响应面法的温差发电器优化设计

为提高温差发电器的发电效率,同时保证整机轻量化,提出了以发电器结构参数为设计变量,以质量、排气背压和发电片两侧温度差为目标函数的整机多目标优化设计方案。首先利用中心复合设计法、有限元法分别进行设计点的选取与计算。其次利用响应面法构建设计变量与质量、温差、排气背压三个响应量的响应面模型,并进行了灵敏度分析。通过多目标遗传优化算法进行基于响应面模型的多目标优化,优化后温差发电器的发电效率提高了7.3%。最后对优化结果进行了可靠性验证。     

关节轴承静态疲劳特性的响应面优化

针对某一炼钢厂250t转炉水平悬挂装置的失效问题,分析得知由向心关节轴承疲劳破损引起.以关节轴承为研究对象,通过静力学仿真以及疲劳寿命计算获得关节轴承在转炉倾转至90°的严峻工况下的静态疲劳特性.以关节轴承弹性模量、内圈边缘倒角r1a和r1b及工作温度为约束,最大等效应力、最小疲劳寿命为目标函数,结合响应面法并选用NS...     

Concept optimization for mechanical product using Genetic Algorithm

Conceptual design is the first step in the overall process of product design. Its intrinsic uncertainty, imprecision, and lack of information lead to the fact that current conceptual design activities in engineering have not been computerized and very few CAD systems are available to support conceptual design. In most of the current intelligent design systems, approach of principle synthesis, such as morphology matrix, bond graphic, or design catalogues, is usually adopted to deal with the concept generation, in which optional concepts are generally combined and enumerated through function analysis. However, as a large number of concepts are generated, it is difficult to evaluate and optimize these design candidates using regular algorithm. It is necessary to develop a new approach or a tool to solve the concept generation. Generally speaking, concept generation is a problem of concept synthesis. In substance, this process of developing design candidate is a combinatorial optimization process, viz., the process of concept generation can be regarded as a solution for a state-place composed of multi-concepts. In this paper, genetic algorithm is utilized as a feasible tool to solve the problem of combinatorial optimizalion in concept generation, in which the encoding method of morphology matrix based on function analysis is applied, and a sequence of optimal concepts are generated through the search and iterative process which is controlled by genetic operators, including selection, crossover, mutation, and reproduction in GA. Several crucial problems on GA are discussed in this paper, such as the calculation of fitness value and the criteria for heredity termination, which have a heavy effect on selection of better concepts. The feasibility and intellectualization of the proposed approach are demonstrated with an engineering case. In this work concept generation is implemented using GA, which can facilitate not only generating several better concepts, but also selecting the best concept. Thus optimal concepts can be conveniently developed and design efficiency can be greatly improved.     

Optimal design of nonlinear squeeze film damper using hybrid global optimization technique

The optimal design of the squeeze film damper (SFD) for rotor system has been studied in previous researches. However, these researches have not been considering jumping or nonlinear phenomena of a rotor system with SFD. This paper represents an optimization technique for linear and nonlinear response of a simple rotor system with SFDs by using a hybrid GA-SA algorithm which combined enhanced genetic algorithm (GA) with simulated annealing algorithm (SA). The damper design parameters are the radius, length and radial clearance of the damper. The objective function is to minimize the transmitted load between SFD and foundation at the operating and critical speeds of the rotor system with SFD which has linear and nonlinear unbalance responses. The numerical results show that the transmitted load of the SFD is greatly reduced in linear and nonlinear responses for the rotor system.     

Multidisciplinary design optimization of a structurally nonlinear aircraft wing via parametric modeling

In this study, the optimization of an aircraft wing design was conducted using multidisciplinary design optimization (MDO), which integrates aerodynamic and structural analysis in considering nonlinear structural behavior. Automation is an absolute necessity to make the MDO framework practical for actual engineering optimization problems. The objective of this research was to develop a fully automated MDO framework in which the entire process is automated through a parametric-modeling approach. The computational fluid dynamics (CFD) grid was generated automatically from parametric modeling using CATIA and Gridgen, followed by automatic flow analysis using FLUENT. The computational structure mechanics (CSM) grid was generated automatically by the parametric methods of CATIA and MSC/Patran. The structure was analyzed by ABAQUS considering the deformation nonlinearity, and the aerodynamic load was transferred from the CFD grid to the CSM grid using the volume spline method. The response surface method was applied for optimization, which helped achieve the global optimum. The developed MDO framework was applied to a wing optimization problem in which the objective was wing weight and the constraints were the lift-drag ratio, wing deflection, and structural stress level. The aspect ratio, taper ratio, quarter-chord sweep angle, skin thickness, and spar flange area were the design variables. The optimization design result demonstrated a successful application of the fully automatic MDO framework.     

Optimization of depth of cut in multi-pass turning using nontraditional optimization techniques

Examining the economics of multi-pass machining operations has significant practical importance. Traditional optimization techniques have been used, but are limited in application. Non-traditional optimization techniques like genetic algorithms, simulated Annealing and ant colony optimization are increasingly used to solve optimization problems. This paper discusses the use of non-traditional optimization techniques for optimizing the depth of cut in multi-pass turning. The resulting subdivision of the cut depth indicates the proposed methodologies are competent, efficient and accurate.     

Sliding mode controller with sliding perturbation observer based on gain optimization using genetic algorithm

The Stewart platform manipulator is a closed-kinematics chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. However, this is a complex and nonlinear system, so the control performance of the system is not so good. In this paper, a new robust motion control algorithm is proposed. The algorithm uses partial state feedback for a class of nonlinear systems with modeling uncertainties and external disturbances. The major contribution is the design of a robust observer for the state and the perturbation of the Stewart platform, which is combined with a variable structure controller (VSC). The combination of controller and observer provides the robust routine called sliding mode control with sliding perturbation observer (SMCSPO). The optimal gains of SMCSPO, which is determined by nominal eigenvalues, are easily obtained by genetic algorithm. The proposed fitness function that evaluates the gain optimization is to put sliding function. The control performance of the proposed algorithm is evaluated by the simulation and experiment to apply to the Stewart platform. The results showed high accuracy and good performance.     

基于遗传算法的行星传动多目标模糊优化

遗传算法是一种模拟生命进化机制的搜索和优化方法 ,其全局优化和隐含并行性使得遗传算法适合求解大规模的复杂优化问题 ,并在介绍遗传算法的基础上 ,提出了基于遗传算法的行星传动多目标模糊优化方法。算例计算表明 ,遗传算法在机械多目标优化方面具有较好的应用前景     

基于响应面法的载货汽车平顺性优化

针对传统的载货汽车动力学模型在整车平顺性仿真分析时,存在的仿真精度低、仿真效率慢等问题,应用Hyper-Mesh 软件建立柔性车架,导入到ADAMS软件中建立刚柔耦合整车动力学模型;并通过实车道路试验,验证所建立的刚柔耦合整车模型的正确性.选取驾驶室前、后悬置参数和前、后桥处的减振器特性曲线参数为设计变量,通过灵敏度分...     

基于响应面法的饲喂机器人螺旋输送轴结构优化

以羊只饲喂机器人为研究对象,基于响应面法对输送机构的主要构件螺旋输送轴的结构进行优化.对螺旋输送轴进行受力分析,应用SolidWorks软件建立螺旋输送轴三维模型,采用响应面法建立响应面模型,通过多目标遗传算法得出在螺旋输送轴轴径为50 mm,螺距为140 mm,叶片厚度为2.7 mm的条件下,螺旋输送轴的质量减轻、体...     

基于遗传算法的汽车驱动轴多目标轻量化优化设计

驱动轴是汽车传动系统的重要零部件之一,它对汽车驱动性能和运行稳定性有重大影响。依据汽车轻量化技术的发展趋势,提出了一种基于数值优化和有限元模拟技术相结合的多目标轻量化设计方法。以汽车驱动轴的内径值为设计参数,在满足驱动轴刚度和强度约束条件下,利用拉丁方试验设计和最小二乘方法建立了驱动轴多目标轻量化设计的响应面预测模型,并采用多目标遗传算法计算得到了驱动轴多目标轻量化设计函数的Pareto解集,结合实际产品设计的需要,研发了一种新型全空心驱动轴结构,运用有限元法对新型驱动轴的模态振型和等效应力等力学特性进行了分析。仿真结果表明:新型驱动轴等效应力最大值发生在花键末端圆角过渡部位,低于驱动轴材料的抗扭强度,满足强度设计要求;驱动轴的前10阶非零振型主要为弯曲变形,新型驱动轴固有频率与实心驱动轴固有频率相近,在使用过程中可有效避免共振。为驱动轴以及其它机械产品的多目标轻量化设计提供了一种新的理论方法,在汽车行业轻量化设计方面具有重要参考价值。     

Dynamic analysis of harmonically excited non-linear system using multiple scales method

An analytical method is presented for evaluation of the steady state periodic behavior of nonlinear systems. This method is based on the substructure synthesis formulation and a MS (multiple scales) procedure, which is applied to the analysis of nonlinear responses. The proposed procedure reduces the size of large degrees-of-freedom problem in solving nonlinear equations. Feasibility and advantages of the proposed method are illustrated with the nonlinear rotating machine system as an example of large mechanical structure systems. In addition, its efficiency for nonlinear response prediction will be shown by comparison of other conventional methods.     

汽车钢制车轮的响应面法优化设计

针对汽车钢制车轮研发周期长和生产成本高的问题,以15×5J钢制车轮为例,基于ANSYS Workbench 19.2软件完成车轮重要尺寸的参数化建模并创建车轮弯曲疲劳试验有限元模型,采用优化空间填充设计法,生成车轮重量及Mises应力的试验数据,获得车轮重量及应力响应面,最终在短期内成功确定车轮最优轻量化数模并应用于生...     

基于遗传算法的工艺路线决策与优化

在已有的工艺路线决策优化研究的基础上,分析了工艺知识的特点,确定了基于特征的工艺知识表达方法,根据工艺知识间的约束关系,构建基于工艺约束的工艺路线决策空间,提高遗传算法的搜索能力。针对工艺路线决策的不确定性,建立了多目标优化函数,将遗传算法应用于工艺路线决策过程中。通过设计合理的基因编码规则、适应度函数、交叉、变异算法优化工艺路线。通过实例,介绍了利用遗传算法进行工艺路线决策和优化的过程。     

A method of genetic algorithm based multiobjective optimization via cooperative coevolution

The paper deals with the identification of Pareto optimal solutions using GA based coevolution in the context of multiobjective optimization. Coevolution is a genetic process by which several species work with different types of individuals in parallel. The concept of cooperative coevolution is adopted to compensate for each of single objective optimal solutions during genetic evolution. The present study explores the GA based coevolution, and develops prescribed and adaptive scheduling schemes to reflect design characteristics among single objective optimization. In the paper, non-dominated Pareto optimal solutions are obtained by controlling scheduling schemes and comparing each of single objective optimal solutions. The proposed strategies are subsequently applied to a three-bar planar truss design and an energy preserving flywheel design to support proposed strategies.     

精密复合数控磨床伺服进给系统优化设计

基于提高精密复合数控磨床的超精密加工精度的目的,对其台架伺服系统参数进行优化,建立了基于直线电动机数控伺服系统的控制系统数学模型,采用改进型粒子群算法对伺服系统进行系统优化,首次实现了被控对象传递函数中可变量及PID控制参数的同时优化,最终的实验结果证明了该优化方法的有效性.     

基于遗传算法的汽车底盘系统协调控制优化

为了消除汽车底盘集成系统机械与控制系统间的耦合,首先建立了汽车悬架与转向系统整车动力学模型,分别设计了主动悬架(ASS)LQG控制器和主动前轮转向系统(AFS)滑模变结构控制器和两系统的规则协调控制器。以集成系统机械与控制参数为优化变量,以反映汽车动力学综合性能为目标函数,基于遗传算法编制了集成优化程序,对集成控制系统进行了优化仿真计算。仿真结果表明:汽车底盘集成控制系统经过参数优化后,汽车综合性能得到改善:汽车的横摆角速度、车身侧向加速度均方根值分别降低了近34%,38.12%,车身俯仰角速度降低了4.91%、改善了车辆的乘坐舒适性,方向盘操纵转矩大大降低,提高了汽车转向时的转向轻便性。     

The stacking sequence optimization of stiffened laminated curved panels with different loading and stiffener spacing

An efficient procedure to obtain the optimal stacking sequence and the minimum weight of stiffened laminated composite curved panels under several loading conditions and stiffener layouts has been developed based on the finite element method and the genetic algorithm that is powerful for the problem with integer variables. Often, designing composite laminates ends up with a stacking sequence optimization that may be formulated as an integer programming problem. This procedure is applied for a problem to find the stacking sequence having a maximum critical buckling load factor and the minimum weight. The object function in this case is the weight of a stiffened laminated composite shell. Three different types of stiffener layouts with different loading conditions are investigated to see how these parameters influence on the stacking sequence optimization of the panel and the stiffeners. It is noticed from the results that the optimal stacking sequence and lay-up angles vary depending on the types of loading and stiffener spacing.     

基于RBF代理模型的袋式除尘器清灰性能优化设计

针对袋式除尘器清灰系统性能优化设计中非线性和多参数耦合的问题,提出基于RBF代理模型的袋式除尘器清灰性能优化方法。以数值模拟为基础获取优化目标响应值。采用拉丁超立方试验设计控制清灰参数的设计空间,根据得到的试验数据建立清灰参数与响应值之间的RBF代理模型,并在此基础上采用遗传算法进行优化,得到距袋底1m处侧壁压力峰值最大的清灰参数组合。优化结果表明:距袋底1m处侧壁压力峰值由1.099kPa上升至2.294kPa,实现了对清灰性能的优化设计。     

On-line identification and optimization of feed rate profiles for high productivity fed-batch culture of hybridoma cells using genetic algorithms

In this paper, an on-line identification and optimization method based on genetic algorithms (GAs) has been used to optimize the productivity of a seventh-order nonlinear model of fed-batch culture of hybridoma cells. The parameters of the seventh-order nonlinear model are assumed to be unknown. The intention of this paper is to use GAs for (1) identifying the parameters of a seventh-order nonlinear model of fed-batch culture of hybridoma cells, and (2) determining the best feed rate control profiles for glucose and glutamine. The final level of monoclonal antibodies obtained by this method is then compared with the case where all the parameters are assumed to be known. It is found that the final level of monoclonal antibodies obtained by the on-line identification and optimization method is only about 3% less than the final level of monoclonal antibodies obtained by the case where all the parameters are assumed to be known. GAs proved to be a good alternative method for solving on-line identification and optimization problems.