任意多连通截面扭转应力函数的有限元解法

杆件扭转问题的求解,主要有基于扭转理论翘曲函数的边界元法和有限元法、基于薄壁杆件理论的数值解法和基于扭转理论应力函数的有限元法．根据任意多连通截面直杆扭转问题的应力函数理论,讨论并改进了与微分方程及定解条件等效的泛函,在此基础上推导了求解多连通截面扭转应力函数的有限元列式,将扭转问题的翘曲位移单值条件转化为边界节点上的集中荷载．采用主从节点法满足孔洞边界上应力函数的同值条件,实现了任意多连通复杂截面扭转应力函数的有限元直接求解,通过应力函数积分获得截面的扭转常数．算例验证了方法的可行性和有效性．     

具有动应力和动位移约束的离散变量结构拓扑优化设计方法

讨论了动应力、动位移约束下离散变量结构拓扑优化设计问题.首先给出问题的数学模型,然后用拟静力算法,将结构惯性力极值作为静载荷施加到结构上,求得结构的动位移和动内力,将考虑动应力约束和动位移约束的离散变量结构拓扑设计问题化为静应力和静位移约束的优化问题,然后利用两类变量统一考虑的离散变量结构拓扑优化设计的综合算法进行求解.     

具有动应力、位移和稳定性约束的离散变量桁架结构布局优化设计算法

给出了在动应力、动位移和动稳定约束下离散变量结构布局优化设计问题的数学模型,用“拟静力”算法,将具有动应力约束、动位移约束和动稳定约束的离散变量结构布局优化设计问题化为静应力、静位移和静稳定约束的优化问题,然后利用两级优化算法求解该模型．优化过程由两级组成,拓扑级优化和形状级优化．在每一级,都使用了综合算法,并且在搜索过程中都根据两类设计变量的相对差商值进行搜索．对包含稳定约束和不包含稳定约束的优化结果做了比较,结果显示稳定性约束对优化结果产生较大的影响．     

基于可靠性的工程结构动力响应优化设计

在考虑结构物理参数和作用荷栽同时具有随机性的情况下,建立了具有动应力、动位移可靠性约束和设计变量上下限约束的工程结构优化设计数学模型;分别对结构动力响应的数字特征和基于可靠性的结构动力响应的灵敏度进行了推导.利用内罚函数法求解.算例表明文中构建的优化模型和提出的求解方法是合理与可行的.     

基于对偶二次规划的大型框架结构优化方法

将准则法和数学规划相结合,对于不同的约束采用不同的处理方法:应力约束作为局部性约束,用0阶近似进行处理,借助满应力准则将其转化为动态尺寸下限;位移约束作为全局性约束,根据单位虚载荷法将其显式化,从而建立了满足应力和位移约束的框架结构截面优化的显式模型．为了提高模型的求解效率,根据对偶理论将大规模的框架结构优化问题转化为仅仅几个对偶变量的对偶问题,采用二次规划方法求解,算例证明该方法能极大的提高模型的求解效率．采用近似射线步既能减小计算量又能使迭代过程更加平稳,采用删除无效约束技术能减小优化模型的规模. 以MSC/Nastran软件为结构分析的求解器,以MSC/Patran软件为开发平台,完成了满足刚度和强度的多工况、多变量的框架截面优化软件．算例结果表明上述程序算法的高效性．     

基于自适应萤火虫算法的液压缸优化设计

针对液压缸优化设计问题,以液压机法兰支承液压缸为应用背景,在ANSYS软件中对液压缸进行有限元分析.建立以液压缸体积最小为优化目标的优化数学模型,提出一种自适应萤火虫算法求解模型对液压缸的结构参数进行优化.仿真结果表明,所提出自适应萤火虫算法比基本萤火虫算法收敛的精度更高,迭代次数更少,优化后液压缸的体积相对减少了42.3%,较好地解决了液压缸优化设计问题.进一步对优化后的油缸进行了有限元分析,验证了优化结果的正确性.     

一般组合弹性结构的有限元分析

提出一个求解一般组合弹性结构问题的有限元方法:对体件的位移、 板件的纵向位移和杆件的纵向位移用线性协调元离散, 对杆件的纵向转角用二次协调元离散, 对板件的横向位移和杆件的横向位移分别用Morley元和三次Hermite元离散. 在相应的非协调元空间上建立了广义Korn不等式, 进而证得有限元方法的唯一可解性. 最后导出方法在能量模意义下的最优误差估计.     

基于改进粒子群算法的翼型稳健型气动优化设计

基于标准粒子群算法,将位移变化作为影响微粒速度的变量,使得粒子群算法关于粒子位置为二阶精度函数,加快了收敛速度;进一步地在粒子速度更新公式中引入振荡环节,提高了群体多样性,改善了算法的全局收敛性．以改进粒子群算法为基础,结合气动分析程序、代理模型以及翼型参数化方法,构建了翼型稳健型气动优化设计系统．针对某型客机的基本翼型以及翼梢小翼翼型气动优化设计结果表明,优化后的翼型气动特性相对于初始翼型在较宽的设计范围内都有了大幅度提高．     

基于有限元概念的解析解

基于有限元概念,本文研究了结构响应对于设计变量的严格解析解,以空间桁架为例,得到了位移显式的解析表达式——一个关于截面设计变量的有理函数,并进行了严格的数学论证;进而推出了有益于结构敏度分析、结构优化的若干结论,针对含有位移显式的优化模型,提出了采用广义几何规划解法的建议;最后计算了若干简例.     

基于RBF神经网络模型的结构可靠度优化方法

在结构构件尺寸、材料属性以及外部载荷等不确定性因素影响下,基于可靠度的优化给出了兼顾结构的成本和安全性能的安全设计方案.由于传统的可靠度优化方法采用嵌套的双层优化列式求解,因此导致计算量过大.为了克服这个问题,学者们相继提出了解耦方法和单循环方法等方法.该文采用RBF神经网络模型用于可靠度优化问题的求解中,通过拉丁超立方方法构造代理模型,并用误差指标来验证代理模型的精确程度,同时自适应更新代理模型直至满足需求.通过与现有可靠度优化4种主流算法的比较,说明了该文提出算法的高效性和稳健性.     

Robustness of mechanical systems against uncertainties

In this paper, one can propose a method which takes into account the propagation of uncertainties in the finite element models in a multi-objective optimization procedure. This method is based on the coupling of stochastic response surface method (SRSM) and a genetic algorithm provided with a new robustness criterion. The SRSM is based on the use of stochastic finite element method (SFEM) via the use of the polynomial chaos expansion (PC). Thus, one can avoid the use of Monte Carlo simulation (MCS) whose costs become prohibitive in the optimization problems, especially when the finite element models are large and have a considerable number of design parameters.The objective of this study is on one hand to quantify efficiently the effects of these uncertainties on the responses variability or the cost functions which one wishes to optimize and on the other hand, to calculate solutions which are both optimal and robust with respect to the uncertainties of design parameters.In order to study the propagation of input uncertainties on the mechanical structure responses and the robust multi-objective optimization with respect to these uncertainty, two numerical examples were simulated. The results which relate to the quantification of the uncertainty effects on the responses variability were compared with those obtained by the reference method (REF) using MCS and with those of the deterministic response surfaces methodology (RSM).In the same way, the robust multi-objective optimization results resulting from the SRSM method were compared with those obtained by the direct optimization considered as reference (REF) and with RSM methodology.The SRSM method application to the response variability study and the robust multi-objective optimization gave convincing results.     

Dynamic inversion method for the material parameters of a high arch dam and its foundation

The overall mechanical behavior of the structure of an arch dam is comprehensively reflected by the vibration modal information included in measured vibration response. Hence, the results obtained from inverting material parameters based on measured vibration data are often superior to those based on static monitoring data. In this study, a dynamic inversion method for the material parameters of a high arch dam and its foundation is proposed on the basis of the measured vibration response. First, an arch dam prototype test is conducted to obtain the measured dynamic displacement response as input. Then, a stochastic subspace identification method based on singular entropy is formulated to determine the modal parameters. Second, a dynamic elastic modulus (DEM) with a great influence on the modal parameters is selected as the material parameter to be inverted. Then, a response surface model (RSM), which reflects the nonlinear relationship between the material and modal parameters of each zone, is constructed. Latin hypercube sampling is used to generate the sample library of the DEM. The RSM is fitted by modal parameters calculated on the basis of the arch dam finite element model (FEM) and is applied to replace the FEM. Finally, the optimization mathematical model of the inversion of the DEM is established. Then, the objective function is optimized through a genetic algorithm, and the optimal combination of the DEM in each zone is inverted. The modal parameters of the arch dam calculated by inversion results are consistent with those measured by variation law and values. Therefore, the inversion results are reasonable and reliable. This method provides a new idea for determining the material parameters of a high arch dam and its foundation during the operation period.     

Application of bacterial colony chemotaxis optimization algorithm and RBF neural network in thermal NDT/E for the identification of defect parameters

The identification of defect parameters in thermal non-destructive test and evaluation (NDT/E) was considered as a kind of inverse heat transfer problem (IHTP). However, it can be further considered as a shape optimization problem, and then a structure design optimization problem, and the design results should meet the surface temperature profile of the apparatus with defects. A bacterial colony chemotaxis (BCC) optimization algorithm and a radial basis function (RBF) neural network (NN) are applied to the thermal NDT/E for the identification of defects parameters. The RBFNN is a precise and convenient surrogate model for the time costly finite element computation, which obtains the surface temperature with different defect parameters. The BCC optimization algorithm is derivatively-free, and the convergence speed is fast. Then a simple but complete multi-disciplinary design optimization (MDO) framework is constructed for the sake of generality and flexibility. This method is applied to a simple verification case and the result is acceptable. The algorithm is also compared with the particle swarm optimization (PSO) algorithm, and the BCC algorithm can access the optimum with faster speed.     

区间运算和静力区间有限元

用均值和离差两参数表征区间变量的不确定性,根据区间运算规则,论证了区间变量的运算特性.将区间分析和有限元方法相结合,提出了非概率不确定结构的一种区间有限元分析方法.将区间有限元静力控制方程中n自由度不确定位移场特征参数的求解归结为求解一2n阶线性方程组.实例分析表明文中方法是有效和可行的.     

Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs

This paper proposes an online surrogate model-assisted multiobjective optimization framework to identify optimal remediation strategies for groundwater contaminated with dense non-aqueous phase liquids. The optimization involves three objectives: minimizing the remediation cost and duration and maximizing the contamination removal rate. The proposed framework adopts a multiobjective feasibility-enhanced particle swarm optimization algorithm to solve the optimization model and uses an online surrogate model as a substitute for the time-consuming multiphase flow model for calculating contamination removal rates during the optimization process. The resulting approach allows decision makers to find a balance among the remediation cost, remediation duration and contamination removal rate for remediating contaminated groundwater. The new algorithm is compared with the nondominated sorting genetic algorithm II, which is an extensively applied and well-known algorithm. The results show that the Pareto solutions obtained by the new algorithm have greater diversity and stability than those obtained by the nondominated sorting genetic algorithm II, indicating that the new algorithm is more applicable than the nondominated sorting genetic algorithm II for optimizing remediation strategies for contaminated groundwater. Additionally, the surrogate model and Pareto optimal set obtained by the proposed framework are compared with those of the offline surrogate model-assisted multiobjective optimization framework. The results indicate that the surrogate model accuracy and Pareto front achieved by the proposed framework outperform those of the offline surrogate model-assisted optimization framework. Thus, we conclude that the proposed framework can effectively enhance the surrogate model accuracy and further extend the comprehensive performance of Pareto solutions.     

Optimization of arches using genetic algorithm

An optimization procedure is presented for the minimum weight and strain energy optimization for arch structures subjected to constraints on stress, displacement and weight responses. Both thickness and shape variables defining the natural line of the arch are considered. The computer program which is developed in this study can be used to optimize thick, thin and variable thickness curved beams/arches. An automated optimization procedure is adopted which integrates finite element analysis, parametric cubic spline geometry definition, automatic mesh generation and genetic algorithm methods. Several examples are presented to illustrate optimal arch structures with smooth shapes and thickness variations. The changes in the relative contributions of the bending, membrane and shear strain energies are monitored during the whole process of optimization.     

Probabilistic nonlinear analysis of CFR dams by MCS using Response Surface Method

This paper presents the probabilistic analysis of concrete-faced rockfill (CFR) dams according to the Monte Carlo Simulation (MCS) results which are obtained through the Response Surface Method (RSM). ANSYS finite element program is used to get displacement and principal stress components. First of all, some parametric studies are performed according to the simple and representative finite element model of dam body to obtain the optimum approximate model. Secondly, a sensitivity analysis is performed to get the most effective parameters on dam response. Then, RSM is used to obtain the approximate function through the selected parameters. After the performed analyses, star experimental design with quadratic function without mixed terms according to the k = 1 is determined as the most appropriate model. Finally, dam-foundation-reservoir interaction finite element model is constituted and probabilistic analyses are performed with MCS using the selected parameters, sampling method, function and arbitrary factor under gravity load for empty and full reservoir conditions. Geometrically and materially nonlinearity are considered in the analysis of dam-foundation-reservoir interaction system. Reservoir water is modeled by fluid finite elements based on the Lagrangian approach. Structural connections are modeled as welded contact and friction contact based on Coulomb’s friction law. Probabilistic displacements and stresses are presented and compared with deterministic results.     

Optimization of probabilistic multiple response surfaces

Response surface methodology (RSM) is a statistical-mathematical method used for analyzing and optimizing the experiments. In analysis process, experts usually face several input variables having effect on several outputs called response variables. Simultaneous optimization of the correlated response variables has become more important in complex systems. In this paper multi-response surfaces and their related stochastic nature have been modeled and optimized by Goal Programming (GP) in which the weights of response variables have been obtained through a Group Decision Making (GDM) process. Because of existing uncertainty in the stochastic model, some stochastic optimization methods have been applied to find robust optimum results. At the end, the proposed method is described numerically and analytically.     

Adaptive surrogate-based design optimization with expected improvement used as infill criterion

A prominent advantage of using surrogate models in structural design optimization is that computational effort can be greatly reduced without significantly compromising model accuracy. The essential goal is to perform the design optimization with fewer evaluations of the typically finite element analysis and ensuring accuracy of the optimization results. An adaptive surrogate based design optimization framework is proposed, in which Latin hypercube sampling and Kriging are used to build surrogate models. Accuracy of the models is improved adaptively using an infill criterion called expected improvement (EI). It is the anticipated improvement that an interpolation point will lead to the current surrogate models. The point that will lead to the maximum EI is searched and used as infill points at each iteration. For constrained optimization problems, the surrogate of constraint is also utilized to form a constrained EI as the corresponding infill criterion. Computational trials on mathematical test functions and on a three-dimensional aircraft wing model are carried out to test the feasibility of this method. Compared with the traditional surrogate base design optimization and direct optimization methods, this method can find the optimum design with fewer evaluations of the original system model and maintain good accuracy.     

Micromechanical analysis of the stress transfer in single-fiber composite: The influence of the uniform and graded interphase with finite-thickness

A theoretical model is developed to analyze the stress transfer between fiber and matrix through the interphase with finite thickness. The Young's modulus of interphase is assumed to be homogeneous uniform or power-graded along radial direction while other material parameters are constants. The bonds between fiber and interphase as well as between interphase and matrix are perfect. The geometrical equations are strictly satisfied except that the radial displacement gradient with respect to the axial direction is neglected, as its magnitude is much smaller than that of the axial displacement gradient with respect to the radial direction. The equilibrium equations along radial direction are strictly satisfied, while the equilibrium equations along axial direction are satisfied in the integral forms. In addition, both the interfacial displacement and stress continuity conditions as well as stress boundary conditions are enforced exactly. Two coupled 2nd-order ordinary differential equations can be obtained in terms of average axial stresses in fiber and matrix. Finite element analysis (FEA) with refined mesh for single-fiber composite containing uniform interphase with finite thickness is developed to validate the present model. Series of parameter studies are performed to investigate the influence of interphase properties and thickness as well as the fiber volume content and model length on the stress distribution in composites.