基于参数和代理模型不确定性的冲压稳健性设计优化

在传统的基于代理模型的冲压稳健性设计中,由于代理模型与真实模型间存在着误差,必然会导致优化结果存在一定的误差。将实验设计、Kriging模型相结合,综合考虑参数不确定性和代理模型不确定性的影响,提出了一种新的冲压稳健性优化设计方法。通过因素敏感性分析筛选出相应的设计变量和噪声因素,基于Kriging模型构建设计参数和质量指标的代理模型,采用蒙特卡罗分析方法以及遗传算法获得最优工艺解。实例分析结果表明,综合两种不确定因素的稳健设计方法能有效地降低拉裂、起皱约束失效概率,提高冲压件成形质量和工艺稳健性。     

基于不确定性定位参数与转向机构稳健设计

车辆定位参数与转向机构在设计过程中,存在诸多不确定性因素,这些将影响机构性能的稳健性。基于ADAMS与i SIGHT搭建基于随机不确定性的定位参数与转向机构的稳健设计模型,采用正交试验方法得到各优化目标的主要因素及各因素之间的交叉影响关系,依据试验数据建立优化目标与设计变量之间的二阶响应面近似模型。综合考虑定位参数、转向梯形机构的尺寸误差、安装误差等随机不确定性因素,应用田口方法建立了定位参数和转向梯形机构的稳健设计模型,并通过与传统设计方法的对比验证了田口方法的稳健性。应用蒙特卡洛法保证了转向过程中转向轮转角的精度,应用可靠性优化方法保证了转向机构传动角约束的可靠性。     

稳健优化的表述、不确定分析与求解策略

首先通过对优化问题的表述,说明稳健优化与传统确定性优化的区别。稳健优化需进行不确定性分析,为此对目标的均值和方差同时进行优化。然后分析和比较了蒙特卡罗法、基于敏感度法、解析法、基于代理模型法等不确定性分析方法的特点,其中着重介绍了基于代理模型的不确定性分析方法。最后讨论了2类求解稳健优化问题的策略：加权法和多目标遗传算法。     

考虑不确定性的稳健优化设计研究

传统的稳健设计仅能处理含有随机不确定性参数的问题,以响应的均值和方差来评价。稳健优化设计同时考虑随机不确定性参数和认知不确定性参数,实现不确定性传播的基础上对问题设计参数的优化。采用证据理论来统一描述随机不确定性和认知不确定性,并介绍了描述不确定性时的不确定性传播和响应原理,提出了响应评价准则。最后,以测试系统为研究对象,采用证据理论结构进行不确定性传播,以提出的准则为判断标准,对优化过程进行了验证。     

基于多权值优化代理模型的撒砂装置稳健可靠性设计

为合理衡量不确定性因素对撒砂装置最大应力的影响,提升其稳健可靠性水平,文中提出了基于多权值优化代理模型的稳健可靠性设计方法。首先,构建撒砂装置有限元模型并计算其最大应力响应;其次,提取不确定性因素并编制参数化分析文件,进而依据试验设计结果,初步构建不确定性因素与最大应力的代理模型,采用遗传算法对代理模型中传递及加权系数进行优化,提升代理模型对最大应力响应预测结果的合理性;最后,基于稳健可靠性设计思想,建立撒砂装置稳健可靠性优化设计模型,并通过对比分析验证所提方法的有效性。结果表明：多权值优化代理模型最大应力决定系数提升至0.991 8;经稳健可靠性设计得到的撒砂装置最大应力波动标准差减小至1.46 MPa,可为其他工程结构稳健可靠性的设计提供一定参考。     

自适应神经网络的机械臂终端滑模鲁棒控制

针对带有未知负载力矩和模型误差等不确定性的机械臂系统,同时考虑电机动态特性的影响,提出了一种自适应神经网络的终端滑模鲁棒控制方法.首先建立了包含不确定性和电机动态的机械臂系统模型,然后通过设计终端滑模面来抑制传统滑模面的抖振现象,并提出了终端滑模鲁棒控制律,同时引入RBF神经网络来准确估计不确定性,并且设计了自适应律来...     

机械稳健设计的研究概况及发展趋势

阐述了稳健设计的意义：介绍了产品稳健设计研究的方法,包括传统稳健设计方法和工程稳健优化设计方法;讨论了稳健设计与不确定性因素以及稳健设计与多学科、多目标优化相结合的发展趋势,同时简要指出了稳健优化设计方法与实际应用存在的差距。     

基于灵敏度分析的区间不确定性稳健设计

针对机电产品优化设计中存在的大量不确定性因素，在灵敏度分析的基础上提出了区间不确定性稳健设计方法。首先探讨了灵敏度分析理论，借用Sobol'法筛选出对系统影响较大的因素并将不灵敏项进行固化；然后根据区间不确定性因素的特性，分析了不确定因素在某一区间变化时对系统整体的影响；最后给出了基于灵敏度分析的区间不确定性稳健设计优化模型。工程实例证明了该方法的有效性。     

一种基于证据理论的稳健设计方法

提出了基于证据理论同时处理随机不确定性与认知不确定性的稳健设计方法。通过对随机不确定性参数的概率密度进行证据结构化,建立了随机与认知不确定性参数的统一量化和传播框架,构建了基于证据结构响应均值与变差的稳健设计准则。实例研究表明所提方法可行有效。     

稳健优化的表述、不确定分析与求解策略

首先通过对优化问题的表述,说明稳健优化与传统确定性优化的区别.稳健优化需进行不确定性分析,为此对目标的均值和方差同时进行优化.然后分析和比较了蒙特卡罗法、基于敏感度法、解析法、基于代理模型法等不确定性分析方法的特点,其中着重介绍了基于代理模型的不确定性分析方法.最后讨论了2类求解稳健优化问题的策略:加权法和多目标遗传算法.     

A Metamodeling Method Based on Support Vector Regression for Robust Optimization

Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationaUy expensive simulation models.Existing metamodels main focus on polynomial regression(PR),neural networks(NN)and Kriging models,these metamodeis are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity.To address the problem,a reduced approximation model technique based on support vector regression(SVR)is introduced in order to improve the accuracy of metamodels.A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear.First appropriate design parameter samples are selected by experimental design theories,then the response samples are obtained from the simulations such as finite element analysis,the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions.Combining other constraints,the robust optimization model is formed which can be solved by genetic algorithm(GA).The applicability of the method developed is demonstrated using a case of two-bar structure system study.The performances of SVR were compared with those of PR,Kriging and back-propagation neural networks(BPNN),the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty.The robust optimization solutions are near to the real result,and the proposed method is found to be accurate and efficient for robust optimization.This reaserch provides an efficient method for robust optimization problems with complex structure.     

Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.     

不确定性参数识别的模态区间逆响应面法

结合模态区间分析及响应面的相关理论,提出一种新的不确定性参数识别方法,即模态区间逆响应面法。首先,以有界区间数来量化结构参数的不确定性,通过合理的实验设计确定样本数据;然后,以响应为输入,设计参数为输出,采用逐步回归分析构造设计参数与结构响应的模态区间逆响应面模型,进而直接在模态区间逆响应面模型上进行模态区间运算,即可识别材料参数的变异性区间;最后,采用一组钢板模态实验来验证所提方法的可行性及可靠性。结果表明:所提方法可准确识别钢板材料参数的取值区间,有效地解决多重变量区间运算存在的区间过估计问题,识别过程避免区间迭代优化,具有较高的计算效率。     

A robust optimization using the statistics based on kriging metamodel

Robust design technology has been applied to versatile engineering problems to ensure consistency in product performance. Since 1980s, the concept of robust design has been introduced to numerical optimization field, which is called the robust optimization. The robustness in the robust optimization is determined by a measure of insensitiveness with respect to the variation of a response. However, there are significant difficulties associated with the calculation of variations represented as its mean and variance. To overcome the current limitation, this research presents an implementation of the approximate statistical moment method based on kriging metamodel. Two sampling methods are simultaneously utilized to obtain the sequential surrogate model of a response. The statistics such as mean and variance are obtained based on the reliable kriging model and the second-order statistical approximation method. Then, the simulated annealing algorithm of global optimization methods is adopted to find the global robust optimum. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.     

串联式混合动力汽车辅助功率单元鲁棒辨识

串联式混合动力汽车柴油机辅助功率单元(APU)是一个多输入多输出的复杂非线性系统,为便于控制器的设计,提出一种简单的面向控制的APU模型鲁棒辨识方法。该方法基于非线性变增益控制的原理,把APU系统看成一个线性变参数系统,并将参数区域进行网格离散化,在每一工作点采用基于模型不确定性建模的方法进行模型辨识,其结果表示为名义模型及其不确定性区域。最后在APU试验台架上进行了辨识试验,试验结果表明该方法的可行性。     

耐火材料水流量平板法导热系数的测试及其不确定度分析

导热系数是耐火材料重要的热学性能参数,也是炉窑设计重要的热工技术参数,通过阐述水流量平板法导热系数的测试原理,分析其不确定度。