胶印机滚筒结构的拓扑及尺寸优化设计

胶印机滚筒在印刷压力作用下的挠曲变形是影响印品质量的重要原因。以PZ1740胶印机橡皮滚筒为研究对象,提出了一种基于拓扑优化和尺寸优化的橡皮滚筒结构设计方法。建立了滚筒结构的二维拓扑模型,以应变能最小为目标,体积分数作为约束,得到滚筒结构的拓扑分布;在拓扑构型基础上,建立了滚筒结构的参数化模型,以挠曲变形最小为目标,结构质量作为约束条件进行尺寸优化,得到了滚筒具体的结构。优化分析结果表明,优化后的滚筒结构在质量约束的条件下有效地减小了挠曲变形,提出的方法在胶印机滚筒结构优化设计中可行且有效。     

平面桁架结构拓扑优化设计的改进智能算法

结构拓扑及形状退火算法(STSA)用于桁架结构拓扑优化设计,其优化特点为注重结构构型的改变而较少考虑结构的力学性能,而针对既定几何构型的桁架结构截面优化,满应力准则法(FSD)具有明显优势,因此,将其引入退火历程改进STSA。提出结构几何构型状态相对稳定判别方法,并以结构构型状态相对稳定作为引入FSD的最佳时机形成杂交算法。算例分析表明：该改进智能算法使寻优搜索过程更为稳定,其表现为搜索效率、鲁棒性和最优解均得以改善。     

漫谈结构优化设计

文章简明的阐述结构优化设计相关的一些基本问题,其目的在于向安装工程结构设计人员推介这门新技术.     

地面运动激励下结构的动力学形状优化设计

通过推广和修改ESO方法来进行结构形状优化设计，以达到控制地面运动激励下的结构随机动力响应的目的，根据工程实际要求，用随机动力学理论构造具有白噪声功率谱的地面运动的随机响应表达式。基于特征导数的模态截断法和近似处理，导出了一套平均均方动响应的灵敏度公式，在优化软件上实现了形状优化算法，提供的算例显示了本方法的合理性和有效性。     

离散变量桁架结构拓扑优化设计的混合算法

将相对差商法和混沌优化结合起来,形成求解离散变量桁架结构拓扑优化设计的混合算法。利用相对差商法可以对离散变量快速寻优的特点,及混沌变量的全局遍历性,可以有效地跳出局部最优解,达到拓扑优化全局寻优的目的。通过采用和准最优解的对比及几何稳定性的判断等辅助性技术,降低了重分析次数。同时,高效的重分析方法的结合,提高了求解的效率,也避免了拓扑优化问题中求解的一些困难。算例表明,该算法对于离散变量的拓扑优化设计问题是快速有效的。     

空间桁架结构拓扑优化设计的线性规划方法

本文以杆件内力为设计变量,构造了多工况作用下空间桁架结构拓扑优化的线性规划模型,考虑了应力和位移约束,能够避免奇异最优拓扑和不稳定结构的产生。     

桁架结构智能布局优化设计

结构的布局优化由于涉及尺寸、形状和拓扑三个层次的综合设计而成为优化问题中的难点,结合桁架结构提出了一个基于多个初始基结构的布局优化方法。以智能生成的、型式多样合理的基结构代替传统模型中的单一基结构,然后从不同基结构下的拓扑优化结果中找出最优设计。在克服传统基结构法有可能限制求解空间而丢失最优解这一局限性的同时,将形状和拓扑优化设计有效分离,降低了求解的难度,并且结合拓扑变化法,实现了桁架结构从选型生成、分析计算到优化设计的一体化智能设计过程。算例表明:利用该文提出的方法进行桁架结构的最优布局设计是可靠有效的。     

液压挖掘机斗杆结构的拓扑优化设计

为了提高斗杆的刚度并减小其质量,以BD85W型液压挖掘机的斗杆为例,应用变密度拓扑优化方法,以刚度最大为目标,以体积分数为约束,对斗杆结构进行优化设计,得到了液压挖掘机斗杆的新结构.斗杆的新旧结构对比表明:斗杆新结构体积减小18.22%,刚度增大6.58%,达到了预期的刚度要求.拓扑优化方法有利于提高产品的系列化程度和设计水平,能为挖掘机斗杆的结构分析和优化设计提供参考.     

基于人工材料的结构拓扑渐进优化设计

首先,提出了一种在结构边界和孔洞周围附加人工材料的思路。在此基础上,结合ESO方法和应力灵敏度,建立了结构有限单元增、删的准则, 给出了一种新的拓扑优化算法。算例表明该方法能采用固定有限元网格中不同的初始优化结构就可获得优化拓扑。由于其概念上的简洁性和应用上的有效性,该方法具有一定的工程应用价值。     

机械基础结构多目标拓扑优化设计方法

机械的基础结构在保证具有足够的刚度、强度和稳定性的条件下,经济性也必须要好,因此机械基础结构常采用内部布置有加筋板的箱体结构。以某机械基础结构为例,分别用基于经验设计的内部筋板布置方法和多目标拓扑优化方法进行优化设计,得到了2 种设计方案;比较了2 种方案的动静态力学性能。结果表明,多目标拓扑优化设计的基础结构比一般经验设计的结构,刚度有所提高,而结构质量减小11. 21%,一阶固有频率提高25. 07%。     

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 fixed‐grid bidirectional evolutionary structural optimization method and its applications in tunnelling engineering

Topology optimization has exhibited an exceptional capability of improving structural design. However, several typical topology optimization algorithms are finite element (FE) based, where mesh‐dependent zigzag representation of boundaries is barely avoidable in both intermediate and final results. To tackle the problem, this paper proposes a new fixed‐grid‐based bidirectional evolutionary structural optimization method, namely FG BESO. The adoption of an FG FE framework enables a continuous boundary change in the course of topology optimization, which provides a means of dealing with not only the non‐smooth boundary of the final design but also the interpretation of intermediate densities. As a class of important practical application, it is interesting to make use of the new FG BESO method to the reinforcement design for underground tunnels. To accommodate the FG BESO technique to geological engineering applications, a nodal sensitivity is derived for a two‐phase material model comprising the artificial reinforcement and original rock. In this paper, some innovative topological designs of tunnel reinforcements are presented for minimizing the floor and sidewall heaves under different geological loading conditions. Copyright © 2007 John Wiley & Sons, Ltd.     

桁架结构布局优化的并行子空间方法

由于结构布局优化存在设计变量类型众多和变量耦合等问题,采取合适的优化方法获得满足结构设计要求的最小质量的结构具有重要的工程意义.基于多学科设计优化方法中的并行子空间优化法,提出一种桁架结构布局优化的并行子空间优化方法.将结构布局设计问题按设计变量类型分为布局、形状和尺寸三个并行的子空间,设计变量在各自的子空间内单独优化,各子空间优化结束后,在系统级中协调3类设计变量,保持最小质量的子空间的优化设计变量不变,采用近似一维搜索的方法协调其他子空间的设计变量,然后进行下一次迭代直至收敛.2个算例表明该方法能够取得较好的优化结果,具有实际工程应用价值.     

Automated structural optimization system for integrated topology and shape optimization

Abstract

This paper combines previously developed techniques for image‐preprocessing and characteristic image‐interpreting together with a newly proposed automated shape‐optimization modeling technique into an integrated topology‐optimization and shape‐optimization system. As a result, structure designers are provided with an efficient and reliable automated structural optimization system (ASOS). The automated shape‐optimization modeling technique, the key technique in ASOS, uses hole‐expanding strategy, interference analysis, and hole shape‐adjusting strategy to automatically define the design variables and side constraints needed for shape optimization. This technique not only eliminates the need to manually define design variables and side constraints for shape optimization, but during the process of shape optimization also prevents interference between the interior holes and the exterior boundary. The ASOS is tested in three different structural configuration design examples.     

An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.     

Topology optimization for thermal insulation: an application to building engineering

This article deals with a numerical implementation for topology optimization that is based on the heat conduction equation and addresses problems such as the optimal design of thermal insulation in building engineering. The formulation handles heat diffusivity under the steady-state assumption for a domain with assigned convective-like boundary conditions. The optimization framework is implemented within a general-purpose finite-elements code that is set to solve the thermal problem iteratively, thus allowing for a straightforward handling of two-dimensional and three-dimensional problems. A few numerical results are firstly presented to compare classical formulations for maximum heat conduction and the addressed scheme for optimal thermal insulation. The proposed methodology is therefore exploited to cope with issues peculiar to the optimal design of building envelopes, such as the mitigation of the effects of thermal bridges and the design for minimum thermal transmittance of the components of a modular curtain wall.     

A unified parametric design approach to structural shape optimization

This paper presents a general parametric design approach for 2-D shape optimization problems. This approach has been achieved by integrating practical design methodologies into numerical procedures. It is characterized by three features: (i) automatic selection of a minimum number of shape design variables based on the CAD geometric model; (ii) integration of sequential convex programming algorithms to solve equality constrained optimization problems; (iii) efficient sensitivity analysis by means of the improved semi-analytical method. It is shown that shape design variables can be either manually or systematically identified with the help of equality constraints describing the relationship between geometric entities. Numerical solutions are performed to demonstrate the applicability of the proposed approach. A discussion of the results is also given:     

Application of a territorial-based filtering algorithm in turbomachinery blade design optimization

A territorial-based filtering algorithm (TBFA) is proposed as an integration tool in a multi-level design optimization methodology. The design evaluation burden is split between low- and high-cost levels in order to properly balance the cost and required accuracy in different design stages, based on the characteristics and requirements of the case at hand. TBFA is in charge of connecting those levels by selecting a given number of geometrically different promising solutions from the low-cost level to be evaluated in the high-cost level. Two test case studies, a Francis runner and a transonic fan rotor, have demonstrated the robustness and functionality of TBFA in real industrial optimization problems.     

A hybrid topology optimization algorithm for structural design

A hybrid algorithm for solving structural topology optimization problems is presented. This hybrid algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This hybrid algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value during the optimization. This hybrid algorithm has improved calculation efficiency and accelerated convergence when compared with the MMA or MGCMMA algorithm, which is demonstrated with three examples.     

Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

Genetic algorithms have already been applied to various fields of engineering problems as a general optimization tool in charge of expensive sampling of the coded design space. In order to reduce such a computational cost in practice, application of evolutionary strategies is growing rapidly in the adaptive use of problem‐specific information. This paper proposes a hybrid strategy to utilize a cooperative dynamic memory of more competitive solutions combining indirect information share in ant systems with direct constructive genetic search. Some proper coding techniques are employed to enable testing the method with various sets of control parameters. As a challenging field of interest, its application to structural layout optimization is considered while an example of a traveling salesman problem is also treated as a combinatorial benchmark. Copyright © 2007 John Wiley & Sons, Ltd.