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。     

基于多分辨率-多边形单元建模策略的多材料结构动刚度拓扑优化方法

利用多边形有限单元的高精度求解优势,融合多分辨率拓扑优化方法,实现粗糙位移网格条件下的高分辨率构型设计,由此提出多材料结构动刚度问题的拓扑优化方法。将多边形单元(位移场求解单元)劈分为精细的小单元,构造设计变量与密度变量的重叠网格,形成多分辨率-多边形单元的优化建模策略;以平均动柔度最小化为目标和多材料的体积占比为约束,建立多材料结构的动力学拓扑优化模型,通过HHT-α方法求解结构动响应,采用伴随变量法推导目标函数和约束的灵敏度表达式,利用基于敏度分离技术的ZPR设计变量更新方案构建多区域体积约束问题的优化迭代格式;通过典型数值算例分析优化方法的可行性和动态载荷作用时间对优化结果的影响机制。     

复合材料机翼鲁棒气动弹性优化设计

针对气动弹性结构, 利用遗传-敏度混合算法开展鲁棒优化设计。以大展弦比复合材料机翼的鲁棒气动弹性结构优化设计为例验证了鲁棒设计方法的适用性和有效性, 比较了鲁棒结构优化设计与传统优化设计的区别。研究结果表明: 在设计变量存在不确定性的情况下, 考虑鲁棒性约束优化得到的结构较传统优化结构具有更好的抗干扰性; 但鲁棒性的满足是以增加结构质量为代价的, 鲁棒性要求越高, 结构增重越明显。      

考虑泊松效应的材料/结构一体化设计方法

为实现含有不同泊松比组分复合材料的优化设计,并考虑宏观结构及复杂的边界条件,提出了考虑泊松效应的材料/结构一体化设计方法,其显著特征在于不同组分材料中引入了泊松比插值,假设宏观结构由周期性排列的复合材料组成,复合材料含两种各向同性且泊松比不同的组分材料,以静态问题中柔顺度最小化或动态问题中特征值最大化为目标以及宏微观体积比为约束建立了拓扑优化模型。采用均匀化理论预测了复合材料等效性能,推导了目标函数对宏微观密度变量的敏度表达式。分别采用密度过滤和敏度过滤来消除宏微观拓扑优化中的不稳定性现象。采用优化准则法分别更新宏观、微观密度变量,考察了微观体积比和组分材料泊松比参数对优化结果的影响。三维数值算例结果表明所提出的一体化方法具有可行性和优越性。     

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。     

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。     

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。     

约束阻尼结构的双向渐进拓扑优化

针对约束层阻尼板的拓扑优化问题,以模态损耗因子最大化为目标函数,约束阻尼材料体积分数为约束条件,建立了约束阻尼板的拓扑优化模型。基于模态应变能方法,推导了目标函数对设计变量的灵敏度。采用双向渐进优化算法(BESO)对约束阻尼材料的布局进行了拓扑优化,获得了约束阻尼材料的最优拓扑构型,并与渐进优化算法(ESO)进行了比较。研究结果表明：双向渐进优化算法相比单向渐进优化算法,获得的模态损耗因子更高,阻尼减振效果更好。     

一种改进的广义遗传算法及其在鲁棒优化问题中的应用

提出一种改进的广义遗传算法,算法中引入了异种机制以提高种群的多样性,在保证收敛速度的同时防止了早熟收敛。将该方法应用于复杂载荷作用下结构的鲁棒优化问题,并采用Taguchi望目特性的SN比构造了遗传算法的目标函数。数值算例表明,异种机制能够有效地提高广义遗传算法收敛于全局最优解的概率,加快收敛速度;结合了Taguchi鲁棒设计方法的广义遗传算法能够有效地求解复杂载荷作用下带有不确定参数的结构鲁棒优化问题。     

基于动响应约束的结构材料优化设计

针对于随机荷载作用下动响应为约束的结构材料优化问题,基于结构拓扑优化思想,提出了一种变动响应约束的结构材料优化方法。采用分式有理式和幂函数识别结构材料单元特性参数,以微观单元拓扑变量倒数为设计变量,导出了频率及振型对微观单元设计变量的一阶导数,进而得到了随机荷载作用下结构均方响应的一阶近似展开式。结合变约束限的思想,建立了以结构质量作为目标函数,均方响应作为约束条件的连续体微结构拓扑优化近似模型,并采用对偶方法进行求解。对典型结构进行了考虑单个和多个动响应约束的结构材料优化设计,优化所得结果验证了该方法的有效性和可行性。     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

汽车车身耐撞性设计参数的区间稳健性优化

基于区间分析,提出了一种考虑公差的汽车车身耐撞性稳健优化设计模型,可在有效降低耐撞性能对设计参数波动敏感性的同时实现公差范围的最大化。该模型首先利用对称公差来描述汽车碰撞模型中车身关键耐撞部件的主要尺寸、位置和形状等设计参数本身的不确定性,然后将参数设计和公差设计相结合,建立了以稳健性评价指标和公差评价指标为优化目标,设计变量名义值和公差同步优化的多目标优化模型。再次,利用区间可能度处理不确定约束,将该优化模型转换为确定性多目标优化模型。最后,将该模型应用于两个汽车耐撞性优化设计问题,并通过序列二次规划法和改进的非支配排序遗传算法进行求解,结果表明该方法及稳健优化设计模型可行且实用。     

基于鞍点逼近的机械结构可靠性稳健优化设计

将可靠性优化设计理论与可靠性灵敏度分析方法相结合,讨论了机械零部件稳健优化设计的问题.系统地推导了基于鞍点逼近的可靠性灵敏度公式,并把可靠性灵敏度计算结果融入可靠性稳健优化设计模型之中,将可靠性稳健优化设计归结为满足可靠性要求的多目标优化问题.在基本随机参数概率分布已知的前提下,应用鞍点逼近技术,得到极限状态函数的分布函数与概率密度函数,并且将此结果应用到机械零部件的可靠性灵敏度分析中,进而实现了机械零部件的可靠性稳健优化设计.通过与Monte-Carlo方法计算所得的结果相比可知,应用鞍点逼近技术可以迅速、准确地得到机械零部件可靠性稳健设计信息．     

汽车零部件的可靠性稳健优化设计——理论部分*

将可靠性优化设计理论与可靠性灵敏度分析方法相结合，讨论了汽车零部件的可靠性稳健优化设计问题，提出了可靠性稳健优化设计的计算方法。把可靠性灵敏度融入可靠性优化设计模型之中，将可靠性稳健优化设计归结为满足可靠性要求的多目标优化问题。     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Developing a fuzzy proportional–derivative controller optimization engine for engineering design optimization problems

In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.     

Optimum design of concrete cable-stayed bridges

The design of cable-stayed bridges involves a significant number of design variables and design objectives. The concrete cable-stayed bridge optimization is formulated here as a multi-objective optimization problem with objectives of minimum cost, minimum deflections and minimum stresses. A numerical method is developed to obtain the optimum design of such structures. This numerical method includes: structural analysis, sensitivity analysis and optimization. The structural analysis accounts for all the relevant effects (concrete time-dependent effects, construction stages and geometrical nonlinear effects). The structural response to changes in the design variables is achieved by a discrete direct sensitivity analysis procedure, and an entropy-based approach was used for structural optimization. The features and applicability of the proposed method are demonstrated by numerical examples concerning the optimization of a real-sized concrete cable-stayed bridge.     

Horsetail matching: a flexible approach to optimization under uncertainty*

It is important to design engineering systems to be robust with respect to uncertainties in the design process. Often, this is done by considering statistical moments, but over-reliance on statistical moments when formulating a robust optimization can produce designs that are stochastically dominated by other feasible designs. This article instead proposes a formulation for optimization under uncertainty that minimizes the difference between a design's cumulative distribution function and a target. A standard target is proposed that produces stochastically non-dominated designs, but the formulation also offers enough flexibility to recover existing approaches for robust optimization. A numerical implementation is developed that employs kernels to give a differentiable objective function. The method is applied to algebraic test problems and a robust transonic airfoil design problem where it is compared to multi-objective, weighted-sum and density matching approaches to robust optimization; several advantages over these existing methods are demonstrated.     

Game-theory approach for multi-objective optimal design of stationary flat-plate solar collectors

A game-theory approach has been used for the multi-objective optimum design of stationary flat-plate solar collectors. The clear-day solar-beam radiation and diffuse radiation at the location of the solar collector (Miami) are estimated. Three objectives are considered in the optimization-problem formulation: maximization of the annual average incident solar energy; maximization of the lowest month incident solar energy; and minimization of costs. The game-theory methodology is used for the solution of the three objective-constrained optimization problems to find a balanced solution. This solution represents the best compromise in terms of the super-criterion selected. Two types of sensitivity analyses are conducted on the optimum solution in this work. The sensitivity analysis with respect to the design variables indicates which design valuables are more important to different objective functions. The sensitivity analysis with respect to the solar constant shows that small fluctuations of solar constant experienced in practice affect the various objectives very little, thereby indicating that the mathematical model is robust. This work represents the first work aimed at the application of multi-objective optimization strategy, particularly the game theory approach, for the solution of the solar collector design problem.