基于拓扑优化的水下航行器壳体设计

为了能够有效地降低水下航行器耐压壳体的重量,以耐压壳体的概念设计为研究内容,使用ANSYS二次开发语言,对壳体进行了基于渐进结构法的拓扑优化方案,并对各方案作以比较,最终形成了拓扑优化后的耐压壳体设计方案。优化结果表明,其结构型式十分新颖,值得深入地探讨。     

水下机器人耐压壳体结构优化

基于响应面法对水下机器人耐压壳体结构优化,以耐压壳体结构5个尺寸为优化变量,借助ANSYS求解结构应力响应数值,利用组合试验缩小优化变量范围后进行响应面试验设计,优化约束条件为耐压壳体强度和稳定性满足要求,目标函数为壳体质量最小,利用Design-Expert试验设计软件进行响应面优化求解,将结果与利用1st Opt数值计算软件得到优化数值对比,最终优化结果为耐压壳体质量降低15.52%,且壳体满足强度和稳定性要求,达到优化目的。     

模拟退火算法在水下航行器壳体设计中的应用

运用模拟退火算法 ,以水下航行器壳体的总质量最小为目标 ,对其结构参数进行全局优化设计。计算结果表明 :与常规优化方法相比 ,运用模拟退火算法可以求得全局最优解 ,并且具有一定的高效性     

基于多失效模式的水下航行器耐压壳体结构可靠性研究

耐压壳体结构是水下航行器重要的组成部分,考虑到结构强度和稳定性失效之间的相关性以及结构参数存在的随机性,基于可靠性理论开展多耦合失效模式下的耐压壳体结构性能分析研究。在研究过程中,首先基于二阶响应面法对耐压壳体结构的力学模型进行简化分析,获得相应的结构力学数学模型;其次在综合考虑结构参数随机性基础上,结合随机摄动法、二阶矩方法和窄界限法,建立水下航行器耐压壳体结构多失效模式可靠性分析模型;最后在分析计算各结构参数灵敏度基础上,获得结构参数对耐压壳体结构性能的影响规律。通过研究清楚地了解各因素对耐压壳体结构性能的影响程度,为结构设计提供一定的理论参考。     

圆柱薄壳结构动态综合优化

根据低噪声水下航行器壳体的设计要求,通过分析水下圆柱壳体结构振动和声辐射特性,及应满足的稳定性和强度要求,得出了低噪声圆柱薄壳结构优化设计方法.运用MATLAB优化工具箱对鱼雷圆柱壳体进行了结构优化设计,通过分析得出该圆柱壳体应满足的约束条件,得出了优化结果.     

某型UUV耐压壳体可靠性分析

耐压壳体是UUV各功能设备的装载平台,其可靠性水平关系到UUV的安全性、可靠性。文中应用可靠性理论中的应力-强度干涉模型,介绍了某型UUV水下耐压壳体强度可靠度计算方法,并给出了设计实例。     

基于ANSYS的迎流耐压壳体强度与稳定性分析

迎流耐压壳体是水下航行器的重要承压部分,必须对其进行强度和稳定性计算,由于其结构复杂,传统理论计算准确性较低,故应用有限元分析软件ANSYS进行分析。对迎流耐压壳体进行有限元建模、加载、强度分析及稳定性分析,获得迎流耐压壳体的应力情况和失稳临界载荷,为其结构设计提供了重要理论依据。     

加肋圆柱壳参数化建模与稳定性分析

加肋圆柱壳是鱼雷以及水下航行器较常采用耐压壳体形式。壳体除需满足一定的强度要求外,稳定性也是十分重要的一个方面。本文采用参数化建模的方法,利用ANSYS中的梁、壳单元,使用组合建模的方法进行加肋圆柱壳的建模,在保证足够精度的同时简化了建模减小了计算量。使用APDL语言编制了加肋圆柱壳的非线性屈曲分析程序,对稳定性进行了分析,并与理论计算的结果进行了比较。结果显示此方法具有较高的精度,具有实际应用价值。     

水下无人航行器壳体抗毁伤性能的数值仿真研究

应用ABAQUS通用有限元软件中的声固耦合算法,对水下无人航行器壳体在远场水下爆炸冲击波作用下的抗毁伤性能进行了数值仿真研究。在数值仿真中,计算出在三硝基甲苯炸药爆炸时,不同方向及爆距条件下航行器壳体结构的动态响应过程,确认了航行器壳体的易损舱段及位置,得出抗爆炸毁伤的安全距离,为水下无人航行器的结构设计和抗毁伤性能指标考核提供了参考。     

于ANSYS对混合驱动水下滑翔器耐压壳体设计及分析

对混合驱动水下滑翔器的耐压壳体进行了设计计算与有限元分析。首先对耐压壳体的材料、结构进行了选择设计,然后分别对耐压壳体壁厚、失稳、变形方面进行了设计计算,从而验证了结构、尺寸确定的理论可靠性。最后,通过对混合驱动水下滑翔器的耐压壳体中最危险部分进行强度和屈曲有限元仿真,得到分析结果与设计计算结果基本一致,从而验证了耐压壳体的设计是合理的、可行的,为混合驱动水下滑翔器的实体试验提供了很大的借鉴价值。     

基于组合优化算法的6R机器人逆运动学求解

针对逆运动学求解存在的多解、精度低及通用性差等问题,提出了一种适用于各类6R工业机器人求逆解的组合优化算法。根据经典D-H法建立了机器人运动学模型,以最小化位姿误差为目标,结合运动平稳性原则构造了逆解问题的目标函数,以线性加权和法设计了适应度函数。通过混沌映射初始化种群、收敛因子非线性更新、自适应惯性权重位置调整及引入模拟退火策略等4种措施得到了一种改进的鲸鱼优化算法,并用于逆运动学求解。组合算法将鲸鱼算法求解的结果作为初始值,再利用Newton-Raphson数值法迭代出满足精度要求的运动学逆解。仿真试验结果表明：改进后的鲸鱼算法求解性能得到了较大提高,相比于直接利用鲸鱼算法进行逆运动学求解,组合优化算法具有求解速度快、稳定性好、精度高的特点,证明了该算法求逆的可行性与有效性。     

基于组合优化策略的动车组悬挂参数优化设计

为快速获得改善车辆横向平稳性的最优悬挂参数,提出基于自适应模拟退火算法和非线性序列二次规划算法的组合优化策略对动车组悬挂参数进行优化设计。建立动车组动力学模型,利用最优拉丁超立方抽样方法选取对横向平稳性影响较大的悬挂参数作为设计变量;以横向平稳性为目标函数构建Kriging代理模型,并利用可决系数检验代理模型精度;采用自适应模拟退火算法对代理模型进行全局范围内初步寻优,在初步最优解的基础上采用非线性序列二次规划算法进行局部空间精确求解。研究结果表明,基于Kriging代理模型和组合优化策略的优化效率明显提高,车辆横向平稳性得到显著改善,并且优化前后运行稳定性均满足要求。     

基于遗传算法的行星轮减速器优化

遗传算法可以通过模拟自然进化的过程来搜索最优解,只需目标函数及适应度函数,不需要依赖梯度或其它辅助信息,多用于函数优化、组合优化、生产调度、自动控制、机器人学、图像处理、遗传编程等诸多方面。文中利用遗传算法对行星轮减速器进行了多目标函数的遗传算法优化分析。     

Research on job-shop scheduling optimization method with limited resources

Job-shop scheduling is an important subject in the fields of production management and combinatorial optimization. It is also an urgent problem to be solved in actual production. It is usually difficult to achieve the optimal solution with classical methods, due to a high computational complexity (NP-Hard). According to the nature of job-shop scheduling, a solution based on a particle swarm optimiser (PSO) is presented in this paper. In addition to establishing a job-shop scheduling model based on PSO, we have researched the coding and optimized operation of PSO. We have also considered more suitable methods of coding and operation for job-shop scheduling as well as the target function and calculation of the proper figure. The software system of job-shop scheduling is developed according to the PSO algorithm. Test simulations illustrate that the PSO algorithm is a suitable and effective approach for solving the job-shop scheduling problem.     

Optimization of operation sequencing in CAPP using simulated annealing technique (SAT)

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an over constrained overall solution space and the final solution is normally far from optimal or even non-feasible. The operation-sequencing problem in process planning is considered to produce a part with the objective of minimizing the sum of machine, setup and tool change costs. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix and reward–penalty matrix using simulated annealing technique (SAT), a meta-heuristic. A number of benchmark case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. The proposed SAT integrates robustness, convergence and trapping out of local minima.     

Genetic algorithm with new encoding scheme for job shop scheduling

In so many combinatorial optimization problems, job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many nonpolynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, and nine methods were proposed to encode a chromosome to represent a solution. In this paper, we proposed a novel genetic chromosome-encoding approach; in this encoding method, the operation of crossover and mutation was done in three-dimensional coded space. Some big benchmark problems were tried with the proposed three-dimensional encoding genetic algorithm for validation and the results are encouraging.     

Virtual enterprises partner selection based on reverse auctions

Partner selection is a key issue in the development of an effective coalition formation mechanism for virtual enterprises (VE). Combinatorial reverse auction can be applied by a firm to select the best partners to minimize the cost in forming VE. The objectives of this paper are to propose architecture for selecting partners based on combinatorial reverse auction mechanism to minimize the cost of VE, develop algorithms to find a near-optimal solution efficiently, and implement a prototype system based on the proposed algorithms. We formulate the partner selection problem based on combinatorial reverse auctions and apply Lagrangian relaxation technique to solve the problem. Our partner selection solution algorithms include an algorithm for solving bidders’ subproblems by exploiting their problem structures, a subgradient algorithm for solving the dual problem, and a heuristic algorithm for finding a near-optimal solution. In addition to theoretical development, we also implement a prototype system based on the proposed algorithms and web services technologies to verify the effectiveness of our methodology.     

交货期惩罚下柔性车间调度多目标Pareto优化研究

针对传统作业车间调度问题的局限性,结合实际生产过程的特点和约束条件,建立路径柔性的作业车间调度仿真模型。采用连续空间蚁群算法,对柔性车间作业进行多变量、多约束下的调度布局优化设计,在考虑各个机器提前/拖期完工的惩罚值,所有机器上的总负荷、成品合格率和最大设备利用率等性能指标更加合理情况下,为每次迭代产生的邻域解集作为Pareto非支配排序,防止算法操作过程中劣解的产生,提高求解效率。并与自适应免疫算法和交换序列混合粒子群法的优化结果进行对比,该算法可有效改善基本蚁群算法的停滞现象和全局寻优能力差的缺点。目前,该方法已在某机械公司进行示范,在提高加工效率、降低生产成本、减少协作费等方面效果显著。     

蚁群优化算法研究综述

介绍了蚁群优化算法的基本原理、流程和研究现状,重点评述了近年来蚁群优化算法在组合优化和连续优化两个领域的研究现状,并展望了这一领域的研究方向。     

基于人机交互的遗传退火算法及其在履带起重机布局设计中的应用

针对履带起重机上车构件布局优化问题，给出一种基于人机交互的遗传退火算法。该算法在退火遗传算法的基础上引入“人机合作”思想，充分发挥人的主观能动性，改善了算法的性能。与传统算法相比，该算法收敛快、通用性好，适合求解复杂优化问题。利用本算法得出了符合工程要求的优化结果。