耦合系统协同进化多学科设计优化算法研究

针对耦合系统的优化问题,为提高系统的自适应性,提出了协同进化多学科设计优化算法.算法受生态系统内生活在同一地域物种间既有竞争又有合作的协同进化关系的启发,将协同进化算法的分解与协作思想和MDO方法的分解与协同思想相结合,在域值分解的基础上将复杂耦合系统分解成学科间相对独立并保持自治的多学科系统.隐含迭代策略协调学科间耦合约束的一致性.建立了多个优化过程模型.并提出的算法应用于多学科耦合系统进行测试,优化结果与MDF、IDF和AAO三种单级优化方法的优化结果进行比较,显示有较强的搜索能力以及较快的收敛速度和自适应性.     

微机械陀螺的多学科设计优化建模的研究

以梳状音叉式振动微机械陀螺为例,将多学科设计优化（MDO）方法应用到微机械陀螺的优化设计中。将微机械陀螺复杂系统分解为归属不同学科的多个子系统,阐明了在多学科设计优化中各子系统之间的相互关系。建立了微机械陀螺的结构设计、机械性能、电学性能子系统和系统级的多学科设计优化模型。用已研制的MMCDO多学科混合协同设计优化算法计算,得到了满意的优化设计结果。     

基于近似技术的导弹总体设计优化系统研究

实施多学科设计优化方法有助于提高导弹总体设计水平,但同时也使总体优化问题变得更加复杂、计算更加困难,因此需要采用近似技术。本文在对设计空间探索方法和MDO中应用的近似技术进行研究的基础上,为了在导弹总体设计优化系统中实现快速寻优,提出采用综合探索近似寻优方法,并通过实例验证了综合探索近似寻优方法的可行性。     

协同优化方法算例研究

多学科设计优化(MDO)技术因其能有效地解决大规模复杂工程系统的设计问题,得到了广泛地应用c基于ModelCenter软件平台通过对算例的研究,分析了协同优化(CO)方法的特点以及影响优化结果的原因.     

基于Isight的BLISS算法框架

介绍多学科优化设计(Multidisciplinary Design Optimization,MDO)算法的核心和应用价值,概述BLISS算法的总体框架.以2个相互耦合系统的方程组为算例,利用Isight搭建BLISS算法流程并进行求解.计算结果表明:BLISS算法的优化效果良好,收敛速度快,因此BLISS是很有价值的MDO算法.     

一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。     

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.     

新型多学科设计优化算法研究

研究多学科系统优化问题,针对工程系统的复杂性,通过多学科设计优化算法框架协同优化算法的研究,根据现有协同优化算法由于系统级一致性约束的存在计算量大,容易发散等缺点,提出了一种新的多学科设计优化算法框架.算法通过对设计变量的重新分配,简化了耦合变量的解耦过程,从而达到简化优化过程的目的,解决了协同算法的上述缺点.采用iSIGHT软件,对具体算例进行仿真,并与协同优化算法进行对比,验证了该算法的有效性.     

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．     

吸气式高超声速飞行器多学科优化设计研究

在冲压发动机推进特性问题的研究中,高超声速飞行器是一种多学科强耦合的先进飞行器,传统的设计方法一般只考虑某一个性能和学科,造成设计性能不理想,而多学科优化设计(MDO)能够探索和充分利用工程系统中的协同机制来实现复杂飞行器的设计.为优化推进技术,完善设计,提高航程,用多学科优化设计方法对高超声速飞行器进行了优化设计.建立了包括空气动力学、推进系统、结构质量以及弹道航程等多个学科模型在内的多学科优化平台.进行仿真,结果表明满足各个学科约束的条件,使得飞行器的航程提高 12.94%.同时也说明文中针对高超声速飞行器搭建的多学科优化平台是可行的,为优化设计提供厂保证.     

An efficient strategy for reliability-based multidisciplinary design optimization using BLISS

This paper presents an efficient reliability-based multidisciplinary design optimization (RBMDO) strategy. The conventional RBMDO has tri-level loops: the first level is an optimization in the deterministic space, the second one is a reliability analysis in the probabilistic space, and the third one is the multidisciplinary analysis. Since it is computationally inefficient when high-fidelity simulation methods are involved, an efficient strategy is proposed. The strategy [named probabilistic bi-level integrated system synthesis (ProBLISS)] utilizes a single-level reliability-based design optimization (RBDO) approach, in which the reliability analysis and optimization are conducted in a sequential manner by approximating limit state functions. The single-level RBDO is associated with the BLISS formulation to solve RBMDO problems. Since both the single-level RBDO and BLISS are mainly driven by approximate models, the accuracy of models can be a critical issue for convergence. The convergence of the strategy is guaranteed by employing the trust region–sequential quadratic programming framework, which validates approximation models in the trust region radius. Two multidisciplinary problems are tested to verify the strategy. ProBLISS significantly reduces the computational cost and shows stable convergence while maintaining accuracy.     

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.     

Metamodel-based collaborative optimization framework

This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.     

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 collaborative optimization using fuzzy satisfaction degree and fuzzy sufficiency degree model

Collaborative optimization (CO) is a bi-level multidisciplinary design optimization (MDO) method for large-scale and distributed-analysis engineering design problems. Its architecture consists of optimization at both the system-level and autonomous discipline levels. The system-level optimization maintains the compatibility among coupled subsystems. In many engineering design applications, there are uncertainties associated with optimization models. These will cause the design objective and constraints, such as weight, price and volume, and their boundaries, to be fuzzy sets. In addition the multiple design objectives are generally not independent of each other, that makes the decision-making become complicated in the presence of conflicting objectives. The above factors considerably increase the modeling and computational difficulties in CO. To relieve the aforementioned difficulties, this paper proposes a new method that uses a fuzzy satisfaction degree model and a fuzzy sufficiency degree model in optimization at both the system level and the discipline level. In addition, two fuzzy multi-objective collaborative optimization strategies (Max–Min and α-cut method) are introduced. The former constructs the sufficiency degree for constraints and the satisfaction degree for design objectives in each discipline respectively, and adopts the Weighted Max–Min method to maximize an aggregation of them. The acceptable level is set up as the shared design variable between disciplines, and is maximized at the system level. In the second strategy, the decision-making space of the constraints is distributed in each discipline independently through the allocation of the levels of α. At the system level, the overall satisfaction degree for all disciplines is finally maximized. The illustrative mathematical example and engineering design problem are provided to demonstrate the feasibility of the proposed methods.     

Multidisciplinary hybrid hierarchical collaborative optimization of electric wheel vehicle chassis integrated system based on driver’s feel

The optimization design of chassis integrated system mainly involves steering, suspension and brake subsystems, which is essentially a multidisciplinary design optimization. This paper mainly researches the multidisciplinary optimization of the chassis integrated system for the electric wheel vehicle, from the view of ensuring a favorable feel for the driver. The dynamic models of differential steering system, brake system, active suspension system and vehicle are established. Then, taking the coupling relationship of the chassis subsystems into account, this paper proposes an evaluating index of driver’s ride comfort (Drc), which is composed of the steering road feel, brake feel and suspension ride comfort. In order to determine the weight coefficient in the quantization formula of Drc, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to overcome the subjectivity in the selection. Based on these, a multidisciplinary hybrid hierarchical collaborative optimization (HHCO) method is proposed on the basis of the collaborative optimization (CO), which consists of a system level coordinator and a coupling analyzer to solve the problem of poor convergence and the low efficiency of CO method. The optimization results show that the proposed HHCO method has excellent computational efficiency and better convergence compared with the CO method, which can further improve the steering road feel and the drive ride comfort, on the premise of ensuring the brake feel and suspension ride comfort.     

On the development of Bi-Level Integrated System Collaborative Optimization

Bi-Level Integrated System Collaborative Optimization (BLISCO) is a new multidisciplinary design optimization (MDO) method based on Bi-Level Integrated System Synthesis (BLISS) and Collaborative Optimization (CO). The key ideas of BLISCO are to replace compatibility constraint with the sum of coupled outputs as an integrated objective of subsystems and to decompose design variables into system design variables and subsystem design variables, while maintaining the collaborative mechanism of CO. One mathematical example and two engineering problems are used to test the effectiveness of BLISCO under the platform of iSIGHT^TM. Results from the test cases show that BLISCO has satisfactory convergence, accurate result and reliable robustness.     

多策略黑猩猩优化算法研究及其工程应用

针对基本黑猩猩优化算法存在的依赖初始种群、易陷入局部最优和收敛精度低等问题,提出一种多策略黑猩猩优化算法EOSMICOA（chaotic elite opposition-based simple method improved COA）。在EOSMICOA算法中,利用混沌精英反向学习策略对黑猩猩个体位置进行初始化,提高种群的多样性和质量,同时在位置更新过程中利用单纯形法和群个体记忆机制对较差个体进行改进,进一步提高算法的局部开发能力和勘探能力,以及算法的寻优精度。为验证改进算法的寻优能力,将EOSMICOA算法与多个智能算法对20个复杂函数进行对比实验,结果表明EOSMICOA在收敛精度、寻优速度等方面都有明显优势。最后,将EOSMICOA与当前最新改进算法应用于焊接梁设计中,对比结果表明EOSMICOA可以更有效地应用于工程设计优化问题。     

基于协同优化算法的流速仪检定系统设计

提出了一种小型轻便的流速仪检定系统的设计方案,并运用改进的多学科协同优化方法求解各学科设计变量的最优解。首先介绍了算法思想及改进措施,并根据系统设计要求建立了各学科的设计变量、目标函数和约束条件；其次,运用遗传算法求解各学科设计变量的最优解,并根据优化结果完善了系统的设计方案,同时运用计算机仿真等方法验证了优化结果的可行性。结果表明,优化后的检定系统满足流体力学效应、匀速运动时间、系统重量等设计要求,同时证明了协同优化算法解决多学科设计优化问题的有效性。