元模型的复杂产品多学科信息建模方法

为了有效地支持复杂多学科产品设计中的信息共享、交互以及多学科模型之间的动态转换,结合元模型的建模思想,提出一种基于元模型的复杂产品信息建模方法.根据复杂多学科产品设计过程的特点,采用设计元模型对多学科产品设计过程信息进行抽象表达,并建立设计元模型和学科元模型之间的映射关系,实现了基于元模型的学科模型的动态转化.最后以某机型机翼的多学科信息建模为例,说明了该方法在复杂产品多学科信息建模中的应用过程.     

支持多学科设计优化的集成产品过程建模方法

针对当前主要的设计过程建模方法缺乏表达复杂产品多学科设计过程中资源的组织调用和协作方式等信息,提出一种支持复杂产品多学科设计优化的设计路线图框架过程建模方法.从全面表达设计过程信息的角度出发,描述产品多学科设计优化过程中的主要活动及其协同关系,建立支持多学科设计优化的过程模型;在此基础上,给出了多学科设计优化的过程规划方法,以降低产品设计过程中的迭代,通过构建支持多学科设计优化的集成产品设计过程结构框架,实现产品多学科设计优化的过程集成.最后通过已开发的多学科系统集成平台,应用具体设计实例验证了整套方法的有效性.     

并行子空间优化在无人机总体设计中的应用

飞机设计是一个多学科的复杂的系统工程,各个学科通常相互影响、相互耦合.这使得飞机设计过程日趋复杂,设计周期越来越长,开发成本越来越高,而并行子空间优化(CSSO)是解决这些问题的一种有效方法.文中对基于神经网络响应面的并行子空间优化算法及其在无人机总体方案设计优化中的应用进行了研究.并行子空间优化算法将多学科耦合的无人机设计优化问题分解为不同的子空间问题,在不同的子空间中建立各自的神经网络响应面,通过响应面完成各子空间之间的数据交换与协调,以此来逼近设计空间最优解.应用结果表明,CSSO算法能有效地应用于无人机总体方案优化设计.     

多学科流程集成及实验设计系统研究与实现

复杂工程系统的设计是多学科交叉综合设计优化决策过程,针对这个过程,设计并开发了基于组件的多学科流程集成与实验设计系统,为复杂工程系统方案设计和仿真试验提供了支撑。与现有多学科系统相比,本系统具有的特点或优势有:1)摒弃项目管理的方式,采用流程与实验设计混合模型存储,方便模型的管理;2)采用多线程技术,实现了流程的组件并行调度和实验设计各实例的并行调度;3)采用动态任务调度技术,实现了实验设计调度过程的自动调度与交互控制相融合的灵活控制方式。     

基于OpenMP/MPI并行编程模型的N体问题的优化实现

多核集群的层次化并行编程模型一直是高性能计算的研究热点。以SMP集群为例,从硬件上可分为节点间和节点内的两层架构。阐述了层次化并行编程的实现技术,针对N体问题算法进行了基于Hybrid并行编程模型的并行化研究。提出了一种块同步MPI/Open MP细粒度N体问题的优化算法。基于曙光TC5000A集群,将该算法与传统的N体并行算法进行了执行时间与加速比的比较,得出了几句总结性具体论述。     

多学科混合变量协同设计优化方法研究

为解决同时含有离散和连续两种变量形式的混合变量复杂产品设计优化问题,利用“分而治之”的混合参数处理思想,在协同设计优化算法的基础上,提出一种多学科混合变量协同设计优化方法．该方法先将优化问题解耦分解成相对简单的多个子系统进行优化计算,然后利用协同设计优化算法的协同机制求得全系统最优解．算例验证结果表明了所提出方法的可行性和有效性．     

基于时间Petri网的并行测试任务过程建模及验证技术研究

为满足复杂装备中某些关键系统的实时测试与诊断需求,在测试性设计过程中开展并行测试调度优化研究;针对测试任务之间关联关系复杂的实际情况,通过建立基于图染色理论的测试任务关系模型,实现了对系统资源冲突、死锁等问题的形式化描述,将并行测试调度优化转化为求解图的色数问题;并在求解图的色数问题过程中,利用改进的遗传算法逐步求解图的最大独立集,即可并行测试的测试任务子集,从而获得并行测试调度方案;最后,利用某实例对文章提出的并行测试任务调度建模及优化方法进行了验证,优化效果明显,证实了该方法对解决并行测试调度优化问题的有效性。     

一种基于自适应代理模型的并行贝叶斯优化方法

提出一种基于自适应代理模型的并行贝叶斯优化方法,用于求解计算成本高的复杂优化问题.该方法基于多点期望改进判据,通过批次采样实现并行优化.针对并行优化产生的大量历史数据会导致全局代理模型建模成本高的问题,提出一种改进的基于数据并行的高斯过程建模方法,在线构造局部代理模型.此外,针对多点期望改进判据计算成本高的问题,提出一种启发式的分层优化策略,通过序贯优化基于自适应代理模型的单点期望改进判据,近似计算多点期望改进判据.最后通过5个测试问题验证所提出方法的有效性.     

面向分级设计优化的飞行器参数化建模方法

针对飞行器气动隐身外形综合设计优化问题,提出合适的面向分级设计优化流程,建立适应该流程的渐进分层参数化建模方法;用基于敏度分析的参数影响程度分析方法筛选复杂设计变量;采用多学科设计优化（Multidisplinary Design Optimization,MDO）理论和差分进化算法进行飞行器气动隐身外形的综合设计优化.将该方法用于某飞行器外形设计优化,结果表明：该方法合理可行,可为飞行器外形多学科设计优化提供一定参考.     

基于LLVM Pass的复杂嵌套循环自动并行化框架

随着多核处理器的普及应用,针对嵌入式遗留系统中串行代码的自动并行化方法是研究热点.其中,针对具有非完美嵌套结构、非仿射依赖关系特征的复杂嵌套循环的自动并行化方法存在技术挑战.提出了一种基于LLVMPass的复杂嵌套循环的自动并行化框架(CNLPF).首先,提出了一种复杂嵌套循环的表示模型,即循环结构树,并将嵌套循环的正则区域自动转换为循环结构树表示;然后,对循环结构树进行数据依赖分析,构建循环内和循环间的依赖关系;最后,基于OpenMP共享内存的编程模型生成并行的循环程序.针对SPEC2006数据集中包含近500个复杂嵌套循环的6个程序案例,分别对其进行复杂嵌套循环占比统计和并行性能加速测试.结果表明,提出的自动并行化框架可以处理LLVMPolly无法优化的复杂嵌套循环,增强了LLVM的并行编译优化能力,且该方法结合Polly的组合优化,比单独采用Polly优化的加速效果提升了9%-43%.     

Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data

Uncertainty-based multidisciplinary design optimization (UMDO) has been widely acknowledged as an advanced methodology to address competing objectives and reliable constraints of complex systems by coupling relationship of disciplines involved in the system. UMDO process consists of three parts. Two parts are to define the system with uncertainty and to formulate the design optimization problem. The third part is to quantitatively analyze the uncertainty of the system output considering the uncertainty propagation in the multidiscipline analysis. One of the major issues in the UMDO research is that the uncertainty propagation makes uncertainty analysis difficult in the complex system. The conventional methods are based on the parametric approach could possibly cause the error when the parametric approach has ill-estimated distribution because data is often insufficient or limited. Therefore, it is required to develop a nonparametric approach to directly use data. In this work, the nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data is proposed. To handle limited data, three processes are also adopted. To verify the performance of the proposed method, mathematical and engineering examples are illustrated.     

Optimal design of reconfigurable parallel machining systems

A reconfigurable machining system is usually a modularized system, and its configuration design concerns the selections of modules and the determination of geometric dimensions in some specific modules. All of its design perspectives from kinematics, dynamics, and control have to be taken into considerations simultaneously, and a multidisciplinary design optimization (MDO) tool is required to support the configuration design process. This paper presents a new MDO tool for reconfigurable machining systems, and it includes the following works: (i) the literatures on the computer-aided design of reconfigurable parallel machining systems have been reviewed with a conclusion that the multidisciplinary design optimization is essential, but no comprehensive design tool is available to reconfigurable parallel machining systems; (ii) a class of reconfigurable systems called reconfigurable tripod-based machining system has been introduced, its reconfiguration problem is identified, and the corresponding design criteria have been discussed; (iii) design analysis in all of the disciplines including kinematics, dynamics, and control have been taken into considerations, and design models have been developed to evaluate various design candidates; in particular, the innovative solutions to direct kinematics, stiffness analysis for the design configurations of tripod-based machines with a passive leg, and concise dynamic modelling have been provided; and (iv) A design optimization approach is proposed to determine the best solution from all possible configurations. Based on the works presented in this paper, a computer-aided design and control tool have been implemented to support the system reconfiguration design and control processes. Some issues relevant to the practical implementation have also been discussed.     

卫星有效载荷的多目标多学科设计优化研究

在卫星有效载荷系统研究中,实施多目标多学科优化的可行性设计。首先,分析了开展卫星有效载荷多学科设计优化的关键技术。建立了包含天线、转发器、数据传输、可靠性、成本和质量的多学科分析模型。然后,应用多目标遗传算法对某卫星有效载荷的可靠性和成本进行多目标设计优化,获得最优解集。最后,运用多学科协同优化结合遗传算法进行可靠性单目标设计优化。研究结果表明:有效载荷的多目标多学科设计优化全面考虑了多个学科之间的关系,设计人员可按需选择其满意的优化结果,大幅提高设计效率;协同优化方法有助于实现学科自治、并行设计,提高设计的灵活性和缩短设计周期。     

A digital twin-based multidisciplinary collaborative design approach for complex engineering product development

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.     

LOcal Uncertainty Processing (LOUP) method for multidisciplinary robust design optimization

In this paper, we develop an easy-to-implement approximate method to take uncertainties into account during a multidisciplinary optimization. Multidisciplinary robust design usually involves setting up a full uncertainty propagation within the system, requiring major modifications in every discipline and on the shared variables. Uncertainty propagation is an expensive process, but robust solutions can be obtained more easily when the disciplines affected by uncertainties have a significant effect on the objectives of the problem. A heuristic method based on local uncertainty processing (LOUP) is presented here, allowing approximate solving of specific robust optimization problems with minor changes in the initial multidisciplinary system. Uncertainty is processed within the disciplines that it impacts directly, without propagation to the other disciplines. A criterion to verify a posteriori the applicability of the method to a given multidisciplinary system is provided. The LOUP method is applied to an aircraft preliminary design industrial test case, in which it allowed to obtain robust designs whose performance is more stable than the one of deterministic solutions, relatively to uncertain parameter variations.     

An efficient metamodel-based multi-objective multidisciplinary design optimization framework

This paper presents an efficient metamodel-based multi-objective multidisciplinary design optimization (MDO) architecture for solving multi-objective high fidelity MDO problems. One of the important features of the proposed method is the development of an efficient surrogate model-based multi-objective particle swarm optimization (EMOPSO) algorithm, which is integrated with a computationally efficient metamodel-based MDO architecture. The proposed EMOPSO algorithm is based on sorted Pareto front crowding distance, utilizing star topology. In addition, a constraint-handling mechanism in non-domination appointment and fuzzy logic is also introduced to overcome feasibility complexity and rapid identification of optimum design point on the Pareto front. The proposed algorithm is implemented on a metamodel-based collaborative optimization architecture. The proposed method is evaluated and compared with existing multi-objective optimization algorithms such as multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II), using a number of well-known benchmark problems. One of the important results observed is that the proposed EMOPSO algorithm provides high diversity with fast convergence speed as compared to other algorithms. The proposed method is also applied to a multi-objective collaborative optimization of unmanned aerial vehicle wing based on high fidelity models involving structures and aerodynamics disciplines. The results obtained show that the proposed method provides an effective way of solving multi-objective multidisciplinary design optimization problem using high fidelity models.     

Multidisciplinary design optimisation and robust design approaches applied to concurrent design

The increasing economic competition of all industrial markets and growing complexity of engineering problems lead to a progressive specialisation and distribution of expertise, tools and work sites. Most industrial sectors manage this fragmentation using the concurrent engineering approach, which is based on tools integration and shared databases and requires significant investments in design and work organisation. Besides, the multidisciplinary design optimisation (MDO) is more and more used as a method for optimal solutions search with regard to multiple coupled disciplines. The paper describes a quite innovative multidisciplinary optimisation method based on robust design techniques: MORDACE (multidisciplinary optimisation and robust design approaches applied to concurrent engineering). Managing uncertainty due to design teams collaboration, our automatic optimisation strategy allows concurrently designing different aspects or parts of a complex product. The method assures effective design work distribution and high optimisation results, containing the CPU time. In addition, our strategy is suited to the early stages of the design cycle, where evolutions of design goals and constraints are possible and exhaustive information about the design space is necessary. A roll stabiliser fin optimisation is presented as an example of this method applied to an industrial design problem.     

A solution process for simulation-based multiobjective design optimization with an application in the paper industry

In this paper, we address some computational challenges arising in complex simulation-based design optimization problems. High computational cost, black-box formulation and stochasticity are some of the challenges related to optimization of design problems involving the simulation of complex mathematical models. Solving becomes even more challenging in case of multiple conflicting objectives that must be optimized simultaneously. In such cases, application of multiobjective optimization methods is necessary in order to gain an understanding of which design offers the best possible trade-off. We apply a three-stage solution process to meet the challenges mentioned above. As our case study, we consider the integrated design and control problem in paper mill design where the aim is to decrease the investment cost and enhance the quality of paper on the design level and, at the same time, guarantee the smooth performance of the production system on the operational level. In the first stage of the three-stage solution process, a set of solutions involving different trade-offs is generated with a method suited for computationally expensive multiobjective optimization problems using parallel computing. Then, based on the generated solutions an approximation method is applied to create a computationally inexpensive surrogate problem for the design problem and the surrogate problem is solved in the second stage with an interactive multiobjective optimization method. This stage involves a decision maker and her/his preferences to find the most preferred solution to the surrogate problem. In the third stage, the solution best corresponding that of stage two is found for the original problem.     

Application of genetic algorithms to conceptual design of a micro-air vehicle

This paper investigates the feasibility of automating the conceptual design of a micro-air vehicle on a personal computer system. The proposed design methodology adopts the use of genetic algorithms as the search engine in the design process. The multidisciplinary optimization problem here is to maximize the lift-to-drag ratio subjected to static longitudinal stability, performance and physical constraints. The six design parameters chosen are angle of attack, main wing twist angle, winglet span, main wing chord length, main wing taper ratio and winglet taper ratio. A case study has been carried out to compare the performance of using genetic algorithms with well-established non-linear optimization method based on sequential quadratic programming.