航空发动机多学科设计优化技术研究

多学科设计优化（MDO）是为实现复杂产品设计方法升级换代的最佳技术途径。针对先进航空发动 机设计面临的各学科间耦合关系复杂、指标冲突严重等困难,按零件、部件和总体方案设计３个阶段对MDO的 各项关键技术开展了研究、开发及应用工作,提出了基于MDO技术的航空发动机一体化设计方法。给出的５个 工程应用实例表明,该方法与传统设计方法相比,能大幅提高航空发动机设计水平,具有广泛的工程应用前景。     

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。     

复杂产品的多学科鲁棒协同设计优化方法研究

为了处理好复杂产品各子系统之间的耦合关系以及各子系统的异构性问题,以协同优化(CO)算法为基础,结合系统不确定分析(SUA)方法和近似不确定传播(IUP)方法,构建了多学科鲁棒协同设计优化算法框架.在设计变量的不确定性能够被概率分布函数描述的情况下,此算法框架能够解决复杂产品的设计优化问题.通过对梳齿式微加速度计的多学科鲁棒协同优化设计算例的计算,验证了此算法在输入参数存在微小扰动的情况下能够有效提高设计解的鲁棒性.     

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。     

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。     

某型飞机风挡及舱盖系统减重多学科设计优化方法研究

研究了某型飞机风挡及舱盖减重多学科设计优化的方法,优化过程中考虑风挡舱盖相关的传热、结构强度、屈曲和模态各学科之间的影响,建立了包括4个学科在内的风挡及舱盖系统多学科优化模型,通过改变透明件厚度达到减重的目的.采用本文提出的优化方法,使得风挡舱盖在满足约束条件的情况下,总重量减少了15.73%,各个设计变量收敛到最优值.本文提供了一种解决风挡及舱盖设计中学科耦合问题的方法,并为进一步研究飞机整机减重优化提供了一定的技术支持.     

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。     

复杂弹性耦合隔振系统建模及其优化设计

复杂弹性耦合隔振系统是在多层隔振系统中考虑中间筏体和基础的弹性而形成的一类隔振系统，提出了关于这类复杂隔振系统子结构建模的新方法，并用该方法建立了二维复杂弹性耦合隔振系统的动力学方程。并在此基础上进一步提出了一种新的基于小生境的自适应遗传优化算法，运用该算法对二维复杂弹性耦合隔振系统基于系统功率流传递的最优化设计。     

基于神经网络的产品包装部件注塑模具多学科优化设计

阐述了多学科优化过程的内涵和特点以及神经网络在产品包装部件注塑模具设计多学科优化中的应用.构建了基于神经网络的摩托车注塑件模具设计多学科优化的基本系统框架,并分配了模具设计多学科优化系统中神经网络算法的输入、输出以及隐层各神经元.     

复杂耦合动力系统振动响应的统计估计方法研究

复杂耦合动力系统是一种常见的工程力学系统,分析了复杂耦合动力系统振动响应的统计估计问题.首先通过动态系统的统计能量分析(SEA)方程,应用扰动法推导随机系统的能量平衡方程,进而推导复杂耦合系统响应的统计估计公式.在分析复杂耦合系统的响应统计估计时,重点分析各种形式激励对系统响应统计估计的影响以及载荷参数的确定方法.分析表明,相互独立的载荷作用是相关载荷作用时的一种特殊情况,它们可以应用统一的计算公式来表达.根据建立的响应统计估计方法,设计了相应的试验件,验证其正确性,试验结果表明,应用该推导得到的理论公式,系统响应的相对偏差能有效的减小,得到的能量平均值能够与试验值较好地吻合.     

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.     

Benchmarking multidisciplinary design optimization algorithms

A comparison of algorithms for multidisciplinary design optimization (MDO) is performed with the aid of a new software framework. This framework, pyMDO, was developed in Python and is shown to be an excellent platform for comparing the performance of the various MDO methods. pyMDO eliminates the need for reformulation when solving a given problem using different MDO methods: once a problem has been described, it can automatically be cast into any method. In addition, the modular design of pyMDO allows rapid development and benchmarking of new methods. Results generated from this study provide a strong foundation for identifying the performance trends of various methods with several types of problems.     

Riemannian optimization and multidisciplinary design optimization

Riemannian Optimization (RO) generalizes standard optimization methods from Euclidean spaces to Riemannian manifolds. Multidisciplinary Design Optimization (MDO) problems exist on Riemannian manifolds, and with the differential geometry framework which we have previously developed, we can now apply RO techniques to MDO. Here, we provide background theory and a literature review for RO and give the necessary formulae to implement the Steepest Descent Method (SDM), Newton’s Method (NM), and the Conjugate Gradient Method (CGM), in Riemannian form, on MDO problems. We then compare the performance of the Riemannian and Euclidean SDM, NM, and CGM algorithms on several test problems (including a satellite design problem from the MDO literature); we use a calculated step size, line search, and geodesic search in our comparisons. With the framework’s induced metric, the RO algorithms are generally not as effective as their Euclidean counterparts, and line search is consistently better than geodesic search. In our post-experimental analysis, we also show how the optimization trajectories for the Riemannian SDM and CGM relate to design coupling and thereby provide some explanation for the observed optimization behaviour. This work is only a first step in applying RO to MDO, however, and the use of quasi-Newton methods and different metrics should be explored in future research.     

Decomposition of multidisciplinary optimization problems: formulations and application to a simplified wing design

Several formulations for solving multidisciplinary design optimization (MDO) problems are presented and applied to a test case. Two bi-level hierarchical decomposition approaches are compared with two classical single-level approaches without decomposition of the optimization problem. A methodology to decompose MDO problems and a new formulation based on this decomposition are proposed. The problem considered here for validation of the different formulations involves the shape and structural optimization of a conceptual wing model. The efficiency of the design strategies are compared on the basis of optimization results.     

IMMUNE NETWORK SIMULATION WITH MULTIOBJECTIVE GENETIC ALGORITHMS FOR MULTIDISCIPLINARY DESIGN OPTIMIZATION

The paper presents a method called MOGA-INS for Multidisciplinary Design Optimization (MDO) of systems that involve multiple competing objectives with a mix of continuous and discrete variables. The method is based on the Immune Network Simulation ( INS) approach that has been extended by combining it with a Multi-Objective Genetic Algorithm ( MOGA). MOGA obtains Pareto solutions for multiple objective optimization problems in an all-at-once manner. INS provides a coordination strategy for subsystems in MDO to interact and is naturally suited for genetic algorithm-based optimization methods. The MOGA-INS method is demonstrated with a speed-reducer example, formulated as a two-level two-objective design optimization problem.     

Aerospace applications of optimization under uncertainty

The Multidisciplinary Optimization (MDO) Branch at NASA Langley Research Center develops new methods and investigates opportunities for applying optimization to aerospace vehicle design. This paper describes MDO Branch experiences with three applications of optimization under uncertainty: (1) improved impact dynamics for airframes, (2) transonic airfoil optimization for low drag, and (3) coupled aerodynamic and structures optimization of a 3-D wing. For each case, a brief overview of the problem and references to previous publications are provided. The three cases are aerospace examples of the challenges and opportunities presented by optimization under uncertainty. The present paper will illustrate a variety of needs for this technology, summarize promising methods, and uncover fruitful areas for new research.     

A Geodesign Method of Human-Energy-Water Interactive Systems for Urban Infrastructure Design: 10KM2 Near-Zero District Project in Shanghai

The grand challenges of climate change demand a new paradigm of urban design that takes the performance of urban systems into account, such as energy and water efficiency. Traditional urban design methods focus on the form-making process and lack performance dimensions. Geodesign is an emerging approach that emphasizes the links between systems thinking, digital technology, and geographic context. This paper presents the research results of the first phase of a larger research collaboration and proposes an extended geodesign method for a district-scale urban design to integrate systems of renewable energy production, energy consumption, and storm water management, as well as a measurement of human experiences in cities. The method incorporates geographic information system (GIS), parametric modeling techniques, and multidisciplinary design optimization (MDO) tools that enable collaborative design decision-making. The method is tested and refined in a test case with the objective of designing a near-zero-energy urban district. Our final method has three characteristics. ① Integrated geodesign and parametric design: It uses a parametric design approach to generate focal-scale district prototypes by means of a custom procedural algorithm, and applies geodesign to evaluate the performances of design proposals. ② A focus on design flow: It elaborates how to define problems, what information is selected, and what criteria are used in making design decisions. ③ Multi-objective optimization: The test case produces indicators from performance modeling and derives principles through a multi-objective computational experiment to inform how the design can be improved. This paper concludes with issues and next steps in modeling urban design and infrastructure systems based on MDO tools.     

Multidisciplinary design optimization based on independent subspaces

Optimization has been successfully applied to systems with a single discipline. Since many disciplines are involved in a coupled fashion in modern engineering, multidisciplinary design optimization (MDO) technology has been developed. MDO algorithms are designed to solve the coupled aspects generated from the interdisciplinary relationship. In a general MDO algorithm, a large design problem is decomposed into smaller ones which can be easily solved. Although various methods have been proposed for MDO, research is still in the early stage. This study proposes a new MDO method which is named MDO based on independent subspaces (MDOIS). Many real engineering problems consist of physically separate components and they can be independently designed. The inter‐relationship occurs through coupled physics. MDOIS is developed for such problems. In MDOIS, a large system is decomposed into small subsystems. The coupled aspects are solved via system analysis which solves the coupled physics. The algorithm is mathematically validated by showing that the solution satisfies the Karush–Kuhn–Tucker condition. Copyright © 2005 John Wiley & Sons, Ltd.