对协同优化及其响应面方法的研究

本文论述了复杂系统及多学科优化中的协同优化思想和数学模型,指出了协同优化中的一个突出问题,即学科不一致、实际运用中需要对其进行协调处理的局限性。论述了解决学科不一致问题的二次响应面近似方法思想及算法。通过一个实例,介绍了运用二次响应面近似法的方法,仿真了优化过程,验证了二次响应面方法在系统级优化上迭代次数较少,在学科优化上却相对要多的特征。     

考虑不确定性的多学科设计优化方法研究综述

目的研究多学科不确定性设计优化中多学科设计优化方法、不确定性建模与传递、不确定性设计优化的相关理论。方法通过研究并分析国内外相关文献,总结归纳考虑不确定性的多学科设计优化中的耦合系统解耦方法、参数和代理模型不确定性的建模方法,以及高效的不确定性传递和设计优化方法。结论系统探讨了在面对复杂多变的外界环境时,多学科设计优化对不确定性量化与传递的需求,提出多学科设计优化不仅要考虑确定性的系统,而且需要考虑由于外界环境变化导致的系统响应的不确定性。针对现有的多学科不确定性设计优化方法的理论研究,提出提高计算效率的关键在于将传统的三层嵌套循环计算框架解耦成单层循环。研究结果表明,考虑不确定性的多学科设计优化将成为复杂多学科系统设计的有力支撑,能显著提高系统的可靠性和稳健性,提高使用寿命,同时能够加快产品的更新换代设计。     

基于SysML的多学科设计建模与优化

目的 基于系统建模语言SysML,分析多学科设计建模与优化过程,在理解多学科设计与优化数学模型的基础上,构建系统设计优化模型。方法 通过分析多学科设计优化的数学模型,利用SysML语言对多学科优化对象模型进行元模型表征,将生成的SysML模型进行模型转化,转换成XML格式以便优化求解器进行求解。结论 提出了一种用于多学科设计建模与优化的SysML扩展优化建模方法。通过SysML系统建模语言的扩展版型,添加多学科优化相关的优化目标、优化约束、优化变量等优化元素的模型内容。提出了SysML优化信息的提取方法,以XML为中间格式,将提取的优化模型与优化求解器进行集成。通过系统设计与系统优化的集成求解为产品系统架构设计人员提供有效的决策支撑。     

多学科设计优化研究及发展趋势分析

多学科设计优化技术属于当前复杂系统设计研究中最新、最活跃的领域,受到越来越广泛的重视。在阐述多学科设计优化技术的定义与内涵的基础上,分析多学科设计优化技术的研究内容,综述了国外的研究现状并对国内外发展状况进行了对比分析,指出了目前多学科设计优化技术研究中存在的问题以及今后的主要发展趋势。     

复杂系统的多学科设计优化综述

从设计和分析的本质出发,结合复杂系统的特点,通过分析传统设计优化流程在面对复杂系统时存在的困难和缺陷,指出多学科设计优化(multidisciplinary design optimization,MDO)方法是解决复杂系统设计优化问题的一种有效措施.在此基础上,介绍了多学科优化方法的基本思想,总结了子系统耦合方式及MDO在处理耦合时的基本方法,归纳了MDO的知识框架和主要研究内容.最后在现有研究成果的基础上,对MDO今后的研究提出了几点参考意见.     

多学科设计优化的数值算法及其组合算法比较的研究

针对多学科设计优化的数值算法比较研究上存在的不足,提出了算法在进行优化时所需的时间、解决问题个数及选用目标函数的相对精度等三项评估标准相结合的三维算法比较方法,首次将精度作为算法比较的一个重要指标,由此得到的算法比较三维模型,为算法选择提供了更加合理的理论依据.在理论研究的基础上,对组合算法和数值算法进行了比较,突破了传统算法比较局限在数值算法的不足.结果表明,在时间变化不大的情况下,组合算法的精度比单纯的数值算法有大幅度的提高,为工程应用提供了更全面的支持.在此基础上给出了数值算法及其组合的算法选择流程.最后,通过手机的多学科设计优化实例,验证了所提出的算法选择流程的合理性和可行性.     

卷弧翼火箭弹的参数化建模方法

针对多学科设计优化的需求,综述了几种常用的参数化建模方法.对卷弧翼火箭弹参数化建模的基本原理和实现步骤进行了详细说明.利用UGNX提供的API开发了参数化建模的内部和外部程序,可为空气动力学科计算提供几何模型和为弹道学科计算提供弹体标志量信息.     

多学科集成下的老年智能产品设计研究

目的 基于多学科集成理论,分析老年智能产品设计现状,在了解老年用户群体对产品需求的基础上,进行设计实践创新方法研究。方法 通过阐明多学科集成方法中系统化、框架化、协同化、优化算法等理论,针对使用者、设计者双方进行分析,寻找出产品设计过程中存在的问题,在服务设计原则和多学科集成的理论支持下,进而推导出设计思路和方法。 结论 提出老年智能产品设计的基础是用户的操作体验和特定需求,设计过程涉及多学科、多目标;以“多目标实现”“多学科综合系统模型”“新技术融合”等应用实例,解释了如何解决产品设计过程中,由于用户需求复杂所产生的计算复杂性和选择复杂性等问题,优化了设计框架,归纳了设计信息,提升了设计过程的合理性、高效性和准确性。     

桁架结构布局优化的并行子空间方法

由于结构布局优化存在设计变量类型众多和变量耦合等问题,采取合适的优化方法获得满足结构设计要求的最小质量的结构具有重要的工程意义.基于多学科设计优化方法中的并行子空间优化法,提出一种桁架结构布局优化的并行子空间优化方法.将结构布局设计问题按设计变量类型分为布局、形状和尺寸三个并行的子空间,设计变量在各自的子空间内单独优化,各子空间优化结束后,在系统级中协调3类设计变量,保持最小质量的子空间的优化设计变量不变,采用近似一维搜索的方法协调其他子空间的设计变量,然后进行下一次迭代直至收敛.2个算例表明该方法能够取得较好的优化结果,具有实际工程应用价值.     

复杂工业产品多学科设计仿真优化平台（UniXDE）研究与应用

针对国内外已有的复杂工业产品多学科设计仿真优化框架与平台在多学科模型集成能力、计算能力、协同能力方面的不足,研究和开发国产自主的新一代复杂工业产品多学科设计仿真优化框架与平台UniXDE (unified exploration and design environment)。本平台基于微服务云架构技术构建整体框架,提供低代码仿真优化流程编排、组件化CAD/CAE参数化集成接口、丰富的多学科设计优化算法库、分布式高性能优化计算引擎、可视化计算监控和报告自动生成等功能。通过白车身轻量化、船型优化、飞行器起落架性能优化等工程应用,表明UniXDE可显著提升产品综合性能和设计成功率。     

基于响应面法的地坑式架车机轻量化研究

传统架车机设计方法往往趋于保守,为了充分发挥地坑式架车机材料的承载性能,将响应面近似模型法与优化技术相结合,提出一种以地坑式架车机结构自重为优化目标的优化方法.该方法基于多学科优化软件ISIGHT和ANSYS,通过最优拉丁超立方试验设计方法分析设计变量对响应的灵敏度,获得最终设计变量,并建立响应面近似模型,利用多岛遗传算法对近似模型进行优化.结果表明,该方法能极大提高优化效率,同时使地坑式架车机结构自重降低20.64%,轻量化效果显著.研究结果可为地坑式架车机的设计提供理论指导.     

基于飞机客舱布局及设施造型的PSS设计

目的为飞机客舱布局及设施造型PSS设计提供一种可行的研究思路与方法。方法以宽体客机乘务员休息区域布局及设施造型案例为研究对象,进行实例验证研究。结论客舱设施造型的PSS设计不再是单一解决形态、色彩、材质、结构等基本要素问题,无形的服务质量也作为设计的重要因素,解决PSS中使用者的安全、高效、舒适、美观以及人机匹配最优化的问题。     

Reliability-based design optimization with frequency constraints using a new safest point approach

In the reliability-based design optimization (RBDO) model, the mean values of uncertain variables are usually applied as design variables, and the cost is optimized subject to prescribed probabilistic constraints as defined by a nonlinear mathematical programming problem. Therefore, an RBDO solution that reduces the structural weight in non-critical regions provides not only an improved design, but also a higher level of confidence in the design. Solving such nested optimization problems is extremely expensive for large-scale multidisciplinary systems that are likewise computationally intensive. This article focuses on the study of a particular problem representing the failure mode of structural vibration analysis. A new method is proposed, called safest point, that can efficiently give the reliability-based optimum solution under frequency constraints, and then several probability distributions are developed, which are mathematically nonlinear functions, for the proposed method. Finally, the efficiency of the extended approach is demonstrated for probability distributions such as log-normal and uniform distributions, and its applicability to the design of structures undergoing fluid–structure interaction phenomena, especially the design process of aeroelastic structures, is also demonstrated.     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

Decoupled approach to multidisciplinary design optimization under uncertainty

We propose solution methods for multidisciplinary design optimization (MDO) under uncertainty. This is a class of stochastic optimization problems that engineers are often faced with in a realistic design process of complex systems. Our approach integrates solution methods for reliability-based design optimization (RBDO) with solution methods for deterministic MDO problems. The integration is enabled by the use of a deterministic equivalent formulation and the first order Taylor’s approximation in these RBDO methods. We discuss three specific combinations: the RBDO methods with the multidisciplinary feasibility method, the all-at-once method, and the individual disciplinary feasibility method. Numerical examples are provided to demonstrate the procedure. Anukal Chiralaksanakul is currently a full-time lecturer in the Graduate School of Business Administration at National Institute of Development Administration (NIDA), Bangkok, Thailand.     

AESOP—a numerical platform for aerodynamic shape optimization

Aerodynamic shape optimization based on Computational Fluid Dynamics can automatically improve the design of aircraft components. In order to obtain the best computational efficiency, the adjoint method is applied on the complete mapping, from the parameters of design to the evaluation of the cost function or constraints. The mapping considered here includes the parameterization, the mesh deformation, the primal-to-dual mesh transformation and the flow equations solved by the unstructured flow solver Edge distributed by FOI. The numerical platform AESOP integrates the flow and adjoint flow solver, mesh deformation schemes, algorithms of shape parameterization and algorithms for gradient-based optimization. The result is a portable and efficient implementation for large scale aerodynamic shape optimization and future applications in multidisciplinary shape optimization. The structure of the program is outlined and examples of applications are presented. The method of shape parameterization using Radial Basis Functions is discussed in more details because it is expected to play a major role in the development of multidisciplinary optimization.     

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

In power and energy systems, both the aerodynamic performance and the structure reliability of turbine equipment are affected by utilized blades. In general, the design process of blade is high dimensional and nonlinear. Different coupled disciplines are also involved during this process. Moreover, unavoidable uncertainties are transported and accumulated between these coupled disciplines, which may cause turbine equipment to be unsafe. In this study, a saddlepoint approximation reliability analysis method is introduced and combined with collaborative optimization method to address the above challenge. During the above reliability analysis and design optimization process, surrogate models are utilized to alleviate the computational burden for uncertainties‐based multidisciplinary design and optimization problems. Smooth response surfaces of the performance of turbine blades are constructed instead of expensively time‐consuming simulations. A turbine blade design problem is solved here to validate the effectiveness and show the utilization of the given approach.