平流层飞艇外形的设计优化

平流层飞艇是依靠浮力升空的飞行器.飞艇外形对于平流层飞艇的设计至关重要,为了获得能够满足动力结构和重量等各个学科要求的最优艇形,将综合设计优化技术引入到飞艇外形设计中,提出了适用于优化的飞艇外形生成曲线,分析了与外形有关的气动、结构和重量等因素,建立了飞艇气动阻力、表面积和最小环向应力的模型,构造了复合目标函数,并针对某飞艇外形进行了优化设计.仿真结果证明,利用蒙特卡罗算法优化设计后的艇形优于传统艇形.     

粒子群优化算法在船型优化设计中的应用仿真

研究船舶外形结构优化设计,由于船舶在航行过程中,不同型号的船舶机动性能力和稳定性存在着较大差异,造成船舶外形的梯度信息突变明显.传统的优化设计方法多是基于梯度信息的优化设计方法,在船舶外形结构梯度发生突变的情况下,很容易陷入梯度差过大,结果不收敛,造成外形设计的缺陷较为明显.为解决上述问题,提出一种基于粒子群优化算法的船舶外形优化设计,利用粒子群的搜索能力,对设计结构中的参数进行最优搜索.针对船舶外形梯度信息差异大,很难小范围收敛的问题,通过遗传算法对其进行优化,使得参数在小范围内也能实现最优化.保证设计结果的稳定,仿真结果表明,改进算法设计的船舶外形参数在升力、阻力和升阻比等参数上有了较为明显的优化,在全航行周期中具有更优的性能.     

基于遗传算法的UUV线型多目标优化设计

基于遗传算法的多目标空间区域内的探索性全局优化技术,将阻力系数、丰满度与最大减压系数发生位置作为综合判定指标,在解决UUV线型优化的过程中,引入集成优化框架,对UUV外形进行多目标优化设计;建立了UUV外形优化的一体化模型,针对UUV外形优化设计的要求,对外形设计进行参数化,提出设计变量的选取原则,并针对具体算例进行仿真优化设计,优化计算结果表明:该优化设计方法有效地改善了UUV的阻力性能和压力分布,阻力减小大约4％,在工程应用中是可行的,为目前UUV外形优化设计提供了一个完整的方法.     

水下自航行器外形及水动力性能优化

研究水下自航行器(AUV) 外形及水动力性能优化的问题,为使得AUV具有较小航行阻力的同时拥有较大承载能力,需要不断进行AUV模型重建以及水动力结果分析,人工完成将会耗时很长,Isight多学科优化设计平台搭载常用的优化算法—NSGA-II遗传算法,整合Solidworks、Gambit、Fluent三大集成模块实现数据交换以进行AUV外形的建模、仿真并完成设计过程的自动化和智能的设计探索,确定最佳设计参数;仿真结果表明,最终优化后的AUV不仅减小了航行阻力并且拥有更大的承载能力;因此采用多学科优化软件Isight能够有效提高AUV外形及水动力性能优化的准确性和效率,提升其整体水动力性能。     

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。     

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.     

多学科设计优化方法与传统设计优化方法的比较研究

多学科优化设计也是在传统设计优化基础上重要的质的发展。它是设计方法、传统机械设计知识、过程设计知识、现代信息技术交叉集成的大系统方法。多学科设计优化技术 以提高产品性能、缩短设计周期和降低研制成本为目的。本文将多学科优化设计与传统优化设计进行对比,并以实例分别进行多学科设计优化和传统优化设计,证明了多学 科优化设计的可行性和高效性.     

基于NURBS方法的气动外形优化设计

采用NURBS曲线曲面,对钝锥弹头和钝双锥弹体建立参数化曲面模型,取NURBS曲线控制点作为设计参数,应用高超声速面元法求解气动力特性,在给定设计约束下,采用遗传算法进行气动外形优化设计,并对优化结果进行了比较分析。结果表明,采用NURBS方法构造参数化外形,并结合优化技术可方便快速地获得所需最优外形;与应用二次曲线构造参数化外形相比,该方法对弹体形状控制更加灵活,并可局部修改弹头曲线形状。因此,基于NURBS方法发展整套的系统优化设计算法很有现实意义和应用价值。     

平流层飞艇蒙皮强度建模与仿真研究

关于飞艇蒙皮强度优化设计问题,平流层飞艇体积巨大,而蒙皮厚度远小于飞艇长度,结构在受载情况下容易发生较大变形甚至破坏。为了给飞艇蒙皮强度设计提供依据,建立了飞艇蒙皮在只受内外压差载荷作用下的环向和轴向应力方程,用Von Mises应力来定量描述蒙皮在环向和轴向应力作用下的整体受力情况。然后建立了飞艇蒙皮的计算模型,通过在ANSYS中进行非线性仿真分析得到蒙皮各点的受力情况,并将理论应力值与有限元仿真结果进行了对比,结果验证了理论方程的有效性,为飞艇蒙皮的应力估算提供参考。     

潜水器舱室空间布局的多学科设计方法研究

载人潜水器舱室空间布局涉及到多目标、多学科问题,通过对潜水器舱室空间的分析,研究了多学科设计优化方法对于这类多目标、多学科问题求解的有效性。建立潜水器多学科设计优化方法和协同优化思路,分解潜水器舱室并对潜水器舱室空间进行详细描述,构建基于Pareto的多目标遗传算法,对潜水器舱室空间进行多学科分解和计算,最终获得潜水器舱室空间设计方案。     

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.     

一种应急救灾飞艇部署与规划方法研究

飞艇具有高分辨、大覆盖、可定点观测及可快速部署等特点。开展应急救灾飞艇快速部署与规划研究能够减少灾害现场监测盲目性,提升救灾科学决策能力,是任务规划领域研究的热点问题之一。提出了一种应急救灾飞艇快速部署与规划新方法,综合考虑飞艇部署与规划问题,建立了基于信息熵的飞艇最优化部署模型,并在此基础上,提出了一种最大执行任务的约束满足任务规划算法,引入相容性矩阵和遗传算法,开展算法求解,降低规划复杂度。最后通过仿真实验,验证了所提方法的有效性。     

基于遗传算法的平流层飞艇路径规划设计

利用传统线性化方法--小扰动法将模型线性化,结合平流层飞艇的工作特性,把飞艇上升运动看成是纵向基准运动和横侧向扰动运动.然后采用最小值原理,根据飞艇的控制量和边界条件,设计和优化飞艇的上升轨迹,其性能目标函数是从初始点到目标点运动过程中能量消耗最小.利用遗传算法,在运动模型和控制量变化的共同约束作用下,求取目标函数的最小值.最后根据飞艇实际工作过程,对仿真结果分析表明,该方法具有实际应用价值.     

A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.

     

Fuzzy genetic sharing for dynamic optimization

Recently,genetic algorithms(GAs) have been applied to multi-modal dynamic optimization(MDO).In this kind of optimization,an algorithm is required not only to find the multiple optimal solutions but also to locate a dynamically changing optimum.Our fuzzy genetic sharing(FGS) approach is based on a novel genetic algorithm with dynamic niche sharing(GADNS).FGS finds the optimal solutions,while maintaining the diversity of the population.For this,FGS uses several strategies.First,an unsupervised fuzzy clustering method is used to track multiple optima and perform GADNS.Second,a modified tournament selection is used to control selection pressure.Third,a novel mutation with an adaptive mutation rate is used to locate unexplored search areas.The effectiveness of FGS in dynamic environments is demonstrated using the generalized dynamic benchmark generator(GDBG).     

欠驱动飞艇平面路径跟踪控制

针对欠驱动飞艇模型,提出一种基于制导向量场的平面路径跟踪控制方法.首先,基于牛顿-欧拉方程建立欠驱动飞艇动力学模型;然后,基于向量场理论构造制导向量场以获得期望偏航角,结合反步法设计路径跟踪控制律,并通过稳定性分析证明所设计的控制律能够使路径跟踪误差收敛到零而且闭环系统状态有界;最后,通过仿真对比了所提出方法与已有方法的控制效果,仿真结果验证了所提出方法的有效性和优越性.     

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

A particle swarm optimization (PSO) solver is developed based on theoretical information available from the literature. The implementation is validated by utilizing the PSO optimizer as a driver for a single discipline optimization and for a multicriterion optimization and comparing the results to a commercially available gradient based optimization algorithm, previously published results, and a simple sequential Monte Carlo model. A typical conceptual ship design statement from the literature is employed for developing the single discipline and the multicriterion benchmark optimization statements. In the main new effort presented in this paper, an approach is developed for integrating the PSO algorithm as a driver at both the top and the discipline levels of a multidisciplinary design optimization (MDO) framework which is based on the Target Cascading (TC) method. The integrated MDO/PSO algorithm is employed for analyzing a multidiscipline optimization statement reflecting the conceptual ship design problem from the literature. Results are compared to MDO analyses performed when a gradient based optimizer comprised the optimization driver at all levels. The results, the strengths, and the weaknesses of the integrated MDO/PSO algorithm are discussed as related to conceptual ship design.     

复杂耦合系统新兴的多学科设计优化方法

针对传统的复杂系统设计与工程设计领域中,设计方法或者优化方法是顺序地或者没有充分考虑子学科或子系统之间的交互作用与耦合关系的弊病,介绍了多学科设计优化方法(Multidisciplinary Design Optimization——MDO)在解决航天器设计复杂系统工程中的有效成就;接着介绍MDO的思想和研究内容,单级优化和两级优化的典型方法,然后叙述了目前国内外的研究成果和进展,充分表明其在解决此类复杂系统上的优势和研究价值;展望了多学科设计优化方法在所有此类复杂设计与工程领域重大研究价值的前景。     

A Problem Solving Environment Portal for Multidisciplinary Design Optimization

Multidisciplinary Design Optimization (MDO) is a design methodology that derives optimal design solutions by concurrently considering various mutually dependent design elements from an assortment of disciplines. As such, it is applicable to the designing of ships and automobiles, as well as to aero vehicles. However, applying MDO methodologies in the real world would require a designer to spend an enormous amount of time arranging and integrating resources used in the process. This paper proposes a Problem Solving Environment (PSE) Portal for MDO methodologies, providing an environment that enables designers to utilize design resources conveniently even without working knowledge of the systems. Furthermore, the PSE portal yields an optimal MDO environment by allowing for global collaborative sites, which securely share design resources, and by offering users an efficient interface.     

一种多学科设计优化工作流验证方法

在多学科设计优化集成系统中,设计过程和优化求解算法均通过可视化工作流实现,工作流有效性验证对提高设计效率和提高系统的用户体验具有重要意义。当前验证方法主要针对办公自动和企业管理系统中的工作流验证问题,多学科设计优化集成系统中的工作流验证问题研究较少。在分析前期工作验证技术的基础上,针对以循环结构为特征的优化环,提出一种基于图论方法的,名为浓缩环(concentration-loop)的验证算法。结合发射平台数字化设计系统的设计与实现,对该算法进行了验证。