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。     

一种改进的广义遗传算法及其在鲁棒优化问题中的应用

提出一种改进的广义遗传算法,算法中引入了异种机制以提高种群的多样性,在保证收敛速度的同时防止了早熟收敛。将该方法应用于复杂载荷作用下结构的鲁棒优化问题,并采用Taguchi望目特性的SN比构造了遗传算法的目标函数。数值算例表明,异种机制能够有效地提高广义遗传算法收敛于全局最优解的概率,加快收敛速度;结合了Taguchi鲁棒设计方法的广义遗传算法能够有效地求解复杂载荷作用下带有不确定参数的结构鲁棒优化问题。     

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.     

通用电机产品族协同设计的模糊优化

产品族设计是大规模定制中的核心内容,许多文献在假定产品特征确定情况下,采用平台参数和非平台参数,提出了大规模定制下的产品族设计优化方法。基于产品族设计理念,针对含有不确定特征的产品族设计中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数的情形,建立一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型,利用有关模糊算法对模糊条件下的通用电机产品族协同设计进行优化。     

柔性航天器饱和鲁棒正位置反馈振动控制研究

为了增强柔性航天器用正位置反馈(PPF)实现振动的主动控制的鲁棒性,在考虑作动器的控制输入限制的情况下,基于李亚普诺夫函数和线性矩阵不等式(LMI)方法设计了一种新型鲁棒PPF控制器.该控制器通过引入系统参数不确定项,将具有控制受限的优化问题转化为求解具有LMI约束的广义特征值问题,实现了闭环系统的稳定性.仿真结果表明了此种方法的有效性以及优点.     

机电产品体积最小化多学科协同设计建模

现代精密机电产品的设计制造技术正朝着产品体积越来越小,技术复杂程度越来越高,集成的功能单元越来越多,产品功能越来越强大的方向发展。基于这种趋势,本文在文献[1]研究基础上,结合多学科协同设计技术,采用二元推理循环反馈法,给出体积最小化设计原则的多学科优化理论及算法模型。     

不确定情景下应急物资储备库选址问题研究

针对在地震等自然灾害发生时受灾点以及应急需求均为不确定的情况,研究了灾前预置应急物资储备库的选址问题。通过设计多个需求情景来描述受灾点与应急需求的不确定性,建立了有最大运输距离限制的鲁棒优化模型,并设计了鲁棒优化方法。通过数值计算比较分析鲁棒优化方法和随机优化方法的计算结果,表明鲁棒优化解受不确定因素产生的偏差要比随机优化解小,鲁棒优化方法能够有效地减弱不确定性因素对选址方案的影响,并且能降低由预测偏差带来的风险。     

协同设计中基于DSM过程重构的研究

将设计结构矩阵方法应用到复杂产品的协同设计开发过程中，通过分析设计行为之间的信息交互获得DSM的量化表达，利用图论中的强连通分支问题算法识别耦合活动集，并在此基础上根据DSM重构原则对DSM进行模块化的过程重构，同时对块内设计任务进行聚类、撕裂，获得协同设计开发过程中的优化重组，为复杂产品的设计开发提供了有效的解决方法，并通过实例说明了该方法的实用性。     

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。     

基于紧约束鲁棒模型预测控制的无人车辆轨迹跟踪控制

针对无人驾驶车辆的轨迹跟踪问题,基于车辆非线性动力学模型,设计了一种紧约束鲁棒模型预测控制(MPC)方法。首先,利用魔术公式轮胎模型,结合车辆3自由度系统,构建了具有有界干扰的车辆线性离散误差模型;其次,采用紧约束控制策略设计系统的鲁棒优化问题;最后,通过在线优化得到最优控制序列,并离线构造之后多个时刻满足约束条件的可行控制序列。仿真结果表明,所提算法能够使车辆稳定快速跟踪上参考轨迹并有效提高系统计算资源利用率。     

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。     

微机电器件的稳健设计

微机电系统（MEMS）是一个新兴的跨学科研究领域,成本和可靠性是MEMS商品化的关键。与传统的机械加工和IC加工相比,MEMS加工的尺寸偏差比较大,而且很难控制,因此需要在设计过程中充分考虑加工的不确定性。稳健设计可以在不提高制造成本的前提下提高设计方案的稳健性。稳健优化设计方法主要包括 Taguchi方法和基于容差模型的方法,后者特别适合于处理带约束的优化设计问题。以微加速度计和微阀为例给出了稳健设计在MEMS设计中的应用,验证了稳健设计可以显著提高MEMS器件的信噪比。     

Uncertainty quantification using evidence theory in multidisciplinary design optimization

Advances in computational performance have led to the development of large-scale simulation tools for design. Systems generated using such simulation tools can fail in service if the uncertainty of the simulation tool's performance predictions is not accounted for. In this research an investigation of how uncertainty can be quantified in multidisciplinary systems analysis subject to epistemic uncertainty associated with the disciplinary design tools and input parameters is undertaken. Evidence theory is used to quantify uncertainty in terms of the uncertain measures of belief and plausibility. To illustrate the methodology, multidisciplinary analysis problems are introduced as an extension to the epistemic uncertainty challenge problems identified by Sandia National Laboratories.After uncertainty has been characterized mathematically the designer seeks the optimum design under uncertainty. The measures of uncertainty provided by evidence theory are discontinuous functions. Such non-smooth functions cannot be used in traditional gradient-based optimizers because the sensitivities of the uncertain measures are not properly defined. In this research surrogate models are used to represent the uncertain measures as continuous functions. A sequential approximate optimization approach is used to drive the optimization process. The methodology is illustrated in application to multidisciplinary example problems.     

A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.     

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。     

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.     

基于分布式计算的复合材料机翼优化设计

将遗传算法与高精度的通用有限元分析软件相结合, 并将其应用于复合材料机翼满足气动弹性要求的优化设计中。为了提高采用遗传算法的复合材料机翼优化设计的效率, 探讨了将分布式计算与遗传算法进行集成, 形成了基于分布式计算和遗传算法的复合材料机翼优化设计方法, 并应用该方法解决某大展弦比复合材料机翼副翼和舵面操纵反效问题。计算结果表明, 该方法可用于解决工程上复杂结构优化问题。      

Collaborative optimization with inverse reliability for multidisciplinary systems uncertainty analysis

This article introduces a method which combines the collaborative optimization framework and the inverse reliability strategy to assess the uncertainty encountered in the multidisciplinary design process. This method conducts the sub-system analysis and optimization concurrently and then improves the process of searching for the most probable point (MPP). It reduces the load of the system-level optimizer significantly. This advantage is specifically more prominent for large-scale engineering system design. Meanwhile, because the disciplinary analyses are treated as the equality constraints in the disciplinary optimization, the computation load can be further reduced. Examples are used to illustrate the accuracy and efficiency of the proposed method.     

基于云模型人工鱼群算法的模糊优化设计

云模型是通过熵来控制云滴的不确定度,并利用模糊集理论中的隶属函数来判断云滴的优劣,其正向云发生器可显著提高智能优化算法的全局寻优能力.为此,采用云模型中的正向云发生器来改进人工鱼群算法(称为云模型人工鱼群算法),并将其应用于机械零部件的模糊优化设计.首先,确定模糊综合评判所需的机械零部件的相关参数,并通过模糊综合评判确...