锥优化模型在Robust桁架拓扑设计实例中的应用

本文讨论Robust桁架拓扑设计(TTD)问题，即桁架结构设计问题，使其在固定重量的情况下，具有最佳的承载能力．本文陈述了几种应用锥优化解Robust TTD问题的方法，并简介了锥优化最新的领域．同时，本文给出了一个单负荷的线性模型和一个多负荷的半正定优化模型以及Robust TTD问题．文中所有的模型均有例证．例证显示通过应用对偶性这些模型的规模能被充分的减小．     

实用下料优化问题模型建立及解法

“下料问题(cuttingstockproblem)”是把相同形状的一些原材料分割加工成若干个不同规格大小的零件的问题,此类问题在工程技术和工业生产中有着重要和广泛的应用.本文首先以材料最省为原则建立模型,采用分层基因算法模型求解出模型的解,若此结果不符合时间限制条件,则通过以客户时间需求为第一目标的分组抽样模型处理后,再借助分层基因算法给出该模型的最优解.     

应力和位移约束下连续体结构拓扑优化

同时考滤应力和位移约束的连续体结构拓扑优化问题,很难用现有的均匀方法或变密度方法等求解。主要困难在于难以建立应力和位移约束与拓扑设计变量间显式关系式;即使建立了这种关系,也由于优化问题规模过大,利用常规的数学规划方法难以求解。隋允康、杨德庆曾提出了基于独立连续拓扑变量及映射变换（ＩＣＭ）的桁架结构拓扑优化模型。本文在此基础上,建立了以重量为目标,考虑应力和位移约束的连续体结构拓扑优化模型,并推导出     

Computational Simulation of Bone Remodeling using Design Space Topology Optimization

The law of bone remodeling, commonly referred to as Wolff's Law, asserts that the internal trabecular bone adapts to external loadings, reorienting with the principal stress trajectories to optimize mechanical efficiency creating a naturally optimum structure. The current study utilized an advanced structural optimization algorithm, called design space toptimization (DSO), to perform a three-dimensional computational bone remodeling simulation on the human proximal femur and analyse the results to determine the validity of Wolff's hypothesis. DSO optimizes the layout of material by iteratively distributing it into the areas of highest loading, while simultaneously changing the design domain to increase computational efficiency. The large-scale simulation utilized a 175 µm mesh resolution with over 23.3 million elements. The resulting anisotropic trabecular architecture was compared to both Wolff's trajectory hypothesis and natural femur samples from literature using radiography. The results qualitatively showed several anisotropic trabecular regions that were comparable to the natural human femur. The realistic simulated trabecular geometry suggests that the DSO method can accurately predict bone adaptation due to mechanical loading and that the proximal femur is an optimum structure as Wolff hypothesized. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

上图像拓扑与多目标优化问题加权解的通有稳定性

用函数的上图象之间的Hausdorff距离定义向量值函数间的距离,在此弱拓扑下研究了多目标优化问题加权解关于权因子和目标函数的稳定性,指出加权解关于权因子和目标函数是通有稳定的．     

Multihazard Design: Structural Optimization Approach

The objective of multihazard structural engineering is to develop methodologies for achieving designs that are safe and cost-effective under multiple hazards. Optimization is a natural tool for achieving such designs. In general, its aim is to determine a vector of design variables subjected to a given set of constraints, such that an objective function of those variables is minimized. In the particular case of structural design, the design variables may be member sizes; the constraints pertain to structural strength and serviceability (e.g., keeping the load-induced stresses and deflections below specified thresholds); and the objective function is the structure cost or weight. In a multihazard context, the design variables are subjected to the constraints imposed by all the hazards to which the structure is exposed. In this paper, we formulate the multihazard structural design problem in nonlinear programming terms and present a simple illustrative example involving four design variables and two hazards: earthquake and strong winds. Results of our numerical experiments show that interior-point methods are significantly more efficient than classical optimization methods in solving the nonlinear programming problem associated with our illustrative example.     

Primal-Dual Newton-Type Interior-Point Method for Topology Optimization

We consider the problem of minimization of energy dissipation in a conductive electromagnetic medium with a fixed geometry and a priori given lower and upper bounds for the conductivity. The nonlinear optimization problem is analyzed by using the primal-dual Newton interior-point method. The elliptic differential equation for the electric potential is considered as an equality constraint. Transforming iterations for the null space decomposition of the condensed primal-dual system are applied to find the search direction. The numerical experiments treat two-dimensional isotropic systems.     

Conic Formulation for l p -Norm Optimization

In this paper, we formulate the l _p -norm optimization problem as a conic optimization problem, derive its duality properties (weak duality, zero duality gap, and primal attainment) using standard conic duality and show how it can be solved in polynomial time applying the framework of interior-point algorithms based on self-concordant barriers.     

Topology Optimization for Continuum Structure with Decreasing Mass

This paper deals with the proper distribution of material in a structure whose available mass decreases in the working time, i.e. part of the structure undergoes degradation and loses mass. This means, the other part of the structure must bear the load. Therefore the structure's mass should be redistributed to replace the degraded material. The present analysis is carried out for the so‐called smart structures which are able to shift their mass within a certain previously fixed area and this research falls within the analysing and decision system. Minimization of the mean compliance under the mass constraints with the artificial approach for updating the material properties is used [1], [2]. As a numerical realization of the problem FEM examples are presented. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

基于离散变量的拓扑优化方法

单元敏度的不准确估计是离散拓扑优化算法数值不稳定的原因之一,特别是添加材料时,传统的敏度计算公式给出的估计误差较大,甚至有时估计符号都是错误的．为了克服这一问题,通过对弹性平衡增量方程的摄动分析构造了新的增量敏度估计公式．这一新的公式无论是添加材料还是删除材料都能较准确地估计出目标函数增量,它可以看作是通过非局部单元刚度阵对传统敏度分析公式的修正．以此为基础构建了一种基于离散变量的拓扑优化算法,它可以从任意单元上添加或删除材料以使目标函数减小,同时为避免优化过程中重新划分网格,采用了单元软杀策略以小刚度材料模拟空单元．这一方法的主要优点是简单,不需要太多的数学计算,特别有利于工程实际的应用．     

Stochastic Material and Topology Design

In order to find a robust optimal topology or material design with respect to stochastic variations of the model parameters of a mechanical structure, the basic optimization problem under stochastic uncertainty must be replaced by an appropriate deterministic substitute problem. Starting from the equilibrium equation and the yield/strength conditions, the problem can be formulated as a stochastic (linear) program “with recourse”. Hence, by discretization the design space by finite elements, linearizing the yield conditions, in case of discrete probability distributions the resulting deterministic substitute problems are linear programs with a dual decomposition data structure.     

Reliability-Based Optimization of Structural Systems involving Discrete Design Variables

This contribution proposes an approach for solving Reliability–based optimization (RBO) problems involving discrete design variables. The proposed approach is based on a decoupling approach and sequential approximations. An application example involving a linear structure under dynamic loading is presented, showing the efficiency of the proposed method. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Stochastic Methods for Practical Global Optimization

Engineering design problems often involve global optimization of functions that are supplied as black box functions. These functions may be nonconvex, nondifferentiable and even discontinuous. In addition, the decision variables may be a combination of discrete and continuous variables. The functions are usually computationally expensive, and may involve finite element methods. An engineering example of this type of problem is to minimize the weight of a structure, while limiting strain to be below a certain threshold. This type of global optimization problem is very difficult to solve, yet design engineers must find some solution to their problem – even if it is a suboptimal one. Sometimes the most difficult part of the problem is finding any feasible solution. Stochastic methods, including sequential random search and simulated annealing, are finding many applications to this type of practical global optimization problem. Improving Hit-and-Run (IHR) is a sequential random search method that has been successfully used in several engineering design applications, such as the optimal design of composite structures. A motivation to IHR is discussed as well as several enhancements. The enhancements include allowing both continuous and discrete variables in the problem formulation. This has many practical advantages, because design variables often involve a mixture of continuous and discrete values. IHR and several variations have been applied to the composites design problem. Some of this practical experience is discussed.     

拓扑优化技术在抑制流体晃荡中的数值模拟研究

流体晃荡问题广泛存在于船舶与海洋工程领域,任何部分载液的储罐运载装备在运动过程中均存在晃荡问题.当外界激励频率接近液舱内流体自由液面的固有频率时,很容易产生剧烈的晃荡,产生极大的冲击力,进而引起结构损害.因此,研究有效的减晃方案,以抑制流体晃荡带来的冲击具有重要意义.该文研究了基于自主研制的数值程序模拟长方体液舱内的流...     

弱拓扑下的非线性随机积分和微分方程组的解*

在本文中,我们首先对具有随机定义域的弱连续随机算子组证明了一个Darbo型随机不动点定理.利用这一定理,我们对Banach空间中关于弱拓扑的非线性随机Volterra积分方程组给出了随机解的存在性准则.作为应用,我们得到了非线性随机微分方程组的Canchy问题弱随机解的存在定理.也得到了这些随机方程组在Banach空间中关于弱拓扑的极值随机解的存在性和随机比较结果.我们的定理改进和推广了Szep,Mitchell－Smith,Cramer－Lakshmikantham,Lakshmikantham-Leela和丁的相应结果.     

Better Optimization of Nonlinear Uncertain Systems (BONUS) for Vehicle Structural Design

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties.     

Topology and Geometry Optimization of Branched Sheet Metal Products

In order to optimize branched sheet metal profiles consisting of several chambers, decisions concerning topology and geometry have to be made. This leads to a problem entailing discrete and nonlinear features. We describe an integrated approach combining both aspects. The underlying idea is to use a branch-and-bound algorithm. In each node of the branch-and-bound tree, a nonlinear optimization problem has to be solved. We describe how the branch-and-bound tree is constructed, i.e., how the topology decision can be classified in a meaningful way. Moreover, we explain how to approach the nonlinear optimization problem arising in the nodes of the tree. We conclude by presenting a numerical example. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)