结构拓扑优化ICM显式化与抛物型凝聚函数对于应力约束的集成化

对于连续体结构拓扑优化问题,该文指出:其最大求解困难除因0-1离散变量的本质造成,更因为目标函数、约束条件在理论上无法建立同0-1拓扑变量的直接联系。ICM(独立-连续-映射)方法解决了这一困难:用阶跃函数构筑了0-1变量与单元具体物理量或几何量联系的桥梁;将其逆函数(我们称之为跨栏函数)代入具体的优化表达式中,就可以显化拓扑优化模型与0-1变量的关系;其中跨栏函数用其逼近函数——过滤函数代替之,于是,不可微的结构拓扑优化规划就被可微化;进而可调常用的光滑算法进行有效的求解了。在磨光函数和过滤函数分别逼近阶跃函数和跨栏函数的同时,传统的0-1拓扑离散变量扩展为[0,1]区间上的连续变量了。由于许用应力、弹性模量、密度等材料性质被过滤函数识别,因而产生了相应的单元全程性质的概念,同理定义了单元全程几何参数的概念。基于ICM方法,利用单元全程许用应力,可以便捷地推导出结构拓扑优化应力奇异问题的ε-松弛解法的公式。提出了抛物型凝聚函数,证明了相关的定理,并且用于应力凝聚化处理的拓扑优化问题求解。另外,数值算例表明该文方法是有效的。     

屈曲与应力约束下连续体结构的拓扑优化

基于ICM(独立、连续、映射)方法建立了以结构重量最小为目标,以屈曲临界力、应力同时为约束的连续体拓扑优化模型:采用独立的连续拓扑变量,借助泰勒展式、过滤函数将目标函数作二阶近似展开;借助瑞利商、泰勒展式、过滤函数将屈曲约束化为近似显函数;将应力这种局部性约束采用全局化策略进行处理,即借助第四强度理论、过滤函数将应力局部性约束转化为应变能约束,大大减少了灵敏度分析的计算量;将优化模型转化为对偶规划,减少了设计变量的数目,并利用序列二次规划求解,缩小了模型的求解规模。数值算例表明:该方法可以有效地解决屈曲与应力约束共同作用的连续体拓扑优化问题,能够得到合理的拓扑结构,并有较高的计算效率。     

离散变量结构拓扑优化设计综合算法的进一步探讨

本文对离散变量结构拓扑优化设计的综合设计方法作了进一步的研究。通过对离散变量结构拓扑优化设计综合算法的数学模型与传统的拓扑估化模型所作的比较,指出因为综合算法的拓扑优化模型中既所含了截面变量又包含拓扑变量,它反击了结构拓扑优化的本质,从而能有效地避免“奇异拓扑”的问题。由于模型的目标函数和约束函数的单调性,从而可以高效地利用相对差商法进行求解。通过数值实验对综合算法的数值稳定性进行了讨论,为应用于     

结构拓扑优化的发展及其模型转化为独立层次的迫切性

回顾了结构拓扑优化的发展过程,叙述了结构拓扑优化的研究现状,阐述了各种常用的拓扑优化方法。结合作者在结构拓扑优化方面的研究工作,介绍了ICM(独立、连续、映射)方法,并通过连续结构和离散结构的典型数值算例说明了将结构拓扑优化的模型转化为独立层次进行研究的迫切性和重要性。     

基于ICM方法的层合板结构流固耦合频率约束拓扑优化

该文基于独立、连续、映射(independent continuous mapping, ICM)的拓扑优化方法,针对层合板结构频率约束下流固耦合的拓扑优化问题进行了建模与求解。利用格林公式与瑞利商,进行了优化模型频率约束的显式化,并基于泰勒线性近似的方法推导了设计灵敏公式,同时采用对偶序列二次规划求解了该模型。另外,通过引入修正的Heaviside函数对拓扑变量进行了离散化处理。利用PCL(Patran Command Language)二次开发平台对现有MSC.Patran软件进行二次开发,并通过MSC.Nastran软件求解器,实现了优化算法。数值算例证明了该文程序与算法的有效性与可行性。     

结构畸变比能处理的应力约束全局化的连续体结构拓扑优化

该文根据von Mises强度准则的畸变比能本质,计算单元畸变比能替代应力约束;依照应力全局化策略,定义结构畸变比能约束概念,求解应力约束下重量最小的连续体结构拓扑优化问题,急剧地减少了应力约束。构造许用应力和结构最大应力的比值含参数幂函数,对约束限进行动态修正。基于ICM(Independent Continuous and Mapping,独立、连续、映射)方法,采用指数型快滤函数建立了结构在畸变比能约束下的结构拓扑优化模型,并选取精确映射下的序列二次规划进行求解。数值算例表明:采用修正的结构畸变比能的应力全局化策略,对于结构拓扑优化问题的求解是有用和高效的。该文提出的方法对解决工况间存在病态载荷的问题也是有益的。     

基于变频率区间约束的结构材料优化设计

针对频率约束的结构材料优化问题,基于结构拓扑优化思想,提出变频率区间约束的结构材料优化方法。借鉴均匀化及ICM(独立、连续、映射)方法,以微观单元拓扑变量倒数为设计变量,导出宏观单元等效质量矩阵及导数,进而获得频率一阶近似展开式。结合变频率区间约束思想,获得以结构质量为目标函数、频率为约束条件的连续体微结构拓扑优化近似模型;采用对偶方法求解。通过算例验证该方法的有效性及可行性,表明考虑质量矩阵变化影响所得优化结果更合理。     

基于对数型Heaviside近似函数作为过滤函数的动力响应结构拓扑优化ICM方法

应用ICM(Independent Continuous and Mapping)方法, 建立了以重量最小为目标函数, 以连续频率带或离散点频率的简谐激励下的响应振幅为约束的拓扑优化模型. 引入了对数型Heaviside近似函数作为过滤函数, 并做了敏度分析, 利用对偶二次规划进行优化模型的求解, 并运用敏度过滤的方法处理动力响应数值不稳定的问题. 数值算例比较了利用对数型函数和幂函数作为过滤函数时对拓扑结构的影响, 结果显示利用对数型函数较幂函数结构优化迭代次数更少, 收敛更快.     

基于物质点描述的双向渐进式拓扑优化方法

:为了克服连续体结构拓扑优化中的数值不稳定现象,定义了表征物质点及其领域有无的物质点拓扑变量,提出基于物质点描述的双向渐进式拓扑优化方法.基于过滤法构造拓扑变量场的插值函数,从而在拓扑优化模型中自然消除了棋盘格现象.为适用于不同单元类型和网格离散形式等,重新定义了灵敏度密度.通过二维数值算例对理论方法进行验证.结果表明:方法在连续体结构拓扑优化设计中具有可行性和有效性.     

力学映射下板壳结构的截面离散优化设计

依据力学映射的方法把离散变量优化问题转换为连续变量优化问题,通过在连续优化最优解附近构造两节模型,采用无穷小单元的无穷组合方法和变量连接术对板壳结构进行优化。同时在MSC公司的Nastran软件平台上利用其PCL语言开发出优化模块,并进行了算例的优化设计。     

An exact penalty function method for optimising QAP formulation in facility layout problem

A quadratic assignment problem (QAP), which is a combinatorial optimisation problem, is developed to model the problem of locating facilities with material flows between them. The aim of solving the QAP formulation for a facility layout problem (FLP) is to increase a system’s operating efficiency by reducing material handling costs, which can be measured by interdepartmental distances and flows. The QAP-formulated FLP can be viewed as a discrete optimisation problem, where the quadratic objective function is optimised with respect to discrete decision variables subject to linear equality constraints. The conventional approach for solving this discrete optimisation problem is to use the linearisation of the quadratic objective function whereby additional discrete variables and constraints are introduced. The adoption of the linearisation process can result in a significantly increased number of variables and constraints; solving the resulting problem can therefore be challenging. In this paper, a new approach is introduced to solve this discrete optimisation problem. First, the discrete optimisation problem is transformed into an equivalent nonlinear optimisation problem involving only continuous decision variables by introducing quadratic inequality constraints. The number of variables, however, remains the same as the original problem. Then, an exact penalty function method is applied to convert this transformed continuous optimisation problem into an unconstrained continuous optimisation problem. An improved backtracking search algorithm is then developed to solve the unconstrained optimisation problem. Numerical computation results demonstrate the effectiveness of the proposed new approach.     

A NEW APPROACH TO NONLINEAR MIXED DISCRETE PROGRAMMING PROBLEMS

Abstract

In this paper a novel method is developed for the numerical solution of a non-linear mixed discrete programming problem. The problem is first transformed into another mixed zero-one discrete optimization problem by replacing each of the discrete variables by a set of new variables with a linear constraint. Each of the new discrete variable sets is then made continuous on [0,1] by introducing a quadratic constraints. Thus the original mixed discrete problem is transformed into a continuous nonlinear optimization problem with constraints. The numerical experiments, performed to demonstrate the effectiveness of the method, show that the method is superior to the previous ones.     

An engineering method for complex structural optimization involving both size and topology design variables

This work presents an engineering method for optimizing structures made of bars, beams, plates, or a combination of those components. Corresponding problems involve both continuous (size) and discrete (topology) variables. Using a branched multipoint approximate function, which involves such mixed variables, a series of sequential approximate problems are constructed to make the primal problem explicit. To solve the approximate problems, genetic algorithm (GA) is utilized to optimize discrete variables, and when calculating individual fitness values in GA, a second-level approximate problem only involving retained continuous variables is built to optimize continuous variables. The solution to the second-level approximate problem can be easily obtained with dual methods. Structural analyses are only needed before improving the branched approximate functions in the iteration cycles. The method aims at optimal design of discrete structures consisting of bars, beams, plates, or other components. Numerical examples are given to illustrate its effectiveness, including frame topology optimization, layout optimization of stiffeners modeled with beams or shells, concurrent layout optimization of beam and shell components, and an application in a microsatellite structure. Optimization results show that the number of structural analyses is dramatically decreased when compared with pure GA while even comparable to pure sizing optimization.     

A discrete stress–strength interference model based on universal generating function

Continuous stress–strength interference (SSI) model regards stress and strength as continuous random variables with known probability density function. This, to some extent, results in a limitation of its application. In this paper, stress and strength are treated as discrete random variables, and a discrete SSI model is presented by using the universal generating function (UGF) method. Finally, case studies demonstrate the validity of the discrete model in a variety of circumstances, in which stress and strength can be represented by continuous random variables, discrete random variables, or two groups of experimental data.     

An efficient algorithm for the optimum design of trusses with discrete variables

A method to efficiently solve the problem of minimum weight design of plane and space trusses with discrete or mixed variables is developed. The method can also be applied to continuous variables. The original formulation leads to a non-linear constrained minimization problem with inequality constraints, which is solved by means of a sequence of approximate problems using dual techniques. In the dual space, the objective function is to be maximized, depends on continuous variables, is concave and has first and second order discontinuities. In addition, the constraints deal simply with restricting the dual variables to be non-negative. To solve the problem an ad hoc algorithm from mathematical programming has been adapted. Some examples have been developed to show the effectiveness of the method.     

A comparison of the continuous and discrete adjoint approach extended based on the standard lattice Boltzmann method in flow field inverse optimization problems

The objective of this research is the numerical implementation and comparison between the performance of the continuous and discrete adjoint Lattice Boltzmann (LB) methods in optimization problems of unsteady flow fields. For this purpose, a periodic two-dimensional incompressible channel flow affected by the constant and uniform body forces is considered as the base flow field. The standard LB method and D2Q9 model are employed to solve the flow field. Moreover, the inverse optimization of the selected flow field is defined by considering the body forces as the design variables and the sum of squared errors of flow field variables on the whole field as the cost function. In this regard, the continuous and discrete adjoint approaches extended based on the LB method are used to achieve the gradients of the cost function with respect to the design variables. Finally, the numerical results obtained from the continuous adjoint LB method are compared with the discrete one, and the accuracy and efficiency of them are discussed. In addition, the validity of the obtained cost function gradients is investigated by comparing with the results of the standard forward finite difference and complex step methods. The numerical results show that regardless of the implementation cost of the two approaches, the computational cost to evaluate the gradients in each optimization cycle for the discrete adjoint LB approach is slightly more than the other one but has a little higher convergence rate and needs a smaller number of cycles to converge. Besides, the gradients obtained from the discrete version have a better agreement with those of the complex step method. Eventually, based on the structural similarities of the continuous LB equation and its corresponding adjoint one and using the simple periodic and complete bounce-back boundary conditions for the LB equation, the improved boundary conditions for the continuous adjoint LB equation are presented. The numerical results show that the use of these boundary conditions instead of the original adjoint boundary conditions significantly improves the relative accuracy and also the convergence rate of the continuous adjoint LB method.     

求解二进制二次规划问题的一种连续化方法

本文提出了一种求解二进制二次规划问题的连续化方法。首先利用NCP函数方法,将二进制变量转化为等价的非光滑方程,再用凝聚函数法对其进行光滑化处理,从而把原来的组合优化问题转化成了一般的可微非线性规划问题。通过对一些标准考题进行计算,表明了该连续化方法的可行性、高效性以及稳定性。     

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.     

基于SA算法的行星齿轮减速器离散变量优化设计

考虑制造工艺要求,将所有设计变量均视为离散变量,包括一般离散变量和伪离散变量,并就这两种情况下状态产生函数的设计原理进行深入研究,解决了将模拟退火算法用于离散变量函数优化的关键技术问题,介绍了一种基于模拟退火算法的离散变量函数优化的新方法。行星齿轮传动中各齿轮的齿数受传动比条件、同轴条件和装配条件的限制而不能任意取值,齿轮的模数也要受国家标准的制约只能取一些离散值,用以数学规划理论为基础的经典约束优化方法求解效果很差,用基于模拟退火算法的离散变量优化设计方法则可以方便快捷地获得满足各方面要求的最优设计方案。     

A new harmony search algorithm for solving mixed–discrete engineering optimization problems

In general design optimization problems, it is usually assumed that the design variables are continuous. However, many practical problems in engineering design require considering the design variables as integer or discrete values. The presence of discrete and integer variables along with continuous variables adds to the complexity of the optimization problem. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This article presents a mixed–discrete harmony search approach for solving these nonlinear optimization problems which contain integer, discrete and continuous variables. Some engineering design examples are also presented to demonstrate the effectiveness of the proposed method.