度/x
11 车架左后/加速度/y 23 驾驶室顶右前/加速度/x
12 车架左后/加速度/z
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商用车驾驶室虚拟迭代目标谱优化与分解
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王凯 1, 2 , 张宗阳 1, 2 , 邴涛 1, 2 , 崔云龙 2 , 孙士涛 2 , 李安海 1
汽车工程 | 2024,46(8): 1501-1510
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汽车工程 | 2024, 46(8): 1501-1510
商用车驾驶室虚拟迭代目标谱优化与分解
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王凯1, 2, 张宗阳1, 2, 邴涛1, 2, 崔云龙2, 孙士涛2, 李安海1
作者信息
  • 1. 山东大学机械工程学院,高效洁净机械制造教育部重点实验室,济南 250061
  • 2. 中国重型汽车集团汽车研究总院,济南 250100

通讯作者:

李安海,博士,副教授,E-mail:
Optimization and Decomposition of Virtual Iteration Target Spectrum for Cabin of Commercial Vehicle
Kai Wang1, 2, Zongyang Zhang1, 2, Tao Bing1, 2, Yunlong Cui2, Shitao Sun2, Anhai Li1
Affiliations
  • 1. School of Mechanical Engineering,Shandong University,Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE,Jinan  250061
  • 2. Institute of Automobile Research,China National Heavy Duty Truck Group Co. ,Ltd. ,Jinan  250100
出版时间: 2024-08-25 doi: 10.19562/j.chinasae.qcgc.2024.08.016
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商用车驾驶室总成载荷谱是影响虚拟疲劳预测精度与计算效率的关键。本文探究了道路载荷谱采集与编辑、高保真动力学建模以及虚拟迭代等重点环节,以期从工程应用角度获取准确、高效的外联点时域载荷。首先,在试验场实车采集了驾驶室总成全路径道路载荷谱,考虑随机误差对原始数据进行归一化拆分并重组,得到统计性较强的试验场总损伤目标;然后,利用等效损伤原则优选出9段工况及其循环数,不仅将误差控制在10%以内,同时时效提高了75%;随后,结合实测减振元件的性能参数,建立了高保真驾驶室刚柔耦合动力学模型,并通过驾驶室7通道道路模拟台架验证了模型精度;最后,通过虚拟迭代完成了优选工况的载荷分解,迭代误差小于10%。基于上述优选并分解得到的外联点载荷,高效完成了驾驶室本体虚拟疲劳计算,并准确预测了驾驶室焊点失效情况,与道路模拟台架耐久试验结果有较高的吻合度,为商用车驾驶室的设计和优化提供了有力的技术支撑。

载荷谱加速编辑  /  损伤等效  /  虚拟迭代  /  载荷提取  /  虚拟疲劳

The load spectrum of commercial vehicle cab assembly is the key factor affecting the accuracy and computational efficiency of virtual fatigue prediction. In this article, key links such as road load spectrum collection and editing, high fidelity dynamic modeling, and virtual iteration are explored, in order to obtain accurate and efficient external point time-domain loads from the engineering application perspective. Firstly, the full path road load spectrum of the driver's cab assembly is collected from the actual vehicle in the test field, and the original data is normalized, split, and reassembled considering random errors to obtain a statistically strong total damage target in the test field. Then, using the principle of equal damage, 9 operating conditions and their number of cycles are optimized, which not only controls the error within 10%, but also increases the efficiency by 75%. Subsequently, based on the performance parameters of the measured damping components, a high fidelity rigid flexible coupling dynamic model of the cab is established, and the accuracy of the model is verified through a 7-channel road simulation bench in the cab. Finally, the load decomposition of the optimal operating conditions is completed through virtual iteration, with an iteration error of less than 10%. Based on the above optimization and decomposition of the external connection point load, the virtual fatigue calculation of the cab body is efficiently completed, and the failure of the cab welding points is accurately predicted, which has a high degree of consistency with the durability test results of the road simulation bench, providing strong technical support for the design and optimization of commercial vehicle cabins.

accelerated editing of load spectrum  /  damage equivalence  /  virtual iteration  /  load extraction  /  virtual fatigue
王凯, 张宗阳, 邴涛, 崔云龙, 孙士涛, 李安海. 商用车驾驶室虚拟迭代目标谱优化与分解. 汽车工程, 2024 , 46 (8) : 1501 -1510 . DOI: 10.19562/j.chinasae.qcgc.2024.08.016
Kai Wang, Zongyang Zhang, Tao Bing, Yunlong Cui, Shitao Sun, Anhai Li. Optimization and Decomposition of Virtual Iteration Target Spectrum for Cabin of Commercial Vehicle[J]. Automotive Engineering, 2024 , 46 (8) : 1501 -1510 . DOI: 10.19562/j.chinasae.qcgc.2024.08.016
商用车使用场景多变且路面冲击较大,行车时驾驶室总成承受复杂的交变载荷,驾驶室本体特别是焊点及焊缝易出现疲劳失效,对整车可靠性、安全性等产生直接影响1-3。随着虚拟仿真与测试技术的发展,在驾驶室结构设计阶段进行虚拟疲劳仿真已得到广泛应用,如何提高仿真精度与效率已成为主要研究热点,输入载荷谱真实性、循环规则时效性是关键影响因素4-7
商用车驾驶室悬置连接结构复杂,用于虚拟疲劳仿真的各连接点载荷谱无法通过测试直接获取,通常采用约束加载法、虚拟迭代法以及虚拟试验场法3种方法获取输入载荷谱。荣兵等8建立了某试验场共振路3D虚拟路面,驱动整车简化动力学模型获取悬架部件道路载荷谱并用于虚拟疲劳仿真,经与试验对比证明该方法具备一定的工程实用性。董国疆等9以底盘零部件载荷谱提取精度为研究目标,采用虚拟迭代和约束加载等方法对多种振动台工况进行仿真、验证,得到虚拟迭代约束加载具备应用范围广、精度高但耗时长的特点。龙岩等10通过改进估计算法修正系统传递函数矩阵后进行虚拟迭代,获取了更加准确的车身疲劳载荷,从而提高了仿真精度。马扎根等11搭建了虚拟试验台并通过虚拟迭代完成实测道路载荷谱提取,在设计阶段实现了白车身疲劳寿命有效预估与结构改进,仿真与试验结果基本一致,实现了结构寿命有效预估。此外,通常商用车耐久循环规范要求验证里程较长,输入载荷直接使用会导致仿真效率较低。花菲菲等12以测试载荷谱为基础,通过分析比较几种主要的编谱方法,发现时域损伤编辑法可在载荷损伤特征保留的同时,有效实现载荷谱加速。葛文韬等13基于时域损伤编辑方法对载荷数据进行缩减处理,单循环里程降低约46%,实现动态载荷仿真效率提升。于佳伟等14基于载荷谱的数值统计特征构造最小标准差组合谱,综合考虑实测道路载荷随机因素干扰并实现载荷谱加速。综上,为了实现驾驶室总成虚拟疲劳精度与效率提升,须系统性考虑载荷采集与编辑、动力学建模、载荷提取等环节对整体精度、时效的影响,获取高保真输入载荷及高时效循环规则。
本文以某型6×4重型牵引车驾驶室总成为研究对象。首先,实车采集耐久试验规范规定的各工况载荷谱,优选确定具有统计意义的试验场损伤目标,在等效损伤原则下通过时域数据编辑技术制定等效加速谱;然后,基于驾驶室总成及相关弹性元件实测参数,建立含柔性驾驶室、柔性半车架的高精度刚柔耦合动力学模型;最后,通过虚拟迭代法完成载荷分解与提取,基于试验场损伤目标优化循环规则,获取得到输入载荷谱用于驾驶室虚拟疲劳仿真,与耐久台架试验对比验证整体仿真精度。
商用车驾驶室总成载荷环境较为复杂,为了全面描述商用车驾驶室总成振动及姿态,按照“激励-传递-响应”全路径确定测点及载荷类型15。测试方案共计23个测点、45个通道载荷,详见表1。各加速度方向按照整车三维坐标确定,且后续动力学建模保持一致。
按照布置方案安装各传感器,同步搭载GPS测量车速及位置信息,如图1所示。测试通道大致分为目标通道、监控通道两类:目标通道主要为车架侧悬置点速度、驾驶室侧悬置点加速度、悬置位移,是虚拟迭代主要控制目标;监控通道主要为部分驾驶室顶部加速度、翻转轴应变等,用于联合目标通道全面评估迭代精度。
在某大型整车综合试验场内,按照耐久循环规范采集载荷谱。测试道路包含强化路面(坑洼路、比利时路、振动路、正弦波路、扭曲路等24种)以及性能路(60 km/h紧急制动等工况),测试前按照GB/T 12534—1990《汽车道路试验方法通则》调整车辆胎压、配载等至标准状态,分别在满载、半载按照对应配速测试,每组至少保证3组有效数据。
原始载荷谱包括12组数据:满载、半载强化路面循环各3组,以及满载、半载紧急制动各3组。以3组驾驶室侧左前悬置点在满载状态下强化路面的垂向加速度为例,整体载荷曲线较为平滑、各强化路面时长接近且幅值处于相同水平,各组数据重复性较好,如图2所示。对原始数据进行滤波、重采样等处理,得到可用于计算分析的试验场基础载荷谱。
考虑重复测量间细微差异,根据耐久循环规范进行归一化处理、组合得到具有统计代表性的目标损伤值。首先,确定12组数据响应循环数。假设该整车试验场强化耐久试验规范里程公里数为 L,单圈公里数为 L 1,强化路面行驶总圈数为 r ' = L / L 1,同时根据规范确定制动总次数为 r ' ',得到满载、半载配载下行驶圈数比例。
r f u l l ' : r h a l f ' = p 1 L L 1 : p 2 L L 1
r f u l l : r h a l f = p 1 r : p 2 r
式中: p 1 p 2分别为满载、半载配比,分别设定为70%、30%:紧急制动次数 r 设定为1 000次。
按照耐久循环规范,分配12组载荷谱循环数,通过式(3)式(4)得到强化路面循环、紧急制动循环配比系数 R ' R ,详见表2
R ' = p 1 L 3 L 1 , p 1 L 3 L 1 , p 1 L 3 L 1 , p 2 L 3 L 1 , p 2 L 3 L 1 , p 2 L 3 L 1 T
R = p 1 r ' ' 3 , p 1 r ' ' 3 , p 1 r ' ' 3 , p 2 r ' ' 3 , p 2 r ' ' 3 , p 2 r ' ' 3 T
随后,计算试验场基础载荷谱相对损伤值。相对损伤是表示载荷损伤严重程度的无量纲参数,将时域数据变换为雨流域后,参照材料S-N曲线原理插值得到相对损伤序列,按照疲劳线性累计准则累加得到相对损伤,通过式(5)计算相对损伤 d。值得注意的是,为了更好地表征载荷谱疲劳特性,速度、加速度信号雨流变换前须进行1、2次积分,应变、力、位移等可直接变换。
d = i = 1 n S i β
式中: S i为雨流域载荷幅值; β为等效疲劳强度指数,按照车辆行业惯例取5。
最后,按照对应的配比系数得到试验场总体损伤目标。计算12组载荷数据相对损伤值,构建矩阵 A,按照表2中对应的配比系数,通过式(6)得到驾驶室总成试验场耐久循环规范所对应的最具代表性的试验场损伤矩阵 D,主要载荷通道相对损伤目标见表3
A R = d 11 d 12 d 1 n d 21 d 22 d 2 n d m 1 d m 2 d m n r 1 r 2 r n = D 1 D 2 D m
式中: d m n为试验场第 n组测试中载荷通道 m的相对损伤; r n为第 n组测试配比系数; D m为载荷通道 m目标总损伤。
为提高仿真计算及道路模拟台架试验效率,在损伤等效原则下对原始载荷谱进行加速编辑16。原始12组载荷谱按路面、工况进行切分成150段工况数据,其中强化路面满载、半载各72段,制动满载、半载各3段,按照式(7)计算150段载荷数据相对损伤并构建矩阵 B,在对应的循环矩阵 N求得等效加速度损伤矩阵 F
B N = f 11 f 12 f 1 o f 21 f 22 f 2 o f m 1 f m 2 f m o n 1 n 2 n o = F 1 F 2 F m
式中: f m o为第 o段工况中载荷通道 m的相对损伤; n o为第 o段工况数据的目标循环数( o = 150); F m为等效加速谱载荷通道 m损伤值。
在损伤等效原则下以缩短载荷周期为目标,求解各片段载荷循环矩阵 N。基本上,等效加速谱主要通道相对损伤较试验场目标总损伤比值控制在0.8~1.2,总时长从原始800压缩至在250 h以内,具体为
0.8 F i D i 1.2
1 o n t i t i 250   h
式中: F i为等效加速谱主要通道 i相对损伤; D i为原始信号通道 i目标损伤值; n i t i分别为150段工况循环数和时长。
更进一步,以最小损伤误差、最短周期等为目标求解最优工况组合,具体为
m i n 1 i F i D i - 1 2 1 o n i t i 1 o n i
式中: 1 i F i D i - 1 2为各通道损伤与试验场目标总损伤的比值趋近于1; 1 o n i t i为优化组合路段时长与循环数乘积最小,即总时间最短; 1 o n i 表示 R中非零值数目最小,即选择出最少的路段进行损伤等效。
首先去除150段工况中损伤较小路面,其次综合考虑驾驶室载荷特性,设置扭曲路及制动循环次数不小于2 000次,最终确定目标时长200 h,经多次迭代优化,共选出9段路面及对应的循环次数,详见表4
试验场等效加速谱主要通道相对损伤较试验场目标总损伤比值 F i / D i接近1,表明路面片段组合优化、循环次数优化合理有效。图3为主要通道相对损伤比。由图可见:车架侧悬置点加速度、悬置位移、驾驶室侧悬置点加速度等关键位置相对损伤比均位于0.9~1.1范围内,表明加速后的等效载荷谱较好地保留了试验场损伤;各工况载荷特性不同,对不同通道损伤贡献占比差异较大,振动路、坑洼路、比利时路等主要为大幅振动、大幅位移,对驾驶室振动损伤贡献较高;扭曲路、紧急制动工况等主要为低幅低频振动、大幅位移,对悬置位移通道的损伤贡献较高。另外,相较于耐久循环规范包含的有效时长870 h,等效谱加速的214 h效果显著,有效保证后续虚拟迭代及疲劳仿真效率提升。
高精度的驾驶室总成多体动力学模型是保证虚拟迭代精度的基础。通常情况下,商用车驾驶室动力学模型采用含质量、转动惯量及惯性积的质心点,无法考虑驾驶室本体的柔性变形的影响。
为了建立高精度动力学模型,本文建立包括较为完整内外饰的驾驶室总成有限元模型,提取柔性体模态中性文件用于动力学建模。驾驶室本体板壳件主要采用5 mm四边形网格单元模拟,同步对特性复杂部分进行局部精细划分,检查网格雅可比、长宽比、翘曲度等参数,保证网格质量满足要求。焊点采用Spot类型单元,如图4(a)所示,包括外核(outer-nugget)、内核(inner-nugget)、梁单元(beam)3部分,焊核直径设置为6 mm;焊缝采用Shell单元模拟,焊缝单元与其连接结构单元须保证垂直;螺栓采用beam+rbe2单元模拟,螺栓孔周围添加washer避免应力集中;胶粘单元采用hexa+rbe3单元模拟。有限元TB驾驶室(Trimmed Body,含白车身、顶盖、车门、内外饰等)模型如图4(b)所示,模型单元1 571 842个、节点1 281 103个、总质量1 223 kg。
图5所示,通过质心惯量试验台获取驾驶室总成质量相关信息。台架测试的总质量1 190 kg,较有限元误差约3%,转动惯量、质心位置等其他关键参数误差均控制在5%以内,表明有限元模型与实际吻合度较高。
此外,考虑车架柔性变形对系统动力学响应特性的影响,截取安装驾驶室悬置前半部分车架建立有限元模型,提取柔性体模态中性文件用于动力学建模。柔性体驾驶室和车架如图6所示。
弹性元件的特性参数是影响动力学模型精度关键因素,主要包括空气弹簧、衬套、减振器、限位块等。图7为相关弹性元件性能曲线,各参数均通过台架试验测量得到,有效保证动力学模型的准确性。
基于上述内容,在Adams软件中建立包含柔性驾驶室、柔性车架、驾驶室悬置的高精度刚柔耦合多体动力学模型,如图8所示。
为了有效模拟驾驶室总成在行车过程中的复杂动态响应,在其空间上设有6个刚体自由度,此外车架柔性变形会通过悬置影响驾驶室载荷,需要在车架上建立至少7通道位移驱动,实现驾驶室总成在行车中受到垂向、横向、纵向3个方向冲击载荷的运动姿态。如图9所示,位移驱动包括4个垂向作动器、2个横向作动器以及1个纵向作动器。
为校对驾驶室总成刚柔耦合多体动力学模型准确性,结合驾驶室7通道道路模拟耐久试验进行探究。将道路模拟台架迭代后的作动器位移可通过液压缸内部位移传感器测量得到,换算成动力学模型虚拟作动器位移,驱动动力学模型得到各点载荷响应数据,与台架相应测点实测数据对比,图10为7通道驾驶室道路模拟台架。
以长波路仿真与试验结果对比为例,对于车架端加速度、悬置位移、驾驶室端加速度全路径观测点,仿真结果与实测数据吻合度非常高,图11为悬置位移数据,表明驾驶室总成刚柔耦合多体动力学模型精度较高,为后续动态载荷分解提供良好基础。
虚拟迭代法是半解析载荷分解方法,其本质上是基于道路实测载荷数据,结合非线性动力学模型反求位移驱动的过程。以迭代所求的位移驱动动力学模型,伴随着可获取目标外联点的载荷数据,较好地平衡了精度与效率间的矛盾。
迭代前须辨识驾驶总成动力学系统模型,求解频率响应函数17-18。驾驶室的动力学模型的时域驱动和响应通道载荷数据表示为 x ( t ) y ( t ),对应的频域表示为 X ( ω ) Y ( ω ),通过式(11)求得频率响应函数(frequency response function)。
H m n = Y ( ω ) / X ( ω )
H m n = H 11 H 12 H 1 n H 21 H 22 H 2 n H m 1 H m 2 H m n
式中: n为驱动通道数量; m为响应通道数量。
为了准确描述驾驶室在不同路面中的运动姿态,综合考虑“车架激励-悬置传递-驾驶室响应”整体传递路径确定迭代通道:车架加速度、悬置位移、驾驶室底部加速度等13个信号为迭代目标通道,驾驶室顶部加速度等10个信号为迭代监控通道,详见表5
通过小幅白噪声信号驱动动力学模型得到系统响应,根据式(12)求解得到系统的传递函数 H m n并计算出传递函数的逆矩阵 H m n - 1。参考驾驶室总成实测载荷谱,生成频带0 ~ 50 Hz、幅值上限为5 mm的白噪声,如图12所示。
图13为得到的驾驶室总成传递函数 H m n - 1。由图可见,各曲线走势平滑,一致性较好,反映出驾驶室动力学模型有较高的精度。
以损伤等效原理得到的等效加速谱为目标,对各个特征路面进行虚拟迭代,使得动力学模型响应谱接近目标谱,获得驾驶室虚拟疲劳分析所需的外联点载荷数据。首先,基于目标信号 Y o ( t ),通过式(13)得到初始驱动谱 x 0 ( t )
x 0 ( t ) = F F T - 1 H m n - 1 F F T [ Y o ( t ) ]
随后,以初始的驱动谱 x 0 ( t )驱动动力学模型,得到第一次迭代响应谱 Y 1 ( ω ),同步通过式(14)计算与目标信号误差谱 ε 1
ε 1 = Y 1 ( t ) - Y 0 ( t )
最后,对误差谱 ε 1求逆,合理设置加权系数 a并得到下一步驱动谱 x i + 1 ( t ),循环上述过程,直到响应谱到达精度要求。
x i + 1 ( t ) = x i ( t ) + a F F T - 1 H m n - 1 F F T [ ε i ]
评价迭代精度过程中,在时域、频域进行对比的基础上,需要以相对损伤比值、均方根误差百分比进行定量评价:相对损伤比值目标、监控通道控制范围分别为0.5~2、0.2~5,均方根误差百分比小于15%。此外,由于不同的路面具有不同的几何特征,对应载荷幅值、频域特性差异较大,对各通道总体损伤贡献比重也不尽相同,须根据不同路面载荷特点对迭代质量进行一定的倾斜,总体上控制迭代结果与等效损伤目标一致性。
由于迭代工况较多,本文以冲击载荷大且方向复杂的振动路工况为例展示迭代结果,如图14所示。由图可见,经过11步迭代,动力学模型响应信号与目标信号高度吻合,且目标通道与监控通道的相对损伤比值均位于0.85~1.2之间。在不同特征路面迭代结果中的实测信号与迭代信号误差满足要求,进一步验证了整车动力学模型的精度,同时监测信号在时域与幅值的变化一致,亦满足精度要求。
根据不同特征路面迭代所获的响应信号,按照表4中各路面循环数,获得基于虚拟迭代模型的各通道总损伤,然后与目标信号损伤对比。图15为整体工况虚拟迭代相对损伤比值,车架侧悬置点加速度、悬置位移、驾驶室侧悬置点加速度等关键位置相对损伤比均位于0.8~1.2范围内,满足精度要求。
综合对比图3图15,各路面迭代结果的损伤值与试验场试验规范下的载荷谱相差不大,满足后续的载荷提取及疲劳分析要求。
驾驶室失效一般为高周疲劳,故采用名义应力法进行疲劳分析。疲劳损伤是一个不断累积的过程,其中应用最为广泛的为Miner线性累积理论19-20。根据载荷分解所得驾驶室外连点载荷、驾驶室单位力惯性释放有限元结果及材料S-N曲线,进行驾驶室虚拟疲劳仿真分析。
疲劳分析失效准则选择临界平面法,考虑应力梯度影响、平均应力影响及塑性变形影响,并通过Haigh图对平均应力进行修正。基于1.3节中优选确定的各等效路面及循环次数,驾驶室疲劳损伤位置分布如图16所示。
仿真结果显示,驾驶室本体损伤较小,损伤主要集中在焊点位置,其中座椅横梁右侧前端焊点A处损伤最大,疲劳损伤值为0.83,该处为座椅横梁、发动机罩及地板焊接处,是座椅载荷的主要传递路径。按照等效加速谱及循环数要求,进行室内7通道道路模拟台架耐久试验,台架试验进行214 h后,该处焊点及驾驶室其他位置均未发生失效。为验证仿真分析的准确性,继续进行台架试验并持续检查,台架试验约350 h后,该处焊点发生开裂,其他位置仍未失效,如图17所示。虚拟疲劳准确预测焊点开裂失效说明通过虚拟迭代得到的分解载荷具备较强的工程应用价值,另外可用于驾驶室总成其他零部件强度与疲劳仿真。
围绕商用车驾驶室总成的强度耐久性开发,从实际工程应用出发,系统性考虑试验场载荷谱采集、试验场总损伤确定及等效加速度编辑、高保真动力学建模、虚拟迭代、载荷提取与应用等关键环节,得到准确、高效的驾驶室总成外联点载荷并进行虚拟疲劳计算,准确预测失效位置,得到如下结论。
(1) 提出了一种基于损伤等效原理的路段选择及优化方法,考虑重复测量间随机误差,根据组合得到具有统计代表性的目标损伤值。从试验150段路面中选出9段路面并计算出相应的循环数。各通道总损伤与目标一致,相对损伤误差控制在10%以内,载荷时长从原始870 h压缩75%至214 h,有效提高了虚拟迭代及疲劳仿真效率。
(2) 利用驾驶室质心、转动惯量和驾驶室悬置连接件的实测数据,建立了以柔性体为主体较高精度刚柔耦合驾驶室多体动力学模型,为后续的虚拟迭代和载荷分解提供基础。通过迭代不同特征路面,各路面下迭代信号与目标信号时域及相对损伤误差较小,迭代精度高,满足了驾驶室载荷分解及疲劳分析的要求。
(3) 采用虚拟迭代方法成功预测驾驶室座椅横梁附近焊点为疲劳寿命危险位置,并通过台架试验验证,仿真与试验失效位置一致,失效里程接近,表明该方法的准确性,可为驾驶室的开发提供支持。
  • “十四五”国家重点研发计划项目(2022YFB2503502)
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2024年第46卷第8期
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doi: 10.19562/j.chinasae.qcgc.2024.08.016
  • 接收时间:2024-03-16
  • 首发时间:2025-07-29
  • 出版时间:2024-08-25
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  • 收稿日期:2024-03-16
  • 修回日期:2024-04-18
基金
“十四五”国家重点研发计划项目(2022YFB2503502)
作者信息
    1. 山东大学机械工程学院,高效洁净机械制造教育部重点实验室,济南 250061
    2. 中国重型汽车集团汽车研究总院,济南 250100

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李安海,博士,副教授,E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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