钢筋混凝土简支梁的多目标可靠性稳健优化设计

为提高钢筋混凝土简支梁的可靠性和稳健性,运用可靠性和稳健优化设计的理论,建立了适合可靠性稳健优化设计的多目标模型。针对可靠性稳健优化设计的多目标模型多属于高维多目标优化问题,把灰色理论中的关联分析引入粒子群优化算法中,根据灰色关联能够很好地分析目标矢量之间的接近程度,并能掌握解空间全貌的特点,提出了适合高维多目标优化问题求解的灰色粒子群算法。与传统方法相比,该方法更能迅速准确地得到钢筋混凝土简支梁的可靠性稳健优化设计信息。     

基于响应面的结构疲劳寿命6σ稳健优化设计

针对结构疲劳寿命离散性较大的问题,将响应面法、6σ稳健设计与疲劳寿命设计相结合,提出一种提高结构疲劳寿命稳健性且减轻结构质量的方法。该方法先用对设计参数进行拉丁超立方采样,用有限元法计算结构疲劳关键点处的平均应力和对称应力谱;然后采用Miner法求出结构关键点处的疲劳寿命,构造出疲劳寿命的响应面模型;最后基于响应面模型对结构进行疲劳寿命的6σ稳健优化设计。对某型搅拌车副车架进行了疲劳寿命稳健优化设计的结果表明,该方法在保证副车架疲劳寿命可靠性满足设计要求的基础上,有效提高其疲劳寿命的稳健性,减轻其结构质量。     

基于遗传算法的膜片弹簧的稳健化设计

在离合器膜片弹簧传统优化设计模型的基础上,应用工程稳健性的灵敏度分析,考虑了可控变量和不可控参数变差对膜片弹簧优化设计的影响,并采用遗传算法对目标函数进行了求解。结果表明,遗传算法具有比传统优化方法更强的全局寻优能力,稳健化设计能有效、可靠的减小质量波动对膜片弹簧优化设计的影响。     

基于区间分析的发动机悬置系统稳健优化设计

针对不确定参数发动机悬置系统的优化问题,基于区间分析理论,将稳健设计与多目标优化相结合,提出一种提高发动机悬置系统隔振性能的稳健优化设计方法。该方法在区间数学模型的基础上建立悬置系统稳健优化模型,以动反力及其变化范围最小为目标函数,固有频率的合理配置为约束条件,悬置刚度为优化变量,并将悬置刚度和悬置位置的波动范围作为区间参数变量,对某型轿车发动机悬置系统进行稳健优化设计。结果表明,该方法改善了悬置系统的隔振性能,提高了系统参数的稳健性。     

汽车半轴可靠性分析的参数灵敏度

讨论了汽车半轴的可靠性灵敏度设计问题，提出了可靠性灵敏度分析的计算方法，研究了设计参数的改变对半轴可靠性的影响，为半轴的可靠性设计提供了理论依据。     

基于灵敏度分析的发动机悬置系统稳健优化设计

以发动机悬置系统能量解耦为目标,悬置刚度参数为设计变量,考虑目标函数和约束函数对于悬置刚度参数的灵敏度,构造了多目标优化数学模型.采用遗传优化算法对一款发动机悬置系统的悬置刚度参数进行了稳健优化设计,并用Monte Carlo方法进行了分析.结果表明,优化方法可以有效降低系统解耦度对悬置刚度参数的灵敏度,提高了悬置系统设计的实用性.     

同步齿形带传动的可靠性优化设计

根据可靠性理论将有关的设计变量处理成随机变量，建立了同步齿形带传动的可靠性优化设计的概率模型，并结合实例给出了同步齿形带可靠性优化设计的方法和结果。     

基于序列Kriging模型的汽车车身轻量化可靠性优化设计CSCD

为减少汽车车身轻量化可靠性优化设计的计算量并提高优化设计的精度,提出一种序列Kriging可靠性优化设计方法。以整车质量作为优化目标,选取整车耐撞性指标作为可靠性约束,建立可靠性优化设计模型。采用Latin超立方试验设计生成汽车正面碰撞有限元仿真模型的样本数据进行计算,根据有限元仿真结果构建目标和约束函数的Kriging近似模型;采用序列优化与可靠性评定方法 (SORA)将该嵌套优化问题解耦为单层次优化问题;优化每一迭代步,基于Kriging模型采用功能度量法评定概率约束。结果表明:所提方法满足工程设计所需的效率和精度要求,满足了整车安全性、轻量化和可靠性设计要求,整车质量减少约1.4%。     

基于容差模型的发动机悬置系统稳健优化设计

应用稳健优化设计理论,考虑设计变量的不确定性对结果的影响,建立稳健优化模型。以发动机悬置系统能量解耦为目标,用Pareto遗传算法对系统的刚度参数进行稳健优化,并将优化结果运用Monte Carlo方法进行分析.结果表明,优化方法可以有效提高悬置系统的稳健性。     

任意分布参数的拉杆可靠性优化设计

讨论了随机参数服从任意分布时拉杆的可靠性优化设计问题。在基本随机参数的前四阶矩已知的情况下，应用随机摄动法和Edgeworth级数方法对拉杆进行可靠性优化设计，通过计算机程序可以实现随机参数服从非正态分布的拉杆可靠性优化设计，迅速准确地得到了拉杆的可靠性优化设计信息。     

Reliability-based sensitivity analysis of vehicle components with non-normal distribution parameters

Techniques from the perturbation method, fourth moment method, reliability-based design theory, and sensitivity analysis approach are employed to present a practical and efficient method for testing the reliability sensitivity of vehicle components with non-normal distribution parameters. With the condition that the first four moments of original random variables are known, the reliability sensitivity theory and cases are researched using the presented numerical method. The variation regularities of reliability sensitivity are obtained and the effects of design parameters on reliability of the vehicle components are studied. The sophisticated formulation provided in this paper is easily amenable to computational procedures. The respective program can be used to obtain the reliability sensitivity of vehicle components with non-normal distribution parameters accurately and quickly. The results obtained are perfect and the solutions compared very well with those from Monte Carlo simulation. The method presents a theoretic basis for the reliability design of the vehicle components.     

Robust optimization design method for powertrain mounting systems based on six sigma quality control criteria

This paper presents a robust optimization design method based on Six Sigma quality control criteria to improve the design of a powertrain mounting system (PMS). The powertrain is modeled as a rigid body having six degrees of freedom (DOF) connected to a rigid base by four rubber mounts, and each mount is simplified as a three-dimensional spring-damper element in its local coordinate system (LCS). The calculation method based on energy decoupling is used to estimate the decoupling ratios of a PMS. The location and static stiffness of each mount and the orientations of the two anti-torsion mounts are selected as uncertain design variables, and the nominal values of these design variables are optimized to obtain a robust Six Sigma design for a PMS. The uncertain design variables are characterized by a perturbation or percent variation around their nominal values. The generalized reduced gradient (LSGRG2) optimization method is employed to solve the robust optimization problem, and a second-order Taylor series expansion is used to estimate the statistical properties of the performance constraints and objectives. The optimization results show that the robust design ensures good robustness or high reliability for the natural frequencies, decoupling ratios, and frequency separation constraints of a PMS.     

镁铝合金车轮胶栓复合连接稳健性优化设计

提出了车轮胶栓复合连接的多目标确定性与稳健性优化设计方法。首先,建立镁铝合金组装式车轮螺栓连接有限元模型,对其进行弯曲疲劳寿命仿真,对比疲劳试验结果,验证了车轮仿真模型的准确性。随后,建立结构胶弹塑性本构模型,通过试验得到其应力应变曲线和剪切强度。最后,选取胶层厚度与种类、螺栓预紧力和螺栓孔直径为设计变量,建立车轮栓胶复合连接参数化仿真模型,以车轮连接螺栓疲劳寿命、结构胶最大拉伸和最大剪切应力为优化目标,借助ISIGHT优化平台,分别采用第二代非劣排序遗传算法(NSGA-Ⅱ)和微存档遗传算法(AMGA)对车轮胶栓连接结构进行了多目标确定性和6σ稳健性优化设计。稳健性优化后车轮连接的可靠性得到进一步提升。     

Robust design optimization of suspension system considering steering pull reduction

This paper presents robust design optimization method to reduce steering pull phenomenon. One of the biggest causes of steering pull phenomenon is tolerance of suspension system such as hard point, spring, damper and bush. Therefore, the relationship between suspension systems and steering pull phenomenon has as nonlinear characteristics. But, it can be very difficult to evaluate the analytical design sensitivity. Thus, it is impossible to directly apply a well-developed optimization algorithm based on gradient information. To avoid these difficulty, this study uses sequential approximation optimization process based on a meta-model. The robust design process has 28 random design variables with tolerance. For efficient design process, the sample variances for the design goals are approximated from meta-models. The proposed approach required only 62 evaluations until it converged. Optimal design reduced the drift by 80% and its deviation by 38.7%, respectively. This result proves that the suggested design method of suspension system is effective and useful.     

基于疲劳寿命的驱动桥壳可靠性与轻量化设计

为提高驱动桥壳的轻量化水平和道路行驶疲劳可靠性，对驱动桥壳进行6-Sigma稳健性多目标轻量化设计。首先，建立驱动桥壳的虚拟台架仿真模型，并进行垂直弯曲刚性和垂直弯曲静强度的仿真分析，将仿真得到的桥壳本体各测点变形量和关键受力点应力值与试验结果进行对比，以验证桥壳虚拟台架仿真模型的可信性。其次，建立驱动桥壳的最大垂向力仿真模型，结合耐久性强化路面下驱动桥壳板簧座处的垂向载荷谱，基于名义应力法，对驱动桥壳进行了道路行驶工况下的疲劳寿命分析。然后，选取驱动桥壳本体各截面壁厚为设计变量，基于熵权法和TOPSIS（Technique for Ordering Preferences by Similarity to Ideal Solution，TOPSIS）方法研究各壁厚变量对桥壳综合性能的影响。结合RBF（Radial Basis Function，RBF）近似模型和NSGA-Ⅱ算法（Elitist Non-dominated Sorting Genetic Algorithm，NSGA-Ⅱ）对驱动桥壳进行基于疲劳寿命的多目标确定性轻量化设计，获取Pareto最优解集，选取桥壳的优化方案。最后，基于蒙特卡罗模拟抽样方法和微存档遗传算法（AMGA）对驱动桥壳进行了多目标6-Sigma稳健性轻量化设计，得到桥壳稳健性优化方案。研究结果表明：稳健性优化后，驱动桥壳本体的疲劳寿命降低了12.3%，但和初始结构的疲劳寿命相比，仍提升了117%；桥壳本体疲劳寿命正态分布的标准方差下降了72.1%，说明桥壳本体的疲劳可靠性得到了大幅提升；桥壳本体的质量升高了1.8%，但和优化前的桥壳原结构相比，仍实现减重5.9%。     

A robust optimization for the frequency and decoupling ratio of a powertrain mounting system based on interval analysis

This paper presents a robust optimization method to decrease the variations in the performance of the designed system caused by the unavoidable manufacturing, installation or measurement errors of the design variables. Generally, it is difficult and costly to determine statistical information with sufficient precision for uncertain design variables; in this study, interval numbers are used to describe the uncertain design variables, and only the bounds of these variables are required. An improved interval truncation method is presented for estimating the variation ranges of the system performances. The robustness estimations of the system performances are incorporated into the optimization formulation to obtain the nominal design variables, which could make the system performances relatively robust; therefore, the design robustness is estimated and improved in the optimization iteration process. The robust optimization method is applied to a general powertrain mounting system (PMS) to improve the design robustness of the PMS decoupling layout and frequency allocation. The optimization results show that the robust optimization method could effectively increase the decoupling ratios in the interested vertical and pitch directions, and the frequency allocation is more robust than that obtained using the traditional deterministic optimization.     

Robust design optimization of the dynamic responses of a tracked vehicle system

This paper presents the robust design optimization of the dynamic responses of a heavy military tracked vehicle system. The tracked vehicle model addressed in this study has 954 degrees of freedom and consists of 189 bodies in total: 37 bodies for the chassis, such as sprockets, road wheels, road arms, etc.; 76 track link bodies for each track subsystem; 36 revolute joints; and 152 bushing elements. The design objectives were to minimize the maximum vertical acceleration of the hull and its variance while satisfying the wheel travel constraints for torsion bars and the hydro-pneumatic suspension units within ±1σ ranges. To avoid the difficulty of the design sensitivity analysis and to overcome the numerical noise, a progressive meta-model technique was employed in the optimization process. First, space-filling methods were used to determine the minimum number of sample points. Second, the simultaneous kriging method was used to construct the initial meta-models, and the augmented Lagrange multiplier (ALM) method was then used to solve the robust design problems of the meta-models. Third, the new design results were added to the analysis results for the initial sample points, and the meta-models were updated automatically. Next, the optimizer resolved the robust design problems of the updated meta-models. These processes were repeated until the convergence tolerances were satisfied. The robust design optimization of the tracked vehicle system, with 11 random design variables, was solved in only 26 analyses, including 12 analyses for the initial meta-models and 14 analyses added during the iterative optimization process.     

Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach.