基于稳健与可靠性优化设计的轿车车身轻量化研究
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摘要
减轻汽车自重是节约能源和提高燃油经济性、减少环境污染的最基本途径之一。车身重量约占整车重量的40%左右，车身的轻量化对于整车的轻量化起着举足轻重的作用。在车身轻量化实际工程应用中，由于工艺制造、环境变化、人为操作等环节中的不确定性因素使得零部件的板厚及材料性能参数（屈服极限、强度极限、弹性模量等）与名义设计值之间存在一定的变差，这些随机变量的变差直接影响汽车的各项结构性能，致使轻量化方案缺乏可靠性，从而丧失实际工程可行性。本文针对现有车身结构形式，从轿车车身轻量化的参数设计角度，基于稳健与可靠性优化设计的理论方法，计及板厚、材料性能参数的变差及其对各项结构性能的影响，开展对轿车车身轻量化方法及工程应用的研究，主要研究工作及结论如下：
(1)复杂近似建模方法拟合结构耐撞性能指标的对比分析研究
车身结构复杂系统具有设计变量多、结构性能响应非线性强等特点，针对二次多项式响应面进行轿车耐撞性等强非线性性能指标的拟合建模远远不能满足精度与计算效率要求的问题，基于试验设计和近似拟合理论与方法，引入机器学习领域的新方法―――支持向量回归(Support vector regression, SVR)，围绕结构耐撞安全性能指标的拟合建模，从拟合精度与计算效率两方面，与移动最小二乘(Moving leastsquare, MLS)、人工神经网络(Artificial neural network, ANN)、克罗格(Kriging)、径向基函数(Radial basis function, RBF)四种常用近似拟合方法进行了对比、分析研究，旨在提供一种先进的适合建立车身强非线性结构性能指标的拟合方法。研究表明，支持向量回归方法在拟合精度及计算效率上具有明显的优势，更适合应用于建立车身强非线性结构性能指标的近似拟合模型。
(2)基于支持向量回归的车身结构性能指标自适应建模方法研究
为提高近似拟合模型在最优解处的预测精度，从而得到具有工程可实施性的优化设计方案，基于利用有限元仿真检验最优设计解的思想，针对车身结构性能指标拟合建模中的自适应反馈方法进行了研究。通过对具体检验内容的研究，提出了自适应反馈过程的重要判别依据，建立了基于支持向量回归的自适应拟合建模方法。依据建立的设计流程与步骤，针对车身前部结构轻量化的工程实例进行了研究，验证了该方法的可行性。基于支持向量回归的自适应建模方法利用有限元仿真分析手段对最优设计解进行检验，并将最优设计解的样本信息反馈至近似拟合模型，保证了最优设计解与仿真分析结果的一致性，从而有效提高了拟合模型预测最优设计解的精度，为稳健与可靠性优化设计提供了高精度的近似拟合建模方法。
(3)基于蒙特卡罗(MCS)的稳健与可靠性优化设计串行优化策略研究
针对目前稳健与可靠性优化设计中的传统双循环优化策略计算效率低、求解不稳定的问题，基于解耦优化与可靠性分析过程的思想，对串行的优化策略进行了研究。通过对约束界限移动步长及约束界限值计算方法的研究，实现了串行优化策略中的确定性优化约束边界自适应调整过程，建立了自适应的约束边界调整方法。提出了基于MCS的串行优化策略，并建立了具体实现步骤。经理论的数学案例以及典型的工程应用案例验证了该优化策略的可行性。基于MCS的串行优化策略摒弃了传统双循环优化策略低效率的劣势，避免了最大可能失效点(Most probable point, MPP)方法进行可靠性分析的多MPP问题，保证可靠度计算精度的同时，有效提高了优化求解效率，适合应用于复杂系统的工程稳健与可靠性优化设计问题，为车身轻量化方法及其工程应用研究奠定了基础。
(4)基于稳健与可靠性优化设计的轿车车身轻量化方法研究
针对轿车车身结构轻量化的特点，结合基于支持向量回归的自适应建模方法以及基于MCS的串行优化策略，对轿车车身轻量化方法进行了研究。提出了基于稳健与可靠性优化设计的轿车车身轻量化总体构架，并建立了具体流程与详细设计步骤，进行了整个轿车车身轻量化工程实例设计研究，得到了工程上可行的轻量化方案并应用于实车轻量化改进，验证了基于稳健与可靠性优化设计的轿车车身轻量化方法的可行性。该方法有效提高了轻量化设计解的工程可行性以及设计效率，为车身轻量化提供了可借鉴的方法。在此基础上，构建了基于稳健与可靠性优化设计的轿车车身轻量化软件平台，为实际工程应用提供了可操作性的设计工具。
Weight reduction of automobiles is a fundamental approach to realize fuel economyand environmental protection. The weight reduction in body structure plays a ratherimportant role in decreasing the weight of full vehicle, which results from the fact thatbody structure possesses about40%weight of full vehicle. In real engineering applications,the variation of gauge thicknesses and mechanical parameters of material (yield limit, ultratensile strength, Young’s modulus) caused by uncertainties including environmentalchange, manufacturing precision and individual difference of operators, affects directly thestructural performances of autobody structure. The lightweight design without consideringaforementioned variations may fail to meet the requirements of structural performances,which will lead to a lack of feasibility and reliability in a real engineering application. Thedissertation concentrates on the study of determining the parameters of the existingautobody structure topology, including sheet thicknesses and mechanical parameters ofmaterial, to achieve the lightweight design of autobody structure. Based on robust andreliability-based design optimization (RRBDO), the method for the lightweight design ofautobody structure, coupled with its engineering application, is investigated consideringthe variation of sheet thickness and mechanical parameters of material caused by variouskinds of uncertainties. The main research work and corresponding conclusions are listedas follows:
(1) Comparative study of complex metamodeling techniques for approximating
crashworthiness performance indicators
The lightweight design for autobody structure is generally considered as the designoptimization problem for a complex system, since autobody structure is characterized bystrong nonlinear structural performance responses and large number of random variables.Polynomial response surface frequently undergoes bad accuracy and efficiency when approximating the structural performance indicators. To overcome this difficulty, supportvector regression (SVR) that is a new technique in machine learning is introduced toapproximate the crashworthiness performance indicators of autobody structure. Thecomparative study of SVR with four commonly used complex metamodeling techniquesincluding moving least square (MLS), artificial neural network (ANN), Kriging (KG) andradial basis function (RBF) is performed. The comparison is investigated both inapproximation accuracy and computational efficiency. It is shown that SVR outperformsother metamodeling techniques considering both approximation accuracy and efficiency.Consequently, SVR will be used in the succeeding study work to approximate structuralperformance indicators of autobody structure.
(2) Study on SVR-based adaptive metamodeling method for approximating
performance indicators of autobody structure
The improvement in prediction accuracy of metamodels at the design optimum willhelp obtain a reliable and feasible design scheme in real engineering applications. Toachieve this goal, the adaptive metamodeling method is studied based on the idea ofverifying the design optimum by using the finite element analysis. The SVR-basedadaptive metamodeling method is proposed and the framework and steps of the methodincluding verification and criterion are introduced in detail. The study case of lightweightdesign for frontal autobody structure is given and the surrogates of crashworthinessperformance indicators are constructed by using the proposed SVR-based adaptivemetamodeling method. The optimum design verified by the finite element simulation isfinally obtained, which makes the lightweight scheme more reliable in the real engineeringapplication. In this method, the design optimum is verified by using finite elementsimulation and then the simulation results of the design optimum will be fed back as theinformation of a sampling point that will be used if necessary to reconstruct themetamodels of structural performance indicators. It is shown that the prediction accuracyof metamodels at the design optimum is effectively improved and guaranteed, whichprovides a high-fidelity metamodeling method for RRBDO of autobody structure.
(3) Study on sequential optimization strategy of RRBDO based on MCS
In robust and reliability-based design optimization (RRBDO), traditional double loop(DL) strategy is frequently used but the weakness in solving efficiency and accuracy isusually aggravated for a large-scale engineering application. In order to tackle the problem, sequential optimization strategy is investigated based on decoupling the process ofoptimization and reliability analysis. The MCS-based sequential optimization strategy isproposed and the design steps are introduced in detail. The adaptive constraint shiftingmethod is created, which helps to realize the constraint boundary shifting for thedeterministic optimization process. The proposed strategy is verified by using classicalmathematical and engineering cases. By using the MCS-based sequential optimizationstrategy, both the unstability and multi-MPP problems of DL strategy are overcome. Whilethe accuracy for the reliability analysis is guaranteed and the solution efficiency is greatlyimproved. Consequently, the MCS-based sequential optimization strategy is suitable forbeing used in the RRBDO of complex system, which lays the foundation for thesucceeding study on the lightweight design method and corresponding engineeringapplications.
(4) Study on lightweight design method of autobody structure based on RRBDO
According to the particularity of the lightweight design for autobody structure, thelightweight design method of autobody structure based on RRBDO is investigated bycombining the SVR-based adaptive metamodeling method and MCS-based sequentialoptimization strategy. The framework of RRBDO-based method for autobody lightweightdesign is proposed and the detailed introduction to the design steps is given. Anapplication case of the lightweight design for full autobody structure is investigated andthe reliable lightweight scheme is obtained and applied in real engineering application,which verifies the feasibility of the proposed RRBDO-based method for autobodylightweight design. By using the RRBDO-based method for autobody lightweight design,the lightweight scheme is more reliable and the design efficiency is effectively improved.Furthermore, a software platform is developed based on the proposed method, which aimsat providing an operable and convenient tool for being used in automotive engineeringapplications.
(1)复杂近似建模方法拟合结构耐撞性能指标的对比分析研究
车身结构复杂系统具有设计变量多、结构性能响应非线性强等特点，针对二次多项式响应面进行轿车耐撞性等强非线性性能指标的拟合建模远远不能满足精度与计算效率要求的问题，基于试验设计和近似拟合理论与方法，引入机器学习领域的新方法―――支持向量回归(Support vector regression, SVR)，围绕结构耐撞安全性能指标的拟合建模，从拟合精度与计算效率两方面，与移动最小二乘(Moving leastsquare, MLS)、人工神经网络(Artificial neural network, ANN)、克罗格(Kriging)、径向基函数(Radial basis function, RBF)四种常用近似拟合方法进行了对比、分析研究，旨在提供一种先进的适合建立车身强非线性结构性能指标的拟合方法。研究表明，支持向量回归方法在拟合精度及计算效率上具有明显的优势，更适合应用于建立车身强非线性结构性能指标的近似拟合模型。
(2)基于支持向量回归的车身结构性能指标自适应建模方法研究
为提高近似拟合模型在最优解处的预测精度，从而得到具有工程可实施性的优化设计方案，基于利用有限元仿真检验最优设计解的思想，针对车身结构性能指标拟合建模中的自适应反馈方法进行了研究。通过对具体检验内容的研究，提出了自适应反馈过程的重要判别依据，建立了基于支持向量回归的自适应拟合建模方法。依据建立的设计流程与步骤，针对车身前部结构轻量化的工程实例进行了研究，验证了该方法的可行性。基于支持向量回归的自适应建模方法利用有限元仿真分析手段对最优设计解进行检验，并将最优设计解的样本信息反馈至近似拟合模型，保证了最优设计解与仿真分析结果的一致性，从而有效提高了拟合模型预测最优设计解的精度，为稳健与可靠性优化设计提供了高精度的近似拟合建模方法。
(3)基于蒙特卡罗(MCS)的稳健与可靠性优化设计串行优化策略研究
针对目前稳健与可靠性优化设计中的传统双循环优化策略计算效率低、求解不稳定的问题，基于解耦优化与可靠性分析过程的思想，对串行的优化策略进行了研究。通过对约束界限移动步长及约束界限值计算方法的研究，实现了串行优化策略中的确定性优化约束边界自适应调整过程，建立了自适应的约束边界调整方法。提出了基于MCS的串行优化策略，并建立了具体实现步骤。经理论的数学案例以及典型的工程应用案例验证了该优化策略的可行性。基于MCS的串行优化策略摒弃了传统双循环优化策略低效率的劣势，避免了最大可能失效点(Most probable point, MPP)方法进行可靠性分析的多MPP问题，保证可靠度计算精度的同时，有效提高了优化求解效率，适合应用于复杂系统的工程稳健与可靠性优化设计问题，为车身轻量化方法及其工程应用研究奠定了基础。
(4)基于稳健与可靠性优化设计的轿车车身轻量化方法研究
针对轿车车身结构轻量化的特点，结合基于支持向量回归的自适应建模方法以及基于MCS的串行优化策略，对轿车车身轻量化方法进行了研究。提出了基于稳健与可靠性优化设计的轿车车身轻量化总体构架，并建立了具体流程与详细设计步骤，进行了整个轿车车身轻量化工程实例设计研究，得到了工程上可行的轻量化方案并应用于实车轻量化改进，验证了基于稳健与可靠性优化设计的轿车车身轻量化方法的可行性。该方法有效提高了轻量化设计解的工程可行性以及设计效率，为车身轻量化提供了可借鉴的方法。在此基础上，构建了基于稳健与可靠性优化设计的轿车车身轻量化软件平台，为实际工程应用提供了可操作性的设计工具。
Weight reduction of automobiles is a fundamental approach to realize fuel economyand environmental protection. The weight reduction in body structure plays a ratherimportant role in decreasing the weight of full vehicle, which results from the fact thatbody structure possesses about40%weight of full vehicle. In real engineering applications,the variation of gauge thicknesses and mechanical parameters of material (yield limit, ultratensile strength, Young’s modulus) caused by uncertainties including environmentalchange, manufacturing precision and individual difference of operators, affects directly thestructural performances of autobody structure. The lightweight design without consideringaforementioned variations may fail to meet the requirements of structural performances,which will lead to a lack of feasibility and reliability in a real engineering application. Thedissertation concentrates on the study of determining the parameters of the existingautobody structure topology, including sheet thicknesses and mechanical parameters ofmaterial, to achieve the lightweight design of autobody structure. Based on robust andreliability-based design optimization (RRBDO), the method for the lightweight design ofautobody structure, coupled with its engineering application, is investigated consideringthe variation of sheet thickness and mechanical parameters of material caused by variouskinds of uncertainties. The main research work and corresponding conclusions are listedas follows:
(1) Comparative study of complex metamodeling techniques for approximating
crashworthiness performance indicators
The lightweight design for autobody structure is generally considered as the designoptimization problem for a complex system, since autobody structure is characterized bystrong nonlinear structural performance responses and large number of random variables.Polynomial response surface frequently undergoes bad accuracy and efficiency when approximating the structural performance indicators. To overcome this difficulty, supportvector regression (SVR) that is a new technique in machine learning is introduced toapproximate the crashworthiness performance indicators of autobody structure. Thecomparative study of SVR with four commonly used complex metamodeling techniquesincluding moving least square (MLS), artificial neural network (ANN), Kriging (KG) andradial basis function (RBF) is performed. The comparison is investigated both inapproximation accuracy and computational efficiency. It is shown that SVR outperformsother metamodeling techniques considering both approximation accuracy and efficiency.Consequently, SVR will be used in the succeeding study work to approximate structuralperformance indicators of autobody structure.
(2) Study on SVR-based adaptive metamodeling method for approximating
performance indicators of autobody structure
The improvement in prediction accuracy of metamodels at the design optimum willhelp obtain a reliable and feasible design scheme in real engineering applications. Toachieve this goal, the adaptive metamodeling method is studied based on the idea ofverifying the design optimum by using the finite element analysis. The SVR-basedadaptive metamodeling method is proposed and the framework and steps of the methodincluding verification and criterion are introduced in detail. The study case of lightweightdesign for frontal autobody structure is given and the surrogates of crashworthinessperformance indicators are constructed by using the proposed SVR-based adaptivemetamodeling method. The optimum design verified by the finite element simulation isfinally obtained, which makes the lightweight scheme more reliable in the real engineeringapplication. In this method, the design optimum is verified by using finite elementsimulation and then the simulation results of the design optimum will be fed back as theinformation of a sampling point that will be used if necessary to reconstruct themetamodels of structural performance indicators. It is shown that the prediction accuracyof metamodels at the design optimum is effectively improved and guaranteed, whichprovides a high-fidelity metamodeling method for RRBDO of autobody structure.
(3) Study on sequential optimization strategy of RRBDO based on MCS
In robust and reliability-based design optimization (RRBDO), traditional double loop(DL) strategy is frequently used but the weakness in solving efficiency and accuracy isusually aggravated for a large-scale engineering application. In order to tackle the problem, sequential optimization strategy is investigated based on decoupling the process ofoptimization and reliability analysis. The MCS-based sequential optimization strategy isproposed and the design steps are introduced in detail. The adaptive constraint shiftingmethod is created, which helps to realize the constraint boundary shifting for thedeterministic optimization process. The proposed strategy is verified by using classicalmathematical and engineering cases. By using the MCS-based sequential optimizationstrategy, both the unstability and multi-MPP problems of DL strategy are overcome. Whilethe accuracy for the reliability analysis is guaranteed and the solution efficiency is greatlyimproved. Consequently, the MCS-based sequential optimization strategy is suitable forbeing used in the RRBDO of complex system, which lays the foundation for thesucceeding study on the lightweight design method and corresponding engineeringapplications.
(4) Study on lightweight design method of autobody structure based on RRBDO
According to the particularity of the lightweight design for autobody structure, thelightweight design method of autobody structure based on RRBDO is investigated bycombining the SVR-based adaptive metamodeling method and MCS-based sequentialoptimization strategy. The framework of RRBDO-based method for autobody lightweightdesign is proposed and the detailed introduction to the design steps is given. Anapplication case of the lightweight design for full autobody structure is investigated andthe reliable lightweight scheme is obtained and applied in real engineering application,which verifies the feasibility of the proposed RRBDO-based method for autobodylightweight design. By using the RRBDO-based method for autobody lightweight design,the lightweight scheme is more reliable and the design efficiency is effectively improved.Furthermore, a software platform is developed based on the proposed method, which aimsat providing an operable and convenient tool for being used in automotive engineeringapplications.
引文
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