Multidisciplinary Design Optimization of A Human Occupied Vehicle Based on Bi-Level Integ

某自行火炮全弹道多学科优化设计牛福强;洪亚军;徐诚【摘要】To improve a self-propelled guns kill efficiency of the projectile,and find the design parameters of optimal matchingrelations,and shorten the design cycle,the model of self-propelled gun’s full ballistic multidisciplinary design optimization is build,which dependson the coupling relationship of interior ballistic,exterior ballistics,and the terminal ballistic. To get the maximum of lethal area and range of fire,the full ballistics is optimized. Compared with the traditional design methods which are single subject optimization,the multidisciplinary design optimization method can effectively improve the comprehensive performance of the gun’ballistic,and avoid the phenomenon of other target’s degradation when a single target optimization.%为提高某自行火炮弹丸的杀伤效能，寻求该火炮全弹道设计参数间更优的匹配关系，并缩短全弹道设计周期，根据其包含的内弹道、外弹道和终点效应3个分学科之间的耦合关系，建立自行火炮全弹道多学科优化设计模型。

新能源汽车轻量化钢制车身结构摘要未来钢制汽车计划（FSV）的目标是为紧凑型的电动汽车（BEV）提出一个能制造出完全不同的钢制车身结构的详细设计构思，也确认了为适应大的插电式混合动力车（PHEV）或燃料电池车（FCEV）车身结构的改变。

这篇文章将说明七个经过优化的车身的子部件是如何达到减重35%，同时满足安全要求和整个寿命周期内碳排放目标要求。

该文章也将对先进的设计优化过程和相应先进的钢材和制造技术概念进行解释。

前言“未来钢制汽车计划（FSV）”是世界汽车钢（WorldAutoSteel）项目，该组织为世界钢铁联盟下属的汽车钢组，共包含全球范围内17家大型钢铁生产企业。

“FSV计划”是一个涉及几百万欧元资金，为期三年的计划，旨在发展出安全、重量轻及采用先进高强钢制造的车身结构，该新型车身结构能够满足电动汽车的不同要求和减少汽车在整个寿命周期内的温室气体（GHG）排放。

GHG气体指的是大气中能够加剧地球温室效应的气体，这些气体能够吸收地球表面的热量，使热量在地球表面和大气层之间进行循环，导致地球表面的平均温度升高。

“FSV计划”将会阐明用先进高强钢来制造车身结构，减轻汽车重量和减少GHG气体排放。

本文说明了“FSV计划”中的相关的钢铁技术和设计构思，及第二阶段现已所获得的结果。

1.0 项目目标“FSV计划”中工程技术人员关注的焦点是提出一种新的全局性的开发设计方法，目标是开发出具有创新性的整车布置和优化的车身结构的先进汽车，该车将会使用一系列在2015年至2020年之间比较成熟的先进钢铁材料和制造技术。

“FSV计划”主要分为三个阶段：阶段1：工程研究（已完成）阶段2：构思与设计（至2010年）阶段3：展示和具体实施（至2011年）第一阶段主要是对将来适用于2015年至2020年之间的，先进汽车动力系统和适合批量生产的未来汽车技术进行综合性评价和验证，该阶段所获得的结果在另外一篇报告中有阐述。

图1-1 “FSV计划”的整个设计优化过程“FSV计划”已进行到第二阶段的中间阶段，优化设计采用先进高强钢制造的车身结构，主要涉及到4种不同的汽车，电动汽车（BEV）和插电式混合动力汽车（PHEV-20），属于A级/B级汽车；插电式混合动力汽车（PHEV-40）和燃料电池汽车（FCEV），属于C级/D 级汽车。

multidisciplinary design analysis and optimization 1. 引言1.1 概述多学科设计分析与优化是一种综合了不同学科领域知识的研究方法和技术。

在现代工程设计中，为了解决复杂的问题，需要融汇多个学科领域的知识和经验。

多学科设计分析与优化旨在通过将不同学科领域的技术和方法结合起来，提供全面的解决方案，并找到最佳设计。

1.2 文章结构本文将首先介绍多学科设计分析与优化的概念及其重要性。

然后，讨论多学科设计分析所涉及的不同方法和工具，并通过实例应用和案例研究进行说明。

接下来，我们将探讨在应用多学科设计分析与优化时面临的挑战，并提出解决方案。

最后，我们总结主要观点和发现，并展望未来研究方向。

1.3 目的本文旨在对多学科设计分析与优化进行全面而系统的介绍，以帮助读者深入理解其概念、方法和应用。

该篇文章还将重点讨论在实践中遇到的挑战，并提出相应解决方案。

通过阅读本文，读者将能够对多学科设计分析与优化的重要性有更深入的了解，并在实际工程设计中应用相关方法和工具。

以上是“1. 引言”部分的详细内容。

2. 多学科设计分析与优化概述2.1 多学科设计多学科设计是一种集成了不同学科知识的设计方法。

传统的工程设计通常只关注特定领域，例如机械设计、电气设计或结构设计等，而忽视了其他相关学科的因素。

然而，在现代复杂系统的开发中，多个学科之间的相互作用和影响变得越来越重要。

因此，多学科设计强调整体性和综合性思考，将各个学科的要求、限制条件和目标统一考虑进来。

2.2 设计分析和优化概念设计分析是指通过建立数学模型和仿真技术来评估并理解产品或系统在不同条件下的行为和性能。

它可以帮助工程师预测产品在实际运行中可能出现的问题，并提供优化方案以改善其性能。

而优化则是指在给定约束条件下，寻找最佳解决方案以达到特定目标。

在多学科设计中，优化需要考虑各个学科之间的相互关系，并综合考虑各个学科对整体性能的影响。

多学科设计优化简要介绍多学科设计优化 (Multidisciplinary Design Optimization,简称 MDO)是一种通过充分探索和利用工程系统中相互作用的协同机制来设计复杂系统和子系统的方法论。

其主要思想是在复杂系统设计的整个过程中利用分布式计算机网络技术来集成各个学科 (子系统 )的知识，应用有效的设计优化策略，组织和管理设计过程。

其目的是通过充分利用各个学科(子系统 )之间的相互作用所产生的协同效应，获得系统的整体最优解，通过实现并行设计，来缩短设计周期，从而使研制出的产品更具有竞争力。

因此，MDO宗旨与现代制造技术中的并行工程思想不谋而合，它实际上是用优化原理为产品的全寿命周期设计提供一个理论基础和实施方法。

MDO研究内容包括三大方面：1，面向设计的各门学科分析方法和软件的集成；2，探索有效的 MDO算法，实现多学科 (子系统 )并行设计，获得系统整体最优解；3，MDO分布式计算机网络环境。

多学科设计优化问题 ,在数学形式上可简单地表达为：寻找：x最小化：f=f(x,y)约束：hi(x,y)=0 (i=1 ,2 ,… ,m) gj(x,y)≤ 0 (j=1 ,2 ,… ,n)其中：f 为目标函数；x为设计变量；y是状态变量；hi(x,y)是等式约束；gj(x,y)是不等式约束。

状态变量 y，约束 hi 和 gj以及目标函数的计算涉及多门学科。

对于非分层系统，状态变量 y，目标函数 f，约束hi 和 gj 的计算，需多次迭代才能完成；对于分层系统，可按一定的顺序进行计算。

这一计算步骤称为系统分析。

只有当一设计变量 x通过系统分随着科学技术日新月异的发展，我们的武器装备，尤其是战斗机的水平日益提高，装备复杂程度已远超乎平常人的想象，装备设计不单要用到大量的人力，甚至已牵涉到了数十门学科。

例如，战斗机设计中就包括了液压、传动、流体力学、计算流体力学、空气动力学、发动机、结构力学、传热学、热力学、自动控制、电子、软件、计算机、可靠性、维修性、保障性、安全性、测试性等若干学科。

操稳特性快速评估及其在飞机设计中的应⽤南京航空航天⼤学硕⼠学位论⽂操稳特性快速评估及其在飞机设计中的应⽤姓名：张帅申请学位级别：硕⼠专业：飞⾏器设计指导教师：余雄庆20081201南京航空航天⼤学硕⼠学位论⽂摘要飞机总体设计阶段需要对飞机设计⽅案的操稳特性作出快速评估。

在采⽤主动控制技术进⾏飞机总体设计时，还需要考虑飞⾏控制系统的作⽤，对包括飞控系统在内的全机操稳特性进⾏快速评估。

针对以上需求，本⽂主要完成了以下研究⼯作：1）对应⽤飞机操稳分析程序（DATCOM）计算⽓动系数和导数的⽅法进⾏了研究，提出了⼀些实⽤的输⼊⽂件建模⽅法，为DATCOM程序开发了输⼊与输出接⼝程序，提⾼了DATCOM 程序的使⽤效率，⽅便了程序与其它分析系统的集成。

2）应⽤Simulink对⽓动数据进⾏插值处理，并建⽴了⾮线性模型；应⽤MATLAB的功能函数实现了⾃动提取线性模型以及根据线性模型分析飞机本体的操稳特性。

3）利⽤ Simulink 的建模及仿真功能，实现了飞控系统的建模、控制律设计和仿真，并建⽴了全机仿真分析模型，实现了全机仿真模型的快速配置以及飞⾏仿真。

4）对MATLAB的RTW⼯具以及引擎技术进⾏了研究，实现了仿真模型的编译处理以及外部调⽤。

在此基础上，将带仿真模型的分析程序进⾏了编译处理；采⽤iSIGHT软件对编译⽣成的可执⾏⽂件进⾏了集成，完成了操稳特性快速评估系统的构建；系统可以单独运⾏，也可以在飞机多学科优化设计系统中作为⼦系统应⽤。

5）在飞机总体设计中应⽤操稳特性快速评估系统，研究了飞翼布局⽆⼈机的操稳特性，以及⼤型民⽤飞机放宽静稳定度技术。

应⽤研究充分表明，本⽂所提出的⽅法和建⽴的操稳特性快速评估系统特别适合飞机总体设计阶段对操稳特性的评估，为飞机多学科优化设计以及应⽤主动控制技术进⾏飞机总体设计提供了有效的操稳分析⽅法和⼯具。

关键词：飞机总体设计；操稳特性；主动控制技术；飞⾏控制系统；控制律；飞⾏仿真操稳特性快速评估及其在飞机设计中的应⽤AbstractIn the conceptual design of aircraft, the S&C (stability and control) of the aircraft need to be evaluated rapidly. When ACT (Active Control Technology) is used in the conceptual design，the rapid evaluation of the S&C need to include the FCS (Flight Control System). The Methods and tool were developed in this thesis to meet this need. The research work in this thesis is presented as the followings:1) In terms of computing aerodynamic coefficient and derivative, some useful methods for input file modeling was improved for DATCOM, some interface code for input and output of DATCOM was developed. The interface code make the DATCOM easier to use and more convenient to be integrated with other codes.2) In Simulink software environment, aerodynamic data interpolation has been completed and non-linear aerodynamic model was set up. Linear model could be extracted from non-linear model by functions in MATLAB. And then the inherent S&C of the aircraft could be analyzed.3) By use of modeling and simulation function in Simulink software, the modeling, control law designing and simulation of the FCS was completed. Then the flight simulation model of the whole aircraft with FCS could be set up.4) By use of the RTW tools and ENGINE technolony in MATLAB, flight simulation model could be compiled. Therefore, all ofthe analysis codes have been compiled to executable files. These files have been integrated by iSIGHT software and built into an integrated rapid evaluation system of the S&C. This system could be used alone or as a sub-system in MDO (Multidisciplinary Design Optimization).5) The rapid evaluation system have been verified by two examples. An unmanned air vehicle with fly-wing configuration was analyzed by this system, and a civil jet transport with RSS (Relaxed Static Stability) technolony was analyzed by this system.The applications demonstrate that the methods and rapid evaluation system developed in this thesis can meet the need of S&C evaluation in the conceptual design. It can be used as an effective tool in the conceptual design of aircraft with ACT technology, as well as a subsystem of MDO.Keywords:Conceptual Design of Aircraft; S&C; ACT; FCS; Control Law; Flight Simulation南京航空航天⼤学硕⼠学位论⽂图表清单图1.1 主动控制技术（下）与传统飞机设计⽅法（上）⽐较 (3)图1.2 飞机多学科优化设计系统框架 (4)图1.3 操稳特性快速评估系统框图 (8)图2.1 本⽂所采⽤的飞机坐标系 (9)图2.2 DATCOM输⼊⽂件的构成⽅式 (12)图2.3 DATCOM输⼊⽂件⽰例 (13)图2.4 导⼊到MATLAB中的DATCOM输出数据 (15)图2.5 输⼊接⼝程序⽣成的DATCOM输⼊⽂件及构成关系 (17)图2.6 利⽤插值处理模块对阻⼒系数有关数据插值处理 (23)图2.7 以Simulink封装模块建⽴的飞机本体⾮线性模型 (27)图2.8 以封装模块与S-function运动⽅程建⽴的飞机本体⾮线性模型 (27)图3.1 飞控系统的⼀般构成 (34)图3.2 飞机纵向线性系统仿真分析模型⽰例 (35)图3.3 ⼤⽓扰动仿真模型 (36)图3.4 爬升指令仿真模型 (37)图3.5 传感器与测量模型 (38)图3.6 ⼤⽓数据计算机仿真模型中的离散采样环节 (39)图3.7 陀螺仪和线加速度计模型 (39)图3.8 舵机仿真模型 (40)图3.9 飞机系统仿真模型 (40)图3.10 全机飞⾏仿真分析模型 (41)图3.11 Simulink与FlightGear视景仿真的接⼝模型 (43)图4.1 系统集成⽅案原理框图 (45)图4.2 采⽤iSIGHT完成的系统集成 (47)图5.1 飞翼⽆⼈机外形及舵⾯布置⽰意图 (48)图5.2 飞控系统的Simulink仿真模型 (49)图5.3 增稳后的扰动运动模态曲线（纵向） (50)图5.4 飞⾏⾼度变化仿真曲线 (50)图5.5 飞机俯仰⾓变化仿真曲线 (51)图5.6 放宽静稳定度对超声速运输机构型的影响 (52)图5.7 飞机优化设计前的基本构型 (53)操稳特性快速评估及其在飞机设计中的应⽤图5.8 优化后的平尾平⾯形状与初始设计形状的对⽐ (55)表2.1 DATCOM中常⽤参数表及控制参数的功能 (11)表2.2 常⽤的DATCOM计算输出结果 (14)表2.3 DATCOM+输出⽂件的类型及其功能 (16)表2.4 DATCOM+中各程序的功能 (16)表3.1 常⽤飞控系统的复杂度分类 (34)表3.2 FlightGear中常⽤的配置参数 (42)表4.1 操稳特性快速评估系统中的各部分程序 (44)表4.2 MATLAB引擎与VC的接⼝函数 (46)表5.1 ⽆⼈机本体扰动运动模态（纵向） (48)表5.2 增稳后的全机扰动运动模态（纵向） (49)表5.3 飞机主要的总体设计参数 (53)表5.4 设计变量、约束、⽬标及其优化结果 (54)表5.5 优化后主要外形特征参数及升阻特性的变化 (55)南京航空航天⼤学硕⼠学位论⽂注释表A展弦⽐ H , h ⾼度 a声速；主轴⽅位⾓ I 转动惯量 b展长 K n 纵向静稳定裕度 c A平均⽓动弦长 L M N 总⼒矩在机体轴系上的分量 A C轴向⼒系数 A A A L M N ⽓动⼒矩在机体轴系上的分量D C阻⼒系数 M a 飞⾏马赫数 L C升⼒系数 P 发动机推⼒或拉⼒ m C俯仰⼒矩系数 Q 动压，0.5ρV 2 N C法向⼒系数 p q r 滚转，俯仰，偏航⾓速度 L C α升⼒系数对攻⾓的导数 T 发动机作⽤⼒在机体x 轴的分量m C α俯仰静稳定性导数 u v w 空速在机体轴上的分量 Y C β侧⼒系数对侧滑⾓的导数 V 空速 n C β偏航静稳定性导数 W 飞机重量 l C β滚转静稳定性导数 cp x 压⼒中⼼的相对位置 Lq C升⼒系数对俯仰⾓速度的导数 ac x 焦点（⽓动中⼼）的相对位置mq C 俯仰⼒矩系数对俯仰⾓速度的导数cg x 重⼼的相对位置L C α升⼒系数对攻⾓变化率的导数 Y 侧⼒ m C α俯仰⼒矩系数对攻⾓变化率的导数α攻⾓（迎⾓） Yp C侧⼒系数对滚转⾓速度的导数β铡滑⾓ lp C滚转阻尼导数 e δ升降舵偏转⾓ lr C滚转交叉导数 f δ襟翼偏转⾓ np C航向交叉导数 a δ副翼偏转⾓ nr C航向阻尼导数φ滚转⾓ C P螺旋桨拉⼒系数θ俯仰⾓ D阻⼒ψ偏航⾓ F x F y F z空⽓动⼒在机体轴系上的分⼒ρ空⽓密度 G重⼒ξ阻尼⽐ g重⼒加速度 n ω固有频率承诺书本⼈声明所呈交的硕⼠学位论⽂是本⼈在导师指导下进⾏的研究⼯作及取得的研究成果。

10.16638/ki.1671-7988.2021.07.021基于多学科优化的正碰约束系统参数设计刘吉1，傅林1，张光辉1，黄杰2（1.威马汽车科技集团有限公司，四川成都610100；2.中国汽车工程研究院，重庆401122）摘要：约束系统是汽车被动安全的重要组成部分，为提高约束系统的有效性，以Hybrid III 50th男性假人在100%重叠正面碰撞工况下的得分为基础进行多学科优化分析，得到了安全带及安全气囊参数与假人胸部、小腿之间的变化关系。

并通过调整安全带和安全气囊参数，得到了最优参数匹配，使假人得分从14.12分提高到14.46分，有效提高了假人得分，对约束系统参数设计具有一定的参考意义。

关键词：多学科优化；约束系统；正碰；参数设计中图分类号：U462.2 文献标志码：A 文章编号：1671-7988(2021)07-62-04Frontal Impact Restraint System Parameter Design Basedon Multidisciplinary OptimizationLiu Ji1, Fu Lin1, Zhang Guanghui1, Huang Jie2( 1.Weltmeister Motor Technology Co., Ltd., Sichuan Chengdu 610100;2.China Automotive Engineering Research Institute, Chongqing 401122 )Abstract: Restraint system is a significant component of vehicle passive safety. In order to improve the effectiveness of the restraint system, multidisciplinary optimization analysis has been completed based on the score of Hybrid III 50th male dummy under 100% overlapping frontal impact condition. The relationship between the parameters of safety belt and airbag and the dummy chest and lower leg was obtained. And the optimal parameter matching was obtained by adjusting the parameters of seat belt and airbag. The dummy score was increased from14.12 to 14.46, which effectively improves the dummy score. It has a certain reference significance for the parameter design of restraint system.Keywords: Multidisciplinary optimization; Restraint system; Frontal impact; Parameter designCLC NO.: U462.2 Document Code: A Article ID: 1671-7988(2021)07-62-04引言随着人们对于汽车安全问题更加重视，各大厂商也在加大安全方面的研发投入，约束系统是汽车被动安全的重要组成部分，汽车碰撞过程中，安全带的使用可大大降低乘员伤亡[1]。

基于iSIGHT的多学科设计优化平台的研究与实现一、本文概述随着现代工程技术的快速发展，产品设计的复杂性日益增加，涉及多个学科领域的知识和技术。

这种复杂性要求设计师在设计过程中必须考虑多种因素，如性能、成本、可靠性、可制造性等，从而实现整体最优设计。

然而，传统的设计优化方法往往只能针对单一学科进行优化，难以处理多学科之间的耦合和冲突。

因此，开发一种基于多学科设计优化（MDO）的平台，对于提高产品设计的质量和效率具有重要意义。

本文旨在研究并实现一种基于iSIGHT的多学科设计优化平台。

iSIGHT作为一种先进的优化算法平台，具有强大的优化求解能力和丰富的优化算法库，为多学科设计优化提供了有力支持。

本文将首先介绍多学科设计优化的基本原理和方法，然后详细阐述基于iSIGHT 的多学科设计优化平台的架构、功能和技术实现，并通过具体案例验证平台的可行性和有效性。

通过本文的研究和实现，旨在为设计师提供一个高效、可靠的多学科设计优化工具，帮助他们在设计过程中综合考虑多个学科因素，实现整体最优设计。

本文也希望为相关领域的研究者和技术人员提供一些有益的参考和启示，推动多学科设计优化技术的发展和应用。

二、多学科设计优化概述随着现代工程技术的不断发展和复杂性的增加，传统的单学科设计优化方法已经无法满足许多复杂系统的设计要求。

因此，多学科设计优化（MDO，Multidisciplinary Design Optimization）应运而生，它通过将不同学科的知识、方法和工具集成在一起，实现复杂系统整体性能的最优化。

MDO旨在解决在产品设计过程中出现的跨学科耦合问题，以提高产品的设计质量和效率。

MDO的核心思想是在产品设计阶段就考虑不同学科之间的相互影响和约束，通过协同优化各个学科的设计参数，实现整个系统的全局最优。

这种方法能够有效地减少设计迭代次数，缩短产品开发周期，并降低成本。

同时，MDO还能够提高产品的综合性能，使其在满足各项性能指标要求的同时，达到最优的整体效果。

发生这样严重的事故后，研发和测试人员对事故原因进行深入分析，有限元分析结果表明该型燃机透平轮盘周向破裂裕度严重不足，设计点状态下存在许多位置应力水平超过材料屈服极限，部分开孔位置甚至局部应力水平超过材料断裂极限。

事故中破裂的第二级透平转子为整体叶盘式，子整圈共计有36个叶片，由于外委设计方设计经验不足，未能按流程进行完整设计，为了确保后续机型安全运行，对该型燃机透平转子进行结构优化势在必行。

有限元网格见图3，边界条件为第一级盘前的端齿连接面施加轴向固支，模型内部各零件间采用标准接触，摩擦系数设置为0.15，叶片离心载荷和气动载荷分别在相应位置建立mass21单元的质量点上施加。

通过有限元分析，可以得到原始设计的两级透平转子等效应力分布，示意见图4。

————————————————————作者简介:顾明恒（1989-），男，江苏盐城人，强度工程师，研究方向为航空发动机与燃气轮机结构强度研究。

图1事故后现场破坏情况图2稳态温度载荷分布/℃2.3盘体参数敏感性分析几何全约束的透平盘参数化模型中的变量较多，本文在优化设计之初进行了灵敏度分析，以便捷的筛选出了对透平盘强度影响较大的关键几何尺寸参数，便于有针对性的提高后期优化设计效率。

进行透平盘盘体几何参数灵敏度计算时，各几何参数的范围选取为原始设计尺寸的±1%。

首先，基于拉丁超立方抽样法构造响应面，每组抽样156次；其次使用随机抽样进行误差分析，每组误差检验的抽样为12次；最后以透平盘体最大等效应力、盘心最大周向应力与幅板最大径向应力作为灵敏度分析的目标函数。

对透平盘体最大等效应力、盘心最大周向应力与幅板如果未观察到明显异常发生，则转速进一步缓慢提升至110%设计转速，维持五分钟，如果未观察到明显异常，则转速进一步缓慢提升至115%设计转速维持五分钟，如果未观察到明显异常，则转速继续缓慢提升，直至某级转子发生破裂。

（图6）试验中二级透平盘先发生破裂，与理论分析一致，破裂转速与理论分为，试验论分析的准确，结微型燃气轮机透平轮盘超转破裂能力满足设计要求，具备产品应用条件。

多学科优化设计在航空航天领域的应用及发展李　哲(国防科学技术大学人文管理学院,长沙　410073)马忠辉(北京宇航系统工程研究所,北京　100076)摘　要　多学科优化设计是世界各国工业设计界新兴的研究领域。

文章对多学科优化设计的主要基本理论、应用研究及发展进行了综述分析。

优化算法是多学科优化设计的重要内容,文章对此进行了介绍,并对多学科优化设计中涉及的软件开发及集成设计框架、软件结构管理的研究成果及研究动态进行了分析。

总结并对比分析了多学科优化设计在航空航天领域的几个典型应用及其特点,并将多学科优化设计与计算机集成制造系统的研究与发展的关系进行评述。

关键词　多学科优化设计　信息集成　工程设计　算法　计算机集成制造系统中图分类号:　T B21　文献标识码:A 文章编号:1009-8518(2004)03-0065-06Study and Development of Multidisciplinary Design OptimizationLi Zhe(National University of Defense T echnology ,Changsha 410073)Ma Zhonghui(China Academy of Launch Vehicle T echnology System Engineering Division ,Beijing 100076)Abstract Multidisciplinary design optimization is a new field within engineering design all over the w orld 1In this paper ,the current status and development of s ome basic theories ,such as optimization alg orithm ,framew ork and s oftware configuration management ,are introduced 1S ome representative applications of MDO in aircraft and reusable launch vehi 2cle are presented 1The relationship between CI MS and MDO is discussed 1K ey Words Multidisciplinary design optimization In formation integration Ag orithm C om puter integrated man 2u facturing system Engineering design收稿日期:2004-05-301　引言航空航天领域的设计,如飞机、导弹等都涉及到气动、结构、控制、发动机等很多学科,而传统的串行设计方式的最大弊端在于它人为的割裂了各学科之间的相互作用,并没有利用各学科之间相互影响产生的协同效应。

多学科设计优化算法研究综述杨磊;韦喜忠;赵峰;李胜忠【摘要】随着多学科设计优化(Multidisciplinary Design Optimization,MDO)技术的发展,它将成为优化设计的大趋势.为了能够更好地运用MDO来解决船舶设计优化问题,本文对近年来发展的MDO算法进行系统梳理和分类,对发展已较成熟的几种算法进行简要介绍,对新发展的几种特殊算法应用环境、性能特点进行重点介绍;最后,对MDO算法研究存在的不足和今后发展趋势提出了若干建议.%With the development of the Multidisciplinary Design Optimization (MDO) technology, it will become the general trend of the optimization in the future. For a better use of MDO to solve ship design problem, this paper provided a survey and classification of the main MDO algorithms that have been present in recent literatures. A brief introduction was carried out for some developed MDO algorithms, but a more details of the application environment as well as performance features were present here for several developing ones. Finally, a discussion on the drawback and develop trend of MDO al-gorithm were performed, and several suggestions were given.【期刊名称】《舰船科学技术》【年(卷),期】2017(039)002【总页数】6页(P1-5,47)【关键词】多学科设计优化;多学科设计优化算法;算法分类【作者】杨磊;韦喜忠;赵峰;李胜忠【作者单位】中国船舶科学研究中心,江苏无锡 214082;中国船舶科学研究中心,江苏无锡 214082;中国船舶科学研究中心,江苏无锡 214082;中国船舶科学研究中心,江苏无锡 214082【正文语种】中文【中图分类】U662现代工程系统规模越来越大、系统之间的交互作用越来越精细、复杂，已很难应用传统优化方法，并通过经验来协调系统内部的耦合效应。

多学科设计优化（MDO）北京航空航天大学 720研究所，国家888/CIMS设计自动化工程实验室合作设计与优化研究小组韩明红编写激烈的竞争和全球性的制造已成为当今制造业的显著特征。

目前，大部分的设计过程经常面临着多学科的严峻挑战。

大家都知道，由最优组件或子系统组成的产品，并不一定是全局的最优产品。

产品创造的关键是平衡来自于消费者、市场、工程设计、制造、后勤、服务以及经济方面的无数的约束。

通过扩展企业人员、数据和软件的有效的协同是满足多学科挑战的必不可少的条件。

产品设计过程已经成为一个多目标优化决策过程，是一个多学科交叉综合设计的优化过程。

工程产品和工程系统甚至过程系统的设计，在概念设计阶段、详细设计阶段甚至是需求设计阶段，都面临着选择各种设计参数的匹配，以取得多学科设计的工程产品和系统的整体系统性能优化。

这是工程产品和系统设计的最终性能指标的要求。

多学科设计优化问题，例如：民用客运飞机的方案设计，可以看作是拉力、气动、重量和性能的多学科设计问题。

其中如环境（空气运动黏度、密度等）、任务需求（飞行范围、负载、起飞重量）、技术（安装推力、燃料油耗、速度）等都具有不确定性的随机变量。

多学科设计优化问题，既要寻求总体系统优化与子系统级优化最佳协同效果，又要考虑不确定性对设计过程（鲁棒性设计问题）和设计结果的（可靠性设计）的影响，还要考虑在能够提供一个比较通用的多学科设计优化方法情况下，许多学科专用的设计方法的应用集成问题，即灵活性、开放性问题。

多学科设计优化的理论和技术是发展复杂工程系统，如飞机、卫星、导弹、舰船、汽车等大型工程产品的技术理论与方法上的新的制高点和难点之一，是标志一个国家产品创新技术与理论的制高点之一，具有重大的战略前沿意义。

从学术上说，多学科设计优化是在传统设计方法、设计优化理论基础上的重要的质的发展。

它是设计方法、传统的机械设计知识、过程设计知识、现代信息技术交叉集成的大系统方法，可适应更大规模的工程产品、系统设计、发展的需要。

Multi-Objective Collaborative Optimization Based on Evolutionary AlgorithmsSu Ruiyi"Beijing System Design Institute ofElectromechanical Engineering,No. 31 Yongding Road, Haidian District,Beijing 100854, Chinae-mail: sry@Gui Liangjine-mail: gui@Fan Zijiee-mail: zjfan@State Key Laboratory of Automotive Safety and Energy, Department of Automotive Engineering,Tsinghua University,Beijing 100084, ChinaThis paper proposes a novel multi-objective collaborative optimi-zation (MOCO) approach based on multi-objective evolutionary algorithms for complex systems with multiple disciplines and objectives, especially for those systems in which most of the disci-plinary variables are shared. The shared variables will conflict when the disciplinary optimizers are implemented concurrently. In order to avoid the confliction, the shared variables are treated as fixed parameters at the discipline level in most of the MOCa approaches. But in this paper, a coordinator is introduced to handle the confliction, which allocates more design freedom and independence to the disciplinary optimizers. A numerical example is solved, and the results are discussed. [DOl: 10.1115/1.4004970]Keywords: multidisciplinary design optimization, multi-objective optimization, collaborative optimization1 IntroductionMultidisciplinary design optimization (MDO) was developed for large scale and complex engineering problems and has attracted much attention in recent years [1-3]. The two challenges of MDO are computational and organizational complexities [2]. The MDO problem involves large size of design variables, multiple objectives, interdisciplinary coupling, etc., which increase the computational expense. Moreover, the interdisciplinary coupling requests data transfer and decision interaction among different disciplinary codes and groups, which bring challenges to the organization of software modules and staffs. Several MDO approaches have been developed to deal with these challenges, such as concurrent subspace optimization [4], collaborative optimization (CO) [5], bi-level integrated system synthesis [6], and analytical target cascading [7].Collaborative optimization [5] is one of the most popular MDO approaches, which decomposes the complex engineering problem into multiple disciplines, components, or subsystems. Each subsystem can be optimized concurrently by a different subject expert group employing appropriate codes. The interaction among disciplinary analysis codes is described by an interdisciplinary compat- 'Corresponding author.Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 13, 2011; final manuscript received August 25, 2011; published online October 18, 2011. Assoc. Editor: Shapour Azarm.Journal of Mechanical Design ibility function. Meanwhile, a system level optirruzer is introduced to minimize the design objectives and ensure the interdisciplinary compatibility.One of the computational challenges in complex systems is raiseddue to multiple objectives. The typical CO approach can be readily used to solve multi-objective problems by applying an aggregate function to convert multiple objectives to a single objective. Forexample, Tappeta and Renaud [8] used the weighted sum method in the system level optimizer to handle multiple objectives. However, thedisadvantages of using the aggregate function in CO are that it cannotfind the Pareto optimal set in a single run and is unable to capture any Pareto solutions on the non convex part of the Pareto frontier [9].These difficulties can be overcome by introducing thepopulation-based multi-objective evolutionary algorithms (MOEAs) to the CO framework. This multi-objective collaborative optimization(MOCO) approach has been studied by Depince et al. [10], Aute andAzarm [11], and Li and Azarm [12]. In their approaches, the system objectives are optimized at the system level and each is alsodecomposed to be considered at the subsystem level, both system andsubsystem problems are solved by an MOEA. Their work shows that the combination of MOEAs and CO can obtain the Pareto optimalsolutions of multi-objective and multidisciplinary problemseffectively.However, for complex systems where most of the variables areshared and significant to more than one discipline, the previous approaches [1Q-12] have organizational and computational troubles,because the shared design variables are considered as fixedparameters at the subsystem level. For example, the window pillars of a bus body are sensitive to the rollover crash safety and significant tothe Noise, Vibration, and Harshness (NVH) performances. Both thecrash and NVH groups expect to design the pillars independently. However, this cannot be achieved as the pillar variables are treated asfixed parameters in the disciplines. As such, it brings troubles toorganization. Moreover, as the shared variables are fixed during the optimization, the design freedom of disciplinary groups is reduced: Ifmost of the disciplinary variables are shared, there would be littlefreedom at the subsystem level, which makes it difficult to find the feasible solutions. In this case, the disciplinary optimization ismeaningless and the MDO of the complex system will fail. This is thecomputational trouble.Both organizational and computational troubles, aforementioned,will be solved in this paper by proposing a novel MOCO framework, where the shared variables can be varied at the subsystem level. Thus,the disciplinary groups have the most design freedom to obtain thePareto optimal solutions effectively. In order to handle the difference of the shared variables from different disciplines, a coordinator (calledmiddle coordinator) is introduced. Consequently, the typical bi-levelCO framework is transformed to a tri-level framework, where the system level problem is solved by an MOEA, while both thesubsystem and middle level problems are solved by the sequentialquadratic programming (SQP) method.The remainder of this paper is organized as follows: Sec. 2describes the terminology of MDO problems; Sec. 3 gives the detailsof the proposed approach; Sec. 4 solves a numerical example and discusses the results; and Sec. 5 concludes the paper.2 TerminologyFigure 1 shows a fully coupled three-discipline nonhierarchic system, which was commonly used in the literature [6] and [8]. Eachbox annotated with Di is a discipline or subsystem, which calculatesthe outputs according to the inputs. For discipline i, the inputs include the design variable vector Xi and state variable vector Yji (j =I- i); theoutputs are composed by the objective vector fi, constraint vector gi and state variable vector Yij (i =I- j). The state variable vector Yu is calculated in discipline i and used in discipline j. The design variablesof discipline i comprise local variables Xli and shared variables Xshi' It isseen that both state variables and shared variables are interdisciplinary coupling factors in an MDO problem.Copyright © 2011 by ASME OCTOBER2011, Vol.133 / 104502-1。

技术响应与说明文件系统集成优化软件Isight技术说明书目录一ISIGHT介绍 (2)二系统目标 (2)三ISIGHT系统架构 (3)四ISIGHT功能 (3)五与招标文件相关技术要求的实质性响应 (4)1.功能要求 (4)1.1设计仿真流程集成 (4)1.2模型的可移植性 (5)1.3集成自编程序能力 (5)1.4知识的可重用性 (5)1.5 优化算法库（Optimization） (5)1.6 组合优化策略 (5)1.7工程数据挖掘（EDM） (7)1.8实验设计（Design of Experiment） (7)1.9近似模型设计 (7)1.10 强大的后处理能力 (7)1.11分布计算的能力 (8)2技术指标要求 (8)2.1 Isight可扩展性 (8)2.2通用接口及无缝集成接口 (8)2.3流程的搭建模式 (8)2.4优化算法嵌套功能 (9)2.5支持新型算法的构建 (9)2.6参数类型及算法并行 (9)2.7实验方案灵活更新 (9)2.8近似模型自动更新 (9)2.9二次开发能力 (10)2.10 Database Lookup功能 (10)2.11跨平台的能力 (10)2.12市场地位 (10)2.13软件可靠性及易用性 (10)六模块配置推荐 (11)一Isight介绍产品设计的数字化是企业信息化的重要内容。

当今的企业面对着激烈的竞争、苛求的客户、细分的市场、越来越复杂的产品、越来越短的产品生命周期、严格的法规和环境保护要求以及系统集成等等问题；同时，产品的复杂性也不断增长，涉及的学科领域也越来越宽，往往是结构、流体、电磁、动力等等学科交织在一起。

传统的产品设计方法已经很难满足企业当前生存和发展的需要，历史经验表明：性能是通过经验的不断积累而获得的。

各企业都希望其高技术、大容量的产品能够在继续保持或提高性能的同时，把成本降下来，如果引入基于计算机的多学科设计优化（MDO，Multidisciplinary Design Optimization）技术，无疑将进一步改善系统整体性能和产品质量。

structural and multidisciplinaryoptimization模板Structural and Multidisciplinary OptimizationStructural and multidisciplinary optimization (SMO) is a powerful technique used in engineering design to enhance the performance and efficiency of complex systems. It involves finding the optimal design parameters that satisfy various constraints while maximizing the system's performance.SMO utilizes a combination of mathematical algorithms, advanced computational tools, and engineering principles to automate the design process. By integrating different disciplines such as structural analysis, fluid dynamics, and material science, SMO allows engineers to address multiple variables simultaneously and optimize the overall system.The primary goal of SMO is to optimize the design of structures, components, or systems subject to various constraints. These constraints can include factors such as weight, stress, deflection, stability, manufacturability, and cost. By considering these constraints in the design process, engineers can ensure that the final product meets safety standards, performs efficiently, and minimizes waste.SMO employs optimization algorithms to iteratively modify the design parameters and evaluate their impact on the system performance. It utilizes numerical techniques to find the optimal solution within the given design space. These techniques may include evolutionary algorithms, gradient-based methods, or surrogate models that approximate the system behavior.The application of SMO is widespread in industries such as aerospace, automotive, civil engineering, and consumer goods. For example, in aerospace engineering, SMO can be used to optimize the shape and placement of aircraft wings to reduce drag and increase fuel efficiency. In automotive engineering, SMO can optimize the vehicle structure to enhance crashworthiness, fuel economy, and passenger comfort.In conclusion, structural and multidisciplinary optimization is a powerful approach that enables engineers to design efficient and high-performing systems. By integrating various disciplines and considering multiple design constraints, SMO allows engineers to find the optimal solution within the given design space. The application of SMO in different industries has revolutionized the design process and has led to significant advancements in product performance and efficiency.。

Multidisciplinary Design Optimization Multidisciplinary Design Optimization (MDO) is a critical process in engineering and design that involves the integration of multiple disciplines to optimize the performance of a system or product. It is a complex and challenging task that requires a deep understanding of various engineering principles, as well as the ability to effectively communicate and collaborate with experts from different fields. In this response, I will explore the importance of MDO from different perspectives, including its impact on product performance, the challenges and benefits of implementing MDO, and the role of collaboration and communication in successful MDO. From a technical perspective, MDO plays acrucial role in improving the performance of complex engineering systems. By considering multiple disciplines such as structural, thermal, and aerodynamic analysis, engineers can identify and address potential conflicts or trade-offs between different design objectives. For example, in the aerospace industry, MDO is used to optimize the design of aircraft components to minimize weight while maintaining structural integrity and aerodynamic efficiency. This holistic approach to design optimization allows engineers to achieve better overall performance and efficiency in their products. However, implementing MDO is not without its challenges. One of the main obstacles is the complexity of integrating different disciplines and their respective design constraints. Each discipline may have its own set of requirements and objectives, which can sometimes conflict with those of other disciplines. This requires a careful balance and trade-off analysis to ensure that the final design meets all the necessary criteria. Furthermore, MDO often requires advanced computational tools and simulation techniques, which can be time-consuming and resource-intensive to implement. Despite these challenges, the benefits of MDO are significant. By considering multiple disciplines simultaneously, engineers can identify opportunities for synergies and optimizations that would not be possible through traditional single-discipline design approaches. This can lead to significant improvements in product performance, reliability, and cost-effectiveness. In addition, MDO encourages a more holistic and integrated approach to design, which can lead to innovative solutions and a competitive advantage in the market. Another important aspect ofMDO is the role of collaboration and communication in its success. Effective MDO requires close collaboration between experts from different disciplines, as wellas clear communication and information sharing. This interdisciplinarycollaboration can be challenging, as it requires individuals with different backgrounds and expertise to work together towards a common goal. However, when done successfully, it can lead to a more comprehensive and robust design process that takes into account a wide range of factors and considerations. In conclusion, multidisciplinary design optimization is a critical process in engineering and design that has the potential to significantly improve the performance and efficiency of complex systems. While implementing MDO comes with its challenges, the benefits are substantial, and it can lead to innovative and competitive solutions. Collaboration and communication are key to the success of MDO, as they enable experts from different disciplines to work together towards a common goal. Overall, MDO is an essential tool for modern engineering and design, and its importance will only continue to grow in the future.。