基于LiToSim平台的疲劳寿命评估LtsFatigue软件开发及应用

针对结构的疲劳问题,考虑随机载荷作用,在自主开发的LiToSim平台基础上,嵌入结构疲劳分析数值计算程序。基于LiToSim平台开发LtsFatigue疲劳软件,运用时域疲劳算法,通过雨流计数法对应力时程曲线处理并计算结构疲劳寿命;引入频域疲劳算法,基于应力响应功率谱根据应力循环分布估算疲劳寿命;通过齿轮算例进行疲劳分析,与商业软件对比,验证了LtsFatigue定制化疲劳软件时域法、频域法的计算精度,同时,频域算法有效提高了计算效率,凸显了LtsFatigue软件的优势。基于LiToSim平台的LtsFatigue定制化疲劳软件开发,对大型复杂结构的疲劳仿真具有重要的应用价值。     

非凸极小极大问题的优化算法与复杂度分析

非凸极小极大问题是近期国际上优化与机器学习、信号处理等交叉领域的一个重要研究前沿和热点,包括对抗学习、强化学习、分布式非凸优化等前沿研究方向的一些关键科学问题都归结为该类问题。国际上凸-凹极小极大问题的研究已取得很好的成果,但非凸极小极大问题不同于凸-凹极小极大问题,是有其自身结构的非凸非光滑优化问题,理论研究和求解难度都更具挑战性,一般都是NP-难的。重点介绍非凸极小极大问题的优化算法和复杂度分析方面的最新进展。     

一种高压开关产品的装配过程优化问题研究

本文针对高压开关产品的装配线提出一个实际的装配过程优化问题:高压开关产品的装配过程优化问题。该问题是在传统的空间布局问题中,加入了装配线工艺流程约束,是一类新的优化问题。本文为该问题建立了整数规划模型,并为该模型开发了启发式算法。然后以ZF11-252产品的装配过程为例,采用启发式算法求解模型。     

非均匀的有限信息扩散函数的探索及应用

小样本的信息处理和分析一直是金融、信息、地震灾害等众多领域中的热点问题．本文运用一维非均匀的有限信息扩散函数（GIDM）方法对小样本问题进行处理，并通过应用于一个实际问题来进行验证，首先根据非均匀信息扩散选取扩散函数，并利用McCormack方法求得其数值解。进而按照优化的准则优化后与大样本进行比较，取得了较理想的结果．     

A FEM model for lifetime prediction of Ancorsteel1000C in rolling contact fatigue

Ancorsteel1000C is commonly used for important components including bearing rollers, gears, connecting rods, wheels and rails; most failures of these components are caused by rolling contact fatigue (RCF). In this study the fatigue damage indicator based on Fatemi-Socie critical plane were determined in a local hot spot area in finite element (FE) model in case of Hertzian stress with zero slip condition. The simulation results compared with respect to experimental data, which is in good agreement. Therefore, this approach can be implemented as a useful tool for lifetime calculations in RCF in the process of structure fatigue design. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

基于时间约束的生产过程优化问题研究

本文提出一个实际的生产过程优化问题:基于时间约束的生产过程优化问题。客户要求企业在规定时间内完成指定批量工件的生产任务,该问题便是从中引出的。该问题的目标是在满足生产时间的条件下最小化总生产成本。本文为该问题建立了整数规划模型。然后以某厂工作缸生产过程为例,采用数学规划软件Cplex 9.0求解模型。     

A computer‐aided methodology for the optimization of electrostatic separation processes in recycling

The rapid growth of technological products has led to an increasing volume of waste electrical and electronic equipments (WEEE), which could represent a valuable source of critical raw materials. However, current mechanical separation processes for recycling are typically poorly operated, making it impossible to modify the process parameters as a function of the materials under treatment, thus resulting in untapped separation potentials. Corona electrostatic separation (CES) is one of the most popular processes for separating fine metal and nonmetal particles derived from WEEE. In order to optimize the process operating conditions (i.e., variables) for a given multi‐material mixture under treatment, several technological and economical criteria should be jointly considered. This translates into a complex optimization problem that can be hardly solved by a purely experimental approach. As a result, practitioners tend to assign process parameters by few experiments based on a small material sample and to keep these parameters fixed during the process life‐cycle. The use of computer experiments for parameter optimization is a mostly unexplored area in this field. In this work, a computer‐aided approach is proposed to the problem of optimizing the operational parameters in CES processes. Three metamodels, developed starting from a multi‐body simulation model of the process physics, are presented and compared by means of a numerical and simulation study. Our approach proves to be an effective framework to optimize the CES process performance. Furthermore, by comparing the predicted response surfaces of the metamodels, additional insight into the process behavior over the operating region is obtained. Copyright © 2015 John Wiley & Sons, Ltd.     

Parameters optimization of selected casting processes using teaching–learning-based optimization algorithm

In the present work, mathematical models of three important casting processes are considered namely squeeze casting, continuous casting and die casting for the parameters optimization of respective processes. A recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of these casting processes. Each process is described with a suitable example which involves respective process parameters. The mathematical model related to the squeeze casting is a multi-objective problem whereas the model related to the continuous casting is multi-objective multi-constrained problem and the problem related to the die casting is a single objective problem. The mathematical models which are considered in the present work were previously attempted by genetic algorithm and simulated annealing algorithms. However, attempt is made in the present work to minimize the computational efforts using the TLBO algorithm. Considerable improvements in results are obtained in all the cases and it is believed that a global optimum solution is achieved in the case of die casting process.     

Search for feasible solutions by interior point algorithms

A family of interior point algorithms for linear programming problems is considered. In these algorithms, entering the feasible solution region of the original problem is presented as an optimization process of an extended problem. This extension is performed by adding a new variable. The main objective of the paper is a theoretical justification of the procedure of entering the feasible region of the original problem under the assumption of non-degeneracy of the extended problem. Specifically, it is proved that under consistent constraints of the original problemthe procedure leads to a relative interior point of the feasible region.     

Second-order necessary conditions for domain optimization problems in elastic structures,part 2: Surface traction dependent on the shape

The second-order sensitivity analysis for a domain optimization problem is studied for a linear elastic structure. In the primary elastic structure considered, the surface traction, a part of the boundary conditions, depends not only on the position but also on the shape of the structure. The first variation and the second variation of the objective functional are calculated in terms of the solution, the first variation of the solution for the primal elastic system, and of the adjoint variable introduced. Moreover, the first-order and the second-order necessary optimality conditions are derived for the structure under a hydrostatic pressure. As an illustrative problem, a mean compliance design is treated.     

Stochastic optimization under constraints

We study a stochastic optimization problem under constraints in a general framework including financial models with constrained portfolios, labor income and large investor models and reinsurance models. We also impose American-type constraint on the state space process. General objective functions including deterministic or random utility functions and shortfall risk loss functions are considered. We first prove existence and uniqueness result to this optimization problem. In a second part, we develop a dual formulation under minimal assumptions on the objective functions, which are the analogue of the asymptotic elasticity condition of Kramkov and Schachermayer (1999).     

Fatigue life of polymethyl methacrylate in stationary and stepped nonisothermal cyclic loading regimes

Rotating cantilever experiments have revealed the heating characteristics of PMMA in multicycle fatigue tests. The effect of heating and damage on the fatigue life under stationary and single-step loading is considered. The statistical characteristics of the fatigue life are presented.     

Diagnostics in Birnbaum–Saunders accelerated life models with an application to fatigue data

In industrial statistics, there is great interest in predicting with precision lifetimes of specimens that operate under stress. For example, a bad estimation of the lower percentiles of a life distribution can produce significant monetary losses to organizations due to an excessive amount of warranty claims. The Birnbaum–Saunders distribution is useful for modeling lifetime data. This is because such a distribution allows us to relate the total time until the failure occurs to some type of cumulative damage produced by stress. In this paper, we propose a methodology for detecting influence of atypical data in accelerated life models on the basis of the Birnbaum–Saunders distribution. The methodology developed in this study should be considered in the design of structures and in the prediction of warranty claims. We conclude this work with an application of the proposed methodology on the basis of real fatigue life data, which illustrates its importance in a warranty claim problem. Copyright © 2012 John Wiley & Sons, Ltd.     

An Optimization Problem with a Separable Non-Convex Objective Function and a Linear Constraint

For a class of global optimization (maximization) problems, with a separable non-concave objective function and a linear constraint a computationally efficient heuristic has been developed.The concave relaxation of a global optimization problem is introduced. An algorithm for solving this problem to optimality is presented. The optimal solution of the relaxation problem is shown to provide an upper bound for the optimal value of the objective function of the original global optimization problem. An easily checked sufficient optimality condition is formulated under which the optimal solution of concave relaxation problem is optimal for the corresponding non-concave problem. An heuristic algorithm for solving the considered global optimization problem is developed.The considered global optimization problem models a wide class of optimal distribution of a unidimensional resource over subsystems to provide maximum total output in a multicomponent systems.In the presented computational experiments the developed heuristic algorithm generated solutions, which either met optimality conditions or had objective function values with a negligible deviation from optimality (less than 1/10 of a percent over entire range of problems tested).     

Application of the sequential parametric convex approximation method to the design of robust trusses

We study an algorithm recently proposed, which is called sequential parametric approximation method, that finds the solution of a differentiable nonconvex optimization problem by solving a sequence of differentiable convex approximations from the original one. We show as well the global convergence of this method under weaker assumptions than those made in the literature. The optimization method is applied to the design of robust truss structures. The optimal structure of the model considered minimizes the total amount of material under mechanical equilibrium, displacements and stress constraints. Finally, Robust designs are found by considering load perturbations.     

Fatigue crack growth simulations of homogeneous and bi-material interfacial cracks using element free Galerkin method

In this paper, quasi-static fatigue crack growth simulations of homogeneous and bi-material interfacial cracks have been performed using element free Galerkin method (EFGM) under mechanical as well as thermo-elastic load. The thermo-elastic fracture problem is decoupled into thermal and elastic problems. The temperature distribution obtained by solving heat conduction equation is used as input in the elastic problem to get the displacement and stress fields. Discontinuities in the temperature and displacement fields are captured by extrinsic partition of unity enrichment technique. The values of stress intensity factors have been extracted from the EFGM solution by domain based interaction integral approach. The standard Paris fatigue crack growth law has been implemented for the life estimation of various model problems. The results obtained by EFGM under mechanical and thermo-elastic loads were compared with those obtained by FEM using remeshing approach.     

Topology optimization considering the fatigue constraint of variable amplitude load based on the equivalent static load approach

In this research, a new layout optimization method is developed to consider high cycle fatigue constraints which occur due to variable amplitude mechanical loading. Although fatigue is a very important property in terms of safety when designing mechanical components, it has rarely been considered in topology optimization with the lack of concept and the difficulty of sensitivity analysis for fatigue constraints calculated from multiaxial cycle counting. For the topology optimization for fatigue constraint, we use transient stress analysis to extract effective stress cycles and Miner's cumulative damage rule to calculate total damage at every spatial element. Because the calculation of the exact sensitivities of a transient system is complex and time consuming for the topology optimization application, this research proposes to use the pseudo-sensitivities of fatigue constraints calculated by applying equivalent static load approach. In addition, as an aggregated fatigue constraint is very sensitive to the changes in stress value which causes some unstable convergences in optimization process, a new scaling approach of the aggregated fatigue damage constraint is developed. To validate the usefulness of the developed approaches, we solved some benchmark topology optimization problems and found that the present method provides physically appropriate layouts with stable optimization convergence.     

Steepest descent preconditioning for nonlinear GMRES optimization

Steepest descent preconditioning is considered for the recently proposed nonlinear generalized minimal residual (N‐GMRES) optimization algorithm for unconstrained nonlinear optimization. Two steepest descent preconditioning variants are proposed. The first employs a line search, whereas the second employs a predefined small step. A simple global convergence proof is provided for the N‐GMRES optimization algorithm with the first steepest descent preconditioner (with line search), under mild standard conditions on the objective function and the line search processes. Steepest descent preconditioning for N‐GMRES optimization is also motivated by relating it to standard non‐preconditioned GMRES for linear systems in the case of a standard quadratic optimization problem with symmetric positive definite operator. Numerical tests on a variety of model problems show that the N‐GMRES optimization algorithm is able to very significantly accelerate convergence of stand‐alone steepest descent optimization. Moreover, performance of steepest‐descent preconditioned N‐GMRES is shown to be competitive with standard nonlinear conjugate gradient and limited‐memory Broyden–Fletcher–Goldfarb–Shanno methods for the model problems considered. These results serve to theoretically and numerically establish steepest‐descent preconditioned N‐GMRES as a general optimization method for unconstrained nonlinear optimization, with performance that appears promising compared with established techniques. In addition, it is argued that the real potential of the N‐GMRES optimization framework lies in the fact that it can make use of problem‐dependent nonlinear preconditioners that are more powerful than steepest descent (or, equivalently, N‐GMRES can be used as a simple wrapper around any other iterative optimization process to seek acceleration of that process), and this potential is illustrated with a further application example. Copyright © 2012 John Wiley & Sons, Ltd.     

On computational search for Nash equilibrium in hexamatrix games

The problem of numerical finding of a Nash equilibrium in a 3-player polymatrix game is considered. Such a game can be completely described by six matrices, and it turns out to be equivalent to the solving a nonconvex optimization problem with a bilinear structure in the objective function. Special methods of local and global search for the optimization problem are proposed and investigated. The results of computational solution of the test game are presented and analyzed.     

Exactness Property of the Exact Absolute Value Penalty Function Method for Solving Convex Nondifferentiable Interval-Valued Optimization Problems

In the paper, the classical exact absolute value function method is used for solving a nondifferentiable constrained interval-valued optimization problem with both inequality and equality constraints. The property of exactness of the penalization for the exact absolute value penalty function method is analyzed under assumption that the functions constituting the considered nondifferentiable constrained optimization problem with the interval-valued objective function are convex. The conditions guaranteeing the equivalence of the sets of LU-optimal solutions for the original constrained interval-valued extremum problem and for its associated penalized optimization problem with the interval-valued exact absolute value penalty function are given.