基于协同优化算法的高速受电弓多学科设计优化

考虑不同学科间的相互影响及耦合作用对受电弓设计优化的影响,分析了高速受电弓不同性能的设计要求,采用多学科设计优化思想建立了受电弓多学科设计的系统级优化模型及运动学、静力学、动力学和控制学的子系统优化模型;根据不同学科设计参数的耦合关系,采用协同优化方法,获取高速受电弓整体设计优化结果;建立受电弓的三维模型,采用有限元软...     

高速受电弓结构参数集成设计研究

随着高速列车运行速度的不断提升,弓网受流质量严重恶化.为了提高弓网受流质量,考虑影响高速受电弓工作性能的设计因素,分析受电弓运动学、静力学、动力学和控制学的设计要求,采用多学科集成方法,建立受电弓各个学科及集成设计的优化模型,采用遗传算法求解受电弓的设计模型,获得高速受电弓的结构参数值,采用有限元方法,进行受电弓结构的...     

电力机车受电弓动态性能优化

在建立单臂受电弓模型的基础上,对高速铁路受电弓在运行过程中的接触力性能、几何参数及弓头轨迹等进行优化,结果表明在新的参数下弓网系统的接触力波动得到明显抑制,受电弓动态性能得以提高,为进一步开发高速机车受电弓提供了借鉴。     

基于两级气压伺服系统的高速受电弓主动控制研究

针对弹性链型悬挂接触网利用有限元法建立接触网模型,采用多体动力学建立受电弓模型,通过接触力元将接触网和受电弓进行耦合建立弓网耦合模型.然后建立气压伺服系统数学模型,根据车速信号采用比例控制策略控制升弓气囊提供弓网静态接触压力,根据采集得到的动态接触力信号采用模糊控制策略控制空气弹簧提供动态接触压力的补偿力.作动器分别作用于下臂杆和弓头悬挂,通过底架上的低频调节和弓头上的高频调节两级方式,主动控制接触压力,实现高速受电弓的稳定受流.     

弓头悬挂结构对受电弓性能的影响分析

为研究弓头悬挂结构对受电弓性能的影响,基于不同弓头悬挂结构的受电弓,搭建了刚性接触网条件下的弓网系统动力学耦合模型,对弓网接触力等进行仿真分析,对比不同弓头悬挂结构对弓网受流性能的影响。基于模态及振动理论,对不同弓头悬挂结构受电弓弓网受流性能的差异进行辨识。通过强度理论及机械结构概率设计方法,对相同工况下不同弓头悬挂结构的受电弓强度及可靠性进行分析。     

基于弓网接触力优化的主动控制式受电弓研究及设计

为改善高速动车组用受电弓弓网受流质量,提出并设计了一种基于弓网接触力优化的主动控制式气囊受电弓,该受电弓通过在线调节气囊气压,提高了弓网受流稳定性。     

一种快速收敛的多学科协同优化方法

为解决协同优化方法求解速度过慢的问题,提出一种快速收敛的多学科设计协同优化方法。在该求解方法中,系统优化的全部设计变量分为全局变量和局部变量,协同优化方法中的辅助设计变量被消去;系统级只控制全局变量,学科级控制局部变量;在系统级添加各学科中包含全局变量的约束条件,并基于学科状态方程构造耦合变量一致性约束条件;从各子学科中消除耦合变量的一致性问题,子学科目标的优化只对系统目标的最优起指向性作用。采用两个典型的多学科设计优化算例,检验快速收敛的多学科设计协同优化方法的计算特性,结果表明该方法的收敛迭代步数比协同优化方法大大减少,具有更快的收敛速度。     

多学科协同与设计优化关键技术研究

传统的设计优化一般主要受到某一个性能或学科的影响,因此造成整体设计优化的结果不理想,甚至互相矛盾,这就使设计优化必然走向系统和总体的设计优化.随着计算技术的发展,人们开始尝试将多学科的设计综合在一起,进行多学科协调优化.本文分析了多学科协同与设计优化关键技术,并提出了协同设计优化的方法,对探讨多学科协调优化有一定的参考价值.     

高速刚性弓网系统受电弓结构参数优化研究

通过弓网耦合系统动力学仿真和正交实验设计方法,研究了DSA250型受电弓六个结构参数变化对速度为160km/h的高速刚性地铁弓网系统动态受流性能的影响,选出了能改善受流性能的最优结构参数组合,并对各参数对受流性能的影响程度进行了排序。结果表明,选出的优组合参数能明显提高弓网系统的动态受流质量;六个结构参数中,受电弓弓头质量对弓网动态受流性能的影响最大,而上框架质量对受流性能的影响最小。     

高速受电弓整体结构特性分析

受电弓整体结构分析对于改进受电弓的设计,提高受电弓的性能,具有重要意义。为保证良好的受流质量,新型高速受电弓大量采用轻型合金材料制作,这可能导致机械强度和刚度的不足。在满足强度与刚度的前提下,最大限度减小受电弓归算质量,以改善弓—网动态性能是受电弓设计的关键问题。文中结合受电弓的开发过程,建立三维仿真模型,利用有限元计算软件Ansys,进行受电弓的强度和刚度校核以及动力特性分析。研究受电弓动力特性的目的在于优化其结构,以控制其模态频率与模态振型,对受电弓的设计具有指导意义。通过对受电弓有限元分析,进行虚拟设计,首次进行高速受电弓的整体结构特性分析。     

某火炮协调器多学科一体化设计优化

为了解决传统火炮优化设计中单学科建模分析的不足,采用多学科一体化设计优化方法对某火炮协调器系统进行了研究。通过对电气控制、液压、机械三个学科及学科间耦合关系的分析,建立了协调器结构控制一体化分析模型,结合多目标进化算法,对建立的一体化分析模型进行了设计优化研究。与原设计相比,优化后的设计使系统拥有更优的综合性能。     

汽车乘员舱安全性与舒适性多学科设计优化

概念设计阶段汽车乘员舱的安全性和舒适性设计是关键任务,两者具有耦合效应,将多学科设计优化运用到乘员舱的设计中实现两个学科的并行设计。针对某款车的乘员舱布置,在该车100%正面碰撞试验基础上,通过乘员损伤分析软件建立该车的正面碰撞乘员约束系统模型并对模型进行验证用于评估其安全性;同时也基于关节强度的概念建立乘员舒适性模型。将多学科设计优化运用到乘员舱的设计中,避免了传统设计方法对安全性和舒适性的单独优化,实现了两个学科的并行设计,缩短了开发周期,最大化了乘员舱的整体性能。为提高计算效率,通过试验设计构建乘员约束系统的Kriging近似模型用于代替仿真模型。结果表明,该方法在较好地满足乘员安全性的同时提高了乘员的乘坐舒适性,在乘员舱布置方面具有较强的工程实用性。     

Robust Collaborative Optimization Method Based on Dual-response Surface

A novel method for robust collaborative design of complex products based on dual-response surface (DRS-RCO) is proposed to solve multidisciplinary design optimization (MDO) problems under uncertainty. Collaborative optimization (CO) which decomposes the whole system into a double-level nonlinear optimization problem is widely Accepted as an efficient method to solve MDO problems. In order to improve the quality of complex product in design process, robust collaborative optimization (RCO) is developed to solve those problems under uncertain conditions. RCO does opfmiTation on the linear sum of mean and standard deviation of objective function and gets an optimal solution with high robustnmess. Response surfaces method is an important way to do approximation in robust design. DRS-RCO is an improved RCO method in which dual-response surface replaces system uncertainty analysis module of CO. The dual-response surface is the approximate model of mean and standard deviation of objective function respectively. In DRS-RCO, All the information of subsystems is included in dual-response surfaces. As an additional item, the standard deviation of objective function is added to the subsystem optimization. This item guarantee both the mean and standard deviation of this subsystem is reaching the minima at the same time. Finally, a test problem with two coupled subsystems is conducted to verify the feasibility and effectiveness of DRS-RCO.     

Adaptive parallel decomposition for multidisciplinary design

The conceptual design of a rotorcraft system involves many different analysis disciplines. The decomposition of such a system into several subsystems can make analysis and design more efficient in terms of the total computation time. Adaptive parallel decomposition makes the structure of the overall design problem suitable to apply the multidisciplinary design optimization methodologies and it can exploit parallel computing. This study proposes a decomposition method which adaptively determines the number and sequence of analyses in each sub-problem corresponding to the available number of processors in parallel. A rotorcraft design problem is solved and as a result, the adaptive parallel decomposition method shows better performance than other previous methods for the selected design problem.     

MULTIDISCIPLINARY ROBUST OPTIMIZATION DESIGN

Because uncertainty factors inevitably exist under multidisciplinary design environment, a hierarchical multidisciplinary robust optimization design based on response surface is proposed. The method constructs optimization model of subsystem level and system level to coordinate the coupling among subsystems, and also the response surface based on the artificial neural network is introduced to provide information for system level optimization tool to maintain the independence of subsystems, i.e. to realize multidisciplinary parallel design. The application case of electrical packaging demonstrates that reasonable robust optimum solution can be yielded and it is a potential and efficient multi-disciplinary robust optimization approach.     

基于状态空间的车辆耦合振动系统分析及优化

以车辆子系统间耦合关系为出发点,基于状态空间理论提出了以子系统耦合系数和相对衰减系数为考察指标的车辆子系统振动传递特性研究方法。采用拉丁超立方设计分析了悬置参数对系统耦合程度的影响,研究了耦合和子系统振动衰减的关系;采用非支配序列遗传算法进行了多目标优化并获取了最优解集,并结合子系统传递函数来验证结果。研究结果表明,以相对衰减系数最小化为目标,综合考虑发动机-底盘和车身-底盘子系统的耦合关系可有效改善车辆平顺性。     

Decomposition method of complex optimization model based on global sensitivity analysis

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.     

基于Agent模型的多学科设计优化方法

复杂工程系统的设计往往涉及多门学科 ,多学科设计优化 (MDO)方法是一类求解复杂系统设计优化问题的方法。本文首先简要回顾现有 MDO方法的特点 ,然后基于 Agent模型提出了一种新的多学科设计优化方法——子空间自主式优化方法。这种方法具有如下特点 :流程简单 ;各个学科组可自主地、并行地进行设计和优化 ;灵活性强 ;所需系统分析的计算次数少 ;没有系统级协调优化环节。为了验证这种算法的有效性 ,对二个典型算例进行了数值实验。初步的数值实验结果表明 ,二个典型的算例经过几次迭代后均能收敛于最优解。子空间自主式方法是一种较有潜力的 MDO方法 ,值得进一步研究     

A response surface based hierarchical approach to multidisciplinary robust optimization design

A multidisciplinary robust optimization design framework, concurrent subsystem robust design optimization, is proposed to obtain robust optimum solution in the large-scaled and coupled system. In this framework, response surfaces in the form of artificial neural networks provide information pertaining to system performance characteristics, and individual subsystems engage in performing robust optimization design in parallel while communicating with the system level. This optimization approach incorporates uncertainty analysis and generates a global robust optimum solution in an iterative fashion. Two applications are considered, and the results demonstrate that the approach yields a reasonable robust optimum solution, and it is a potential and efficient multidisciplinary robust optimization approach .     

基于粒子群优化和协同优化的多学科设计优化研究

协同优化是进行多学科设计优化的有效方法之一。本文将粒子群优化算法应用于协同优化,通过对系统级优化等式约束条件进行转换,克服了协同优化自身内部的计算缺陷,有效解决了当原始系统优化问题不满足Kuhn-Tucker条件时导致的计算困难,最后以数值计算和减速器设计为例进行了验证。结果表明本文提出的方法是有效的,同时也为将新型算法应用于多学科设计优化问题提供了参考。