鲁棒优化设计方法在结构动力学中的应用

在传统的静力学鲁棒优化设计基础上,考虑时间t参数,通过优化系统目标函数和约束条件的鲁棒性,将鲁棒优化设计方法运用在动力学问题中。通过一个主系统的质量和刚度均有微小波动的二自由度模型减振器设计算例,与传统的优化设计方法相比,显示了鲁棒优化设计的优越性,能使结构具有更稳定的性能。     

复合材料支撑机翼撑杆位置与结构综合优化设计

针对复合材料支撑机翼,发展了一种撑杆位置和结构综合优化设计的方法。在两种严重设计载荷状态下,考虑气动弹性效应和复合材料铺层结构的不确定性,以结构质量最小化为目标,以翼尖垂直变形、翼尖扭角、撑杆屈曲稳定性、颤振速度和强度要求为约束,在一个优化过程中实现了撑杆位置和结构参数的同步优化设计和鲁棒优化设计。结果表明,翼尖垂直变形和颤振速度要求对于撑杆位置影响明显,最优的撑杆展向位置都靠近翼根一侧,同时撑杆的总体稳定性成为同步优化设计的关键约束。鲁棒优化设计得到的撑杆位置和结构参数的最优组合对铺层结构的不确定性摄动具有良好的抗干扰性,鲁棒优化得到的最优撑杆位置会随着设计变量摄动范围而变化,翼尖垂直变形成为鲁棒优化设计的关键约束。     

汽车动力总成悬置系统鲁棒优化算法

应用鲁棒优化设计理论,考虑设计变量的不确定性对优化设计结果的影响,建立鲁棒优化模型。以动力总成悬置系统能量解耦为目标,悬置刚度参数为设计变量,考虑设计目标的均值和标准差,建立动力总成悬置系统的鲁棒优化模型。针对粒子群算法求解容易陷入局部最优解的问题,采用混合粒子群算法对动力总成悬置系统的悬置刚度参数进行鲁棒优化,并用Monte Carlo方法进行分析,以考察设计值的变化对目标函数的影响。结果表明,优化方法可以有效提高悬置系统的鲁棒性。     

结构动力鲁棒优化设计方法综述

如何提高结构动力学性能的鲁棒性,以减小各种不确定性因素对设计结果的影响是当前学术界和工程界研究和关注的热点问题之一。该文阐述了结构动力鲁棒优化设计的基本概念,从基于Taguchi的方法、基于多目标优化的方法和基于响应面建模的方法三个方面对结构动力鲁棒优化设计的研究进行了综述。以双转子为例,从结构的动力响应要求出发,采用响应面建模、多目标优化的方法进行了设计并与采用Taguchi方法得到的结果进行比较。结果表明,基于响应面建模、多目标优化的方法能够获得多个具有鲁棒性的设计方案,在处理具有不确定性的结构动力学问题时有着很大的应用潜力。最后,对当前方法和后续研究内容作了简要总结和展望。     

概率及非概率不确定性条件下结构鲁棒设计方法

提出了在概率不确定性和非概率不确定性同时存在时的约束函数鲁棒性和目标函数鲁棒性的实现策略及结构鲁棒设计方法。将传统优化设计问题的约束条件改造成为能同时反映两类不确定性量波动变化影响的约束条件,以实现约束函数的鲁棒性;在传统优化设计问题目标函数中增加若干个关于目标函数灵敏度的新目标函数,构成一个多目标函数设计问题,以实现目标函数的鲁棒性。所提方法应用于一个10杆桁架结构设计,采用宽容排序法求解。计算结果表明,在相同的结构总质量限制条件下,目标函数鲁棒性程度随着变量不确定性程度的增加而降低;在相同的变量不确定性程度条件下,增加结构总质量能提高目标函数鲁棒性的程度。     

基于6σ的动力总成悬置系统鲁棒优化设计

为提高动力总成悬置系统优化设计的鲁棒性,给出一种6σ鲁棒优化设计方法.将动力总成视为六自由度刚体,由四个橡胶悬置支撑在副车架上,每个悬置被简化成沿其三个垂直的弹性主轴方向具有刚度和阻尼的元件.将四个悬置的安装位置、三向静刚度和两个防扭悬置的安装方位角选为不确定性设计变量,设计变量的不确定性由其名义值加、减一个摄动量(或名义值的一个百分比)来表征.通过优化设计变量的名义值得到动力总成悬置系统的6σ鲁棒最优设计.优化结果表明,6σ鲁棒最优设计能够保证动力总成悬置系统以较好地鲁棒性满足固有频率、解耦率以及频率间隔的要求.     

基于极小极大原理的内模PID控制器研究

针对PID控制器的鲁棒性问题,提出了参数摄动范围下的鲁棒内模PID控制器的优化设计方法,应用极小极大原理设计出基于模型失配的最坏情况下的鲁棒内模PID控制器.通过鲁棒性能指标和控制参数的合理设计,使内模PID控制器较好地适应一定范围的模型不确定性.仿真结果表明,与PID控制器相比,内模PID控制器有很好的鲁棒性.     

复杂产品的多学科鲁棒协同设计优化方法研究

为了处理好复杂产品各子系统之间的耦合关系以及各子系统的异构性问题,以协同优化(CO)算法为基础,结合系统不确定分析(SUA)方法和近似不确定传播(IUP)方法,构建了多学科鲁棒协同设计优化算法框架.在设计变量的不确定性能够被概率分布函数描述的情况下,此算法框架能够解决复杂产品的设计优化问题.通过对梳齿式微加速度计的多学科鲁棒协同优化设计算例的计算,验证了此算法在输入参数存在微小扰动的情况下能够有效提高设计解的鲁棒性.     

大展弦比复合材料机翼气动弹性优化

使用遗传/ 敏度混合优化算法对大展弦比复合材料机翼进行气动弹性优化设计研究。在满足强度、位移、发散速度和颤振速度等约束条件的前提下, 以机翼各部件复合材料铺层的厚度为设计变量, 对结构进行重量最小化设计。研究表明: 弯曲变形严重影响最终的优化重量, 是设计大展弦比复合材料机翼结构时应该重点考虑的问题; 按照应力设计准则对这类结构进行设计, 往往很难满足弯曲变形的要求; 使用遗传/ 敏度混合优化算法对大展弦比复合材料机翼进行气动弹性优化设计能够在可以接受的计算耗费下获得满意的结果。      

一种基于响应面的翼型鲁棒优化设计方法

针对不确定因素引起的翼型气动性能波动现象,探讨了翼型几何形状随机不确定波动对翼型气动特性的影响,并进行了减小此影响的鲁棒优化设计。引入类别形状函数变换(CST)方法可以大大降低设计变量自由度数目,以NACA0012翼型为例,进行了考虑翼型几何形状随机扰动的气动不确定性分析,发现几何形状的微小波动对升力特性影响很小。发展了一种基于响应面和遗传算法的鲁棒优化设计方法,能够高效的减小阻力及其波动的影响。计算结果显示尽管相对于确定性优化结果鲁棒优化阻力略有增加,但波动更小,气动性能具有更好的鲁 棒性。     

基于几何因子的复合材料气动弹性剪裁设计

实现了基于几何因子的复合材料层合板建模,解决了几何因子与Natran的参数输入问题,并根据工艺约束中的最小铺层比例对几何因子可行空间进行了推导补充。在此基础上,提出了一种基于几何因子和Nastran的复合材料气动弹性剪裁优化设计方法。首先以总厚度和几何因子作为设计变量以及以Nastran作为求解器,以强度、刚度、颤振和发散速度以及几何因子相关性约束作为约束条件进行结构寻优,得到最优的铺层总厚度和几何因子。其次,以最优几何因子作为目标,进行铺层结构逆问题求解,约束条件为复合材料铺层工艺约束。因几何因子为铺层厚度和铺层顺序的表达式,与传统的多级优化相比,以几何因子作为设计变量可以避免铺层厚度和铺层顺序的解耦,进而获得更大的设计空间,且得到的铺层结构可以满足工艺约束。最后,对一矩形悬臂复合材料层合板进行剪裁设计,使得铺层结构满足气动弹性约束且质量最小。结果显示,运用该优化方法可以得到质量更小且满足工艺约束的铺层结构。     

A kriging metamodel-assisted robust optimization method based on a reverse model

The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer–inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.     

Gearbox design for uncertain load requirements using active robust optimization

Design and optimization of gear transmissions have been intensively studied, but surprisingly the robustness of the resulting optimal design to uncertain loads has never been considered. Active Robust (AR) optimization is a methodology to design products that attain robustness to uncertain or changing environmental conditions through adaptation. In this study the AR methodology is utilized to optimize the number of transmissions, as well as their gearing ratios, for an uncertain load demand. The problem is formulated as a bi-objective optimization problem where the objectives are to satisfy the load demand in the most energy efficient manner and to minimize production cost. The results show that this approach can find a set of robust designs, revealing a trade-off between energy efficiency and production cost. This can serve as a useful decision-making tool for the gearbox design process, as well as for other applications.     

Multi-objective robust optimization design of a front-end underframe structure for a high-speed train

A multi-objective robust design optimization of a front-end underframe structure for application in high-speed trains is proposed and the structural parameter uncertainty is considered. A finite element model of the structure is developed and verified by dynamic impact experiments. The sensitivity analysis demonstrates that the thicknesses of the centre sill have significant influences on structural crushing behaviours. The specific energy absorption and the initial peak crushing force (F_p) are taken as optimization objectives. Compared with the baseline structure, the 6-sigma robust design shows that the F_p and the structural mass are reduced by 54.86% and 13.06%, respectively, and the robust optimum is more reliable. The 6-sigma robust optimal solution has an efficient energy-absorbing capacity while satisfying the design constraint. Thus, 6-sigma robust optimization can be applied for high-speed trains.     

Root cause analysis strategy for robust design domain recognition

Design domain identification with desirable attributes (e.g. feasibility, robustness and reliability) provides advantages when tackling large-scale engineering optimization problems. For the purpose of dealing with feasibility robustness design problems, this article proposes a root cause analysis (RCA) strategy to identify desirable design domains by investigating the root causes of performance indicator variation for the starting sampling initiation of evolutionary algorithms. The iterative dichotomizer 3 method using a decision tree technique is applied to identify reduced feasible design domain sets. The robustness of candidate domains is then evaluated through a probabilistic principal component analysis-based criterion. The identified robust design domains enable optimal designs to be obtained that are relatively insensitive to input variations. An analytical example and an automotive structural optimization problem are demonstrated to show the validity of the proposed RCA strategy.     

New conceptual design of aeroelastic wing structures by multi-objective optimization

Internal structural layouts and component sizes of aircraft wing structures have a significant impact on aircraft performance such as aeroelastic characteristics and mass. This work presents an approach to achieve simultaneous partial topology and sizing optimization of a three-dimensional wing-box structure. A multi-objective optimization problem is assigned to optimize lift effectiveness, buckling factor and mass of a structure. Design constraints include divergence and flutter speeds, buckling factor and stresses. The topology and sizing design variables for wing internal components are based on a ground element approach. The design problem is solved by multi-objective population-based incremental learning (MOPBIL). The Pareto optimum results lead to unconventional wing structures that are superior to their conventional counterparts.