基于NSGA-Ⅱ混合灵敏度分析的白车身轻量化优化设计

针对白车身的轻量化问题,提出了一种基于混合灵敏度分析的参数化优化方法。通过有限元仿真,标定白车身的弯扭刚度及模态性能。以试验设计分析零件厚度相对白车身弯扭刚度及模态的灵敏度,确定并筛选出对白车身刚度及模态性能影响不大的零件。将零件厚度作为多目标优化的设计变量,以白车身质量最小化、弯扭刚度最大化为优化目标,模态性能为约束条件构建多目标优化设计函数。基于NSGA-II遗传算法,进行白车身结构的轻量化优化设计。经Isight优化求解仿真,优化所选零件的厚度,轻量化设计了白车身结构。轻量化设计后的白车身性能仿真结果表明,其刚度及模态性能得到保证的前提下,白车身质量减轻了5.7%。     

基于灵敏度分析的客车车身骨架轻量化设计

将静动态性能指标作为约束条件,建立车身骨架优化设计的数学模型.引入骨架梁截面重要度评价因子,利用对目标函数和状态变量进行灵敏度分析的方法确定优化设计变量,通过有限元Ansys软件的优化设计模块,采取分步优化的方式,进行客车车身轻量化设计.对某车型的计算结果表明,在最大等效应力、扭转刚度、低阶模态频率均较好满足使用要求的前提下,车身骨架累计减重达21.1％.说明该方法是有效的.     

应急监测拖车车身骨架结构分析与轻量化研究

运用有限元分析理论,在ANSYS Workbench有限元软件中建立应急监测拖车车身骨架结构的有限元模型,并对其进行静力学分析和模态分析。根据分析结果采用拓扑优化方法对应急监测拖车车身骨架进行拓扑优化设计,根据拓扑优化结果并结合车身骨架的设计和制造工艺要求,对车身骨架结构重新进行布局和设计,得到优化后的车身骨架结构。通过相对灵敏度分析找出对车身骨架性能不敏感但对车重敏感的设计变量,采用多目标尺寸优化的方法对应急监测拖车车身骨架结构进行轻量化设计,并对其在水平弯曲、极限扭转、紧急制动和紧急转弯4种典型工况下的性能进行对比。结果表明:优化后的车身骨架在满足各项性能要求的前提下,实现减重86.57 kg,减重率为14.08%,取得了一定的轻量化效果。     

微型电动汽车车身的轻量化

目前对于电动汽车车身轻量化研究中,多数都没能兼顾优化后车身性能、优化效率和精度。为了提高轻量化后车身的性能,提出一种综合考虑质量、刚度与一阶振动模态的多目标优化设计法。将有限元分析法和实验验证相结合,构造了基于材料和尺寸的微型电动汽车车身轻量化方法,确保了优化精度。采用灵敏度分析法确定关键部件厚度为优化模型的设计变量,提高了轻量化研究的效率。与传统的轻量化研究相比,该方法在获得近似最优解的同时能够保证轻量化后的车身性能满足要求,并且提高了计算效率和精度。     

混合动力客车车身骨架轻量化设计

利用HyperMesh软件对混合动力客车进行静态分析和模态分析,使用相对灵敏度的方法筛选出对客车刚度和客车模态影响小但对质量影响大的构件,将其厚度作为设计变量,以客车质量最小、扭转刚度最大为目标对客车进行多目标优化,并对比优化前后模型的结构性能.分析结果表明,在各项性能变化不大,在满足设计要求的条件下,客车车身骨架质量减少259kg,轻量化程度达到10.67％,取得了较大的轻量化效果,同时说明了客车骨架结构轻量化设计方案的有效性.     

基于灵敏度分析的白车身结构轻量化设计

建立了白车身有限元模型,并用模态实验验证了模型的正确性,利用有限元法分析了该模型一阶扭转模态频率灵敏度和车身质量灵敏度,选取相对灵敏度绝对值较大的车身板件作为轻量化设计变量,以白车身质量为优化目标、一阶扭转模态频率为状态变量进行优化,优化后白车身质量降低10.5kg.     

基于灵敏度分析的商用车白车身轻量化研究

对某商用车白车身进行模态、弯曲刚度及扭转刚度有限元分析。然后对该白车身总成构件进行灵敏度分析,以确定优化设计变量,在保证白车身模态及刚度性能不比优化前降低的前提下,以白车身总质量最小为优化目标。通过优化,使该白车身总成质量降低约5.1%,同时固有频率及刚度性能也有略有提升,达到了轻量化效果。     

基于ANSYS的客车车身骨架优化设计

应用有限元分析软件ANSYS建立了车身骨架有限元模型并进行计算,采用其提供的优化方法对车身结构进行优化设计.选取车身骨架总质量为优化目标函数,状态变量选定为整车扭转刚度及车身低阶固有频率,设计变量选取为车身骨架主要型材的截面参数.最终保证客车在性能满足要求的前提下,减轻车身自重.     

全承载式客车车身结构优化设计

建立了全承载式客车车身骨架及顶盖蒙皮的有限元分析模型 ,并完成了车身结构的参数优化设计。将车身结构总体积作为优化目标函数 ,以车身骨架主要型材的截面参数为设计变量 ,选择整车扭转刚度及车身低阶固有频率等约束条件构成约束条件。优化后对车身强度和刚度进行校核 ,以检验优化结果的合理性 ,确保客车在性能满足要求的前提下减轻车身自重。     

白车身多学科轻量化优化设计应用

在白车身开发早期阶段引入结构轻量化思想,建立隐式全参数化白车身模型,通过多学科优化过程,找到白车身零件形状、尺寸、位置与厚度等各参数之间的最佳组合,以及满足系统各项性能要求的重量最优解,使白车身轻量化设计的潜能得到最大程度的发挥。根据白车身自身性能的特点对其分成不同的优化区域分别进行不同工况的优化,从而合理地安排设计变量和样本点数量,并对由试验设计得到的近似模型进行多学科的轻量化优化设计,有效地控制分析与优化时间,给车身设计提供指导。最终得到的白车身方案减重12 kg,减重率达到4.5%。同时利用方差分析方法,对各设计变量对性能的贡献量与主效应进行分析,掌握设计变量对刚度,模态、被动安全性能以及重量的影响规律。     

基于刚度灵敏度分析的客车结构优化的研究

文中把灵敏度分析引入到客车结构优化的设计中去,通过对某型客车中门各改进的梁的相关参数相对于中门刚度变化的灵敏度分析,找出对刚度变化最敏感的几个设计参数,再对其用一阶优化方法计算出最优数值,从而给客车中门的改进提供指导。     

车体设计参数对车体垂向弯曲振动频率的灵敏度分析

采用基于灵敏度分析的车体垂向弯曲刚度优化方法,对车体结构关键参数进行了灵敏度分析,确定了关键设计变量与系统响应的关系,从而得到了结构设计变量（车体结构关键参数）对目标约束函数（车体一阶垂向弯曲振动频率）影响的变化梯度。首先,由欧拉-伯努利梁垂向振动微分方程,结合初始条件和边界条件,得到了自由梁的一阶垂向弯曲振动频率方程;然后,结合车体结构特征,推导和修正了车体的一阶垂向弯曲振动频率解析方程,并以某地铁车体对解析方程进行了有限元的验证;最后,选取车体的6组设计参数进行了车体一阶垂向弯曲振动频率的灵敏度分析。根据研究结果,给出了车体关键参数对其一阶垂向弯曲振动频率影响程度的排序,为列车车体设计相关工作提供了参考依据。     

Analytical approach to robust design of nonlinear mechanical systems

The robustness of mechanical systems is influenced by various factors. Their effects must be understood for designing robust systems. This paper proposes a model for describing the relationships among functional requirements, structural characteristics, design parameters and uncontrollable variables of nonlinear systems. With this model, the sensitivity of systems was analyzed to formulate a system sensitivity index and robust sensitivity matrix to determine the importance of the factors in relation to the robustness of systems. Based on the robust design principle, an optimization model was developed. Combining this optimization model and the Taguchi method for robust design, an analysis was carried out to reveal the characteristics of the systems. For a nonlinear mechanical system, relationships among structural characteristics of the system, design parameters, and uncontrollable variables can be formulated as a mathematical function. The characteristics of the system determine how design parameters affect the functional requirements of the system. Consequently, they affect the distribution of system performance functions. Nonlinearity of the system can facilitate the selection of design parameters to achieve the required functional requirements.     

Decomposition method of complex optimization model based on global sensitivity analysis

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.     

混合离散变量优化设计的复合遗传算法

目前，对混合离散变量的遗传算法研究较少，而且现有算法对设计变量的处理不能很好地满足工程设计要求。为此，提出了一种面向设计、制造的设计变量工程化处理方法，能合理地处理优化设计中混合离散变量的取值问题。引入了混沌移民算子对基本遗传算法进行了改进，并开发了混合离散变量优化的复合遗传算法程序LSGA。工程设计实例表明，该算法对优化设计问题的特性无特殊要求，具有较好的普适性，而且程序运行可靠，全局收敛能力强。     

Reliability-based robust optimization design of automobile components with non-normal distribution parameters

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (>0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (>0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.     

Multidisciplinary wing design optimization considering global sensitivity and uncertainty of approximation models

In recent years, high-fidelity analysis tools, such as computational fluid dynamics and finite element method, have been widely used in multidisciplinary design optimization (MDO) to enhance the accuracy of design results. However, complex MDO problems have many design variables and require long computation times. Global sensitivity analysis (GSA) is proposed to assuage the complexity of design problems by reducing dimensionality where variables that have low impact on the objective function are neglected. This avoids wasting computational effort and time on low-priority variables. Additionally, uncertainty introduced by the fidelity of the analysis tools is considered in design optimization to increase the reliability of design results. Reliability-based design optimization (RBDO) and possibility-based design optimization (PBDO) methods are proposed to handle uncertainty in design optimization. In this paper, the extended Fourier amplitude sensitivity test was used for GSA, whereas a collaborative optimization-based framework with RBDO and PBDO was used to consider uncertainty introduced by approximation models. The proposed method was applied to an aero-structural design optimization of an aircraft wing to demonstrate the feasibility and efficiency of the developed method. The objective function was to maximize the lift-to-drag ratio. The proposed process reduced calculation efforts by reducing the number of design variables and achieved the target probability of failure when it considered uncertainty. Moreover, this work evaluated previous research in RBDO with MDO for the wing design by comparing it with the PBDO result.     

基于径向基组合近似模型技术的立柱结构优化设计

针对实际工程复杂优化问题,通常难以获得优化设计变量与优化目标之间的显示函数关系表达式,以SZJY-14型加工中心的立柱作为研究对象,研究了一种基于径向基组合近似模型技术的立柱结构优化设计。首先,针对径向基近似模型预测精度较低等问题,研究了一种基于多策略的径向基近似模型技术,并以改进径向基近似模型技术为基础,构建了组合近似模型技术。其次,利用有限元分析软件Abaqus对立柱三维结构进行网格划分,并对其进行静动态性能分析确定优化目标,采用灵敏度分析法确定优化设计变量,利用组合近似模型技术建立了立柱优化设计模型。最后,采用改进回溯搜索优化算法实现了立柱结构优化设计。求解结果表明,与立柱原结构相比,优化后质量减小了3.77%,最大变形量降低了5.14%,一阶频率提高了5.9%,达到了预期效果。     

基于人工神经网络技术的结构布局优化设计

使用PCL（Patran Command Language）实现了Patran环境下的机翼参数化模型。其优化模型包含两类设计变量：几何位置变量和几何尺寸变量。在采用Nastran软件实现几何尺寸优化的基础上，结舍均匀试验设计方法，利用神经网络的高度非线性映射功能，建立了目标函数与位置设计变量的映射关系。在Matlab环境下，编写了使用改进的可行方向法的优化程序，并对翼梁位置完成优化，最终完成了整个机翼的布局优化设计。可以看出。将参数化建模与神经网络功能结舍进行结构优化，能更好地发挥神经网络的映射功能，使优化结果更加精确、高效。所提方法可以解决在Patran环境下的复杂结构位置变量优化问题，弥补了该软件的不足之处，具有很好的应用推广价值。     

Trade-off analysis in multi-objective optimization using Chebyshev orthogonal polynomials

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps : the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.