Torpedo Overall Multidisciplinary Design Based on Collaborative Optimization

A torpedo multidisciplinary design optimization (MDO) based on the collaborative optimization is proposed. Through decomposition and coordination, some problems in torpedo design such as multidisciplinary coupling, large data volume and complex data relationships can be solved. Taking aim at some complex problems in the torpedo design, such as computation in multidisciplinary design, organization, modeling and information exchange, the collaborative optimization methods based on approximate technology are presented. An example to increase the torpedo range is also given. It demonstrates that the method can converge quickly, has higher reliability and smaller data throughput, and is a very effective MDO method.

Application of the optimal Latin hypercube design and radial basis function network to collaborative optimization

Improving the efficiency of ship optimization is crucial for modem ship design. Compared with traditional methods, multidisciplinary design optimization （MDO） is a more promising approach. For this reason, Collaborative Optimization （CO） is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.

MULTIDISCIPLINARY ROBUST OPTIMIZATION DESIGN

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

Uncertain Multidisciplinary Design Optimization on Next Generation Subsea Production System by Using Surrogate Model and Interval Method

The innovative Next Generation Subsea Production System(NextGen SPS)concept is a newly proposed petroleum development solution in ultra-deep water areas.The definition of NextGen SPS involves several disciplines,which makes the design process difficult.In this paper,the definition of NextGen SPS is modeled as an uncertain multidisciplinary design optimization(MDO)problem.The deterministic optimization model is formulated,and three concerning disciplines—cost calculation,hydrodynamic analysis and global performance analysis are presented.Surrogate model technique is applied in the latter two disciplines.Collaborative optimization(CO)architecture is utilized to organize the concerning disciplines.A deterministic CO framework with two disciplinelevel optimizations is proposed firstly.Then the uncertainties of design parameters and surrogate models are incorporated by using interval method,and uncertain CO frameworks with triple loop and double loop optimization structure are established respectively.The optimization results illustrate that,although the deterministic MDO result achieves higher reduction in objective function than the uncertain MDO result,the latter is more reliable than the former.

Multidisciplinary Design Optimization with a New Effective Method

Collaborative optimization （CO） is one of the most widely used methods in multidisciplinary design optimization （MDO）, which is an effective methodology to solve modem complex engineering problems. CO consists of two-level optimization problems which are system optimization problem and subspace optimization problem. The architecture of CO can reserve the autonomy of individual disciplines in maximum, while providing a mechanism for coordinating design problem. However, CO has low computation efficiency and is easy to diverge. For the purpose of solving these problems, the former improved methods were studied. The relaxation factors were used to change the system consistency constraints to inequality constraints, or the response surface estimation was used to surrogate the system consistency constraints. However, these methods didn＇t avoid the computational difficulties very well, furthermore, some new problems arose. The concept of optimum constraint sensitivity was proposed, and the quadratic constraints in system level were reformed. Hence, a new collaborative optimization was developed, which is called system level dynamic constraint collaborative optimization （DCCO）. The novel method is able to increase the exchange of information between system level and disciplinary level. The optimization results of each disciplinary optimization can be feedback to system level with the optimum constraint sensitivity. On the basis of the information, the new system level linear dynamic constraints can be constructed; it is better to reflect the effect of disciplinary level optimizations. The system level optimizer can clearly capture the boundary where disciplinary objective functions become zero, and considerably enhance the convergence. Two standard MDO examples were conducted to verify the feasibility and effectiveness of DCCO. The results show that DCCO can save the solving time, and is much better in terms of convergence and robustness, hence, the new method is more efficient.

Multidisciplinary Modeling and Optimization Method of Remote Sensing Satellite Parameters Based on SysML-CEA

To enhance the efficiency of system modeling and optimization in the conceptual design stage of satellite parameters,a system modeling and optimization method based on System Modeling Language and Co-evolutionary Algorithm is proposed.At first,the objectives of satellite mission and optimization problems are clarified,and a design matrix of discipline structure is constructed to process the coupling relationship of design variables and constraints of the orbit,payload,power and quality disciplines.In order to solve the problem of increasing nonlinearity and coupling between these disciplines while using a standard collaborative optimization algorithm,an improved genetic algorithm is proposed and applied to system-level and discipline-level models.Finally,the CO model of satellite parameters is solved through the collaborative simulation of Cameo Systems Modeler(CSM)and MATLAB.The result obtained shows that the method proposed in this paper for the conceptual design phase of satellite parameters is efficient and feasible.It can shorten the project cycle effectively and additionally provide a reference for the optimal design of other complex projects.

基于CIM的老旧小区改造多专业协同设计方法

老旧小区改造设计是一项系统且复杂的任务,涉及规划、建筑、景观、市政、交通等多专业领域知识和技术,需要多专业团队协同合作。传统的线性设计方式各专业间相对独立、沟通交流不足、协同效率低,常出现多专业设计成果冲突、信息交流不畅、方案反复调整、施工落地困难等问题,从而制约了老旧小区改造工作的高效开展。本文尝试构建基于CIM的老旧小区改造多专业协同设计方法,通过与行业从业者深度访谈,总结老旧小区改造协同设计中存在的问题,探索从全要素高精度CIM基础数据平台、多专业协同设计平台、多专业成果三维可视化和智能化辅助设计四个方面构建基于CIM的协同设计平台,以期推动老旧小区改造协同设计工作模式的发展和创新。

多学科设计优化方法在水下无人航行器中应用

为了提高水下无人航行器工作的可靠性和稳健性,本文采用多学科设计优化方法开展航行器的总体设计优化。首先开展水下航行器在总体设计阶段的学科设计,分析了各学科内部的模型和计算方法,明确了各学科输入、输出和耦合关系;开展了学科内参数建模,确立了总体设计的设计变量、系统参数和约束条件。然后以航行器质量最轻和机动性能最强为优化目标,获得设计变量计算最优解和优化设计主要输出结果值。设计变量优化计算值有效提高设计方案满足约束的能力,优化设计主要输出结果为水下无人航行器设计方案的选择提供了定量依据。

基于协同优化的耐压柱壳多学科多目标优化

为提高耐压柱壳的设计效率和结构性能,本文基于多学科设计优化思想,进行了耐压柱壳多学科设计优化的学科分解,确定了耐压柱壳多学科优化设计的设计参数,明确了设计变量间的耦合关系,设计了耐压柱壳参数化分析流程,进行了设计变量灵敏度分析。在利用响应面模型建立了具有较高拟合精度耐压柱壳近似模型的基础上,以质量排水量比和极限载荷为目标函数,建立了基于协同优化方法的耐压柱壳多学科多目标优化模型,在确定了系统层和学科层的优化求解方法基础上,设计了基于协同优化的耐压柱壳多学科多目标优化框架,进行了优化的求解工作,得到了优化方案。与初始方案的对比表明:所得到的Pareto解集中,部分方案在2个目标函数上均得到了优化,验证了优化框架的有效性。

新形势下医院“行政MDT”管理模式的设计与实践

新形势下,医院实施“行政多学科协作诊疗模式(MDT)”能够快速地解决系统的管理问题。中国医学科学院北京协和医学院肿瘤医院深圳医院根据多年的临床经验,结合MDT,设计出了适合中国医学科学院北京协和医学院肿瘤医院深圳医院的“行政MDT”管理模式,通过设立小组专项成员,制定与之相关的计划和制度,定期开展绩效评估和考核,不断地总结与反思,如医保的结算、医疗费用的控制、不良事件的上报等,运用“行政MDT”管理模式,可以有效地帮助医院解决问题,提高医院自身的管理能力,使医院能够长期、有效地发展。

用遗传算法提高协同优化方法的可靠性

分析了协同优化方法可靠性不好的根源。提出了用遗传算法来替代系统级优化问题中的基于梯度的优化算法的策略。实例表明所提出的策略是可行有效的。

大客车车身骨架多学科协同优化设计

对某全承载大客车车身骨架结构进行包括轻量化、刚度、强度、振动模态和翻滚的多学科设计优化,以提高客车的综合性能。为克服客车翻滚分析的高度非线性带来的寻优困难,并解决结构有限元分析的低效问题,建立各学科的近似模型。首先建立大客车车身结构有限元模型,其中刚度、强度以及振动模态分析由Msc.Nastran完成,翻滚分析由显式动力学软件Ls-dyna完成。其次结合工艺要求和灵敏度分析结果,选择出各学科的设计变量,用优化的拉丁方方法完成试验设计。在此基础上,采用响应面方法建立各学科结构响应的近似模型。建立客车车身骨架结构的多学科优化模型,并用协同优化方法求解。结果显示,优化后的设计方案在轻量化、刚度、强度、模态振动和翻滚安全性方面均优于原设计方案。

多学科协同设计中的网络视频会议系统

面向计算机支持的协同工作(CSCW)环境下的多学科协同设计,构建了一个基于类B/S结构的多点网络视频会议系统。分析了CSCW框架下多学科协同设计对网络视频会议的需求,讨论了Java媒体框架(JMF)技术和会议过程媒体流的传输与控制机制,并进行了类B/S架构下的系统设计与分析,建立了会议系统的网络组成结构,完成了系统总体功能设计和客户浏览器交互过程控制。系统为协同产品设计过程提供了知识交流与共享、冲突消解的多媒体工作环境。

基于协同优化和多目标遗传算法的车身结构多学科优化设计

在汽车车身结构NVH和侧面碰撞安全性研究中,实施多学科多目标优化的可行性设计。通过试验设计制定试验方案并进行数据采样,构建考虑整车侧撞安全性、白车身模态、静态弯曲刚度、扭转刚度和轻量化等性能的响应面近似模型,然后对车身结构分别进行确定性和可靠性轻量化单目标设计。最后,运用多目标遗传算法结合多学科协同优化对车身结构进行多目标优化设计,获取Pareto最优化解集。研究结果表明:可靠性优化设计较确定性优化设计而言,能考虑产品设计和生产过程中的不确定性因素,保证产品稳健性;车身结构的多目标优化设计全面考虑了车身结构轻量化、NVH和碰撞安全性能等多学科之间的耦合和解耦;设计者可按需选择其满意的优化结果,这将大幅缩减产品开发周期、降低产品开发成本。

基于FORM的齿轮传动多学科优化设计

通常多学科设计优化是一种确定性设计方法,未考虑不确定性因素的影响。为降低多学科设计优化过程中不确定性因素对系统性能的影响,将一次可靠性方法与协同优化方法相结合,应用到多学科设计优化中。建立基于一次可靠性方法的协同优化的数学模型,并阐述其求解流程,该方法可用于多学科设计优化领域的可靠性设计问题。分别运用协同优化方法和基于一次可靠性方法的协同优化实现了减速器齿轮传动的多学科优化设计,在这两种方法的系统级优化中,引入松弛变量,将一致性等式约束转化为不等式约束,使算法易于收敛。优化结果表明基于一次可靠性方法的协同优化方法求得的最优解使得约束条件满足了可靠性要求,提高了系统的可靠性,具有实际工程意义。

一种具有全局稳定性的多学科协同优化方法

针对协同优化对初始点选取敏感和易收敛到局部极值点的问题,提出了一种具有全局稳定性的协同优化方法。该方法采用全局优化和局部优化两阶段优化策略。在全局优化阶段,系统级优化采用较大的松弛因子,在保证学科间一致性的前提下,将初始点优化到全局极值点附近;在局部优化阶段,逐步减少松弛因子,增强学科间的一致性,得到全局极值点。两个典型的优化算例表明,优化结果不受初始点的影响,有效增强了协同优化的全局稳定性,并具有较好的可行性和收敛速度。

一种新的多学科系统不确定性分析方法——协同不确定性分析法

多学科设计优化中的不确定性对整个工程系统的设计过程具有非常重要的影响。对现存的不确定性定义进行比较和分类。详细描述多学科系统中的不确定性及其传播过程。在已有不确定性分析方法的基础上,提出一种新的多学科不确定性分析方法——协同不确定性分析法。该方法的基本思想是利用泰勒近似估算耦合变量和系统输出的方差,经过二次优化获得系统的稳健最优解,并对其具体计算过程进行详细描述。算例结果表明,协同不确定性分析法不仅是可行的,而且具有很高的精度,是分析多学科系统中不确定性的有效方法。

利用协同优化方法实现复杂系统分解并行设计优化

介绍了应用于航空航天领域内复杂系统设计的协同优化算法,并针对现有协同优化算法中系统级协调算法计算量大且易发散等缺点,分析了协同优化系统级协调算法的几何意义,提出了一个新的概念:子问题间不一致信息,并在此基础上构建了一种新的系统级协调算法:动态松弛算法,相对于现有系统级协调算法,动态松弛算法的计算量更小,鲁棒性更好。用本文提出的动态松弛算法实现了一齿轮减速箱的齿和轴的并行设计,计算结果表明了本算法的有效性。

基于组合代理模型的车身多学科设计优化

本文对比分析了4种单一代理模型和两种组合代理模型的预测能力,并基于各代理模型进行正面碰撞安全性多目标优化,结果发现,组合代理模型不仅具有较好的预测精度和稳健性,还能促进最优解的搜索。基于上述分析,采用组合代理模型拟合正面碰撞、侧面碰撞和白车身模态分析的各种响应,建立针对安全性的车身结构多学科优化模型,并用协同优化方法求解。结果表明:组合代理模型在多目标优化中有利于得到更满意的最优解;基于组合代理模型的车身结构多学科设计优化有效改善了汽车的碰撞安全性,并兼顾了轻量化和NVH性能。

协同优化算法的改进

针对协同优化算法是多学科设计优化方法中应用最广、效果最好的MDO算法,但是在应用中存在计算困难的现状,分析了引起协同优化算法计算困难的原因,提出一种改良的协同优化算法——ICO多学科设计优化方法。改进学科一致性约束,增加系统级罚函数,采用快速启动方法,较好地克服了协同优化算法存在计算困难的缺点。标准算例试验结果表明,ICO多学科设计方法能够有效提高算法的稳定性、可靠性和计算效率。