Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

To improve performances of multi-objective optimization algorithms, such as convergence and diversity, a hybridization- encouraged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm （NSGA-Ⅱ）. This mechanism uses the normalized distance to evaluate the difference among genes in a population. Three possible modes of crossover operators--＂Max Distance＂, ＂Min-Max Distance＂, and ＂Neighboring-Max＂--are suggested and analyzed. The mode of ＂Neighboring-Max＂, which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front, is chosen and used in NSGA-Ⅱ on the basis of hybridization-encouraged mechanism （short for HEM-based NSGA-Ⅱ）. To prove the HEM-based algorithm, several problems are studied by using standard NSGA-Ⅱ and the presented method. Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions, convergence, diversity, and quality of solutions. The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm. Finally, as an example in engineering practices, the presented method is used to design a longitudinal flight control system, which demonstrates the obtainability of a reasonable and correct Pareto front.     

一种高效的基于可靠性的多学科设计优化方法(英文)

Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.     

电火花加工计算机辅助工艺过程设计系统

介绍电火花计算机辅助工艺过程设计系统（ＣＡＰＰＳ），用于为一个给定目标选择最佳操作。同时，还考虑了其它的会影响ＥＤＭ加工过程的加工方法（如铣削等）。系统的输入信息是在已有的ＣＡＤ信息的基础上产生的基于ＣＡＰＰ特征的工件描述。每个特征类对应于一个特定的一般工艺规程，并由系统对其进行评价，然后基于最低成本的原则，计算出最优工艺规程。同时还介绍了一种计算工时的通用方法。     

CAD技术及其在卫星工程中的应用

CAD 技术作为一种高效率、高质量、高精度的设计技术,正在包括航天技术在内的多个领域中广泛应用,发挥其效益。尤其是在机械(MCAD)与电子(ECAD)两方面的应用,更是令人瞩目。本文就CAD 技术的范畸、功能、组成、主要技术等并结合卫星研制的具体应用前景,作一介绍,目的在于推动CAD技术的开展,并为CAD/CAM 一体化做好思想准备。     

Aerodynamic optimization design for high pressure turbines based on the adjoint approach

A first study on the continuous adjoint formulation for aerodynamic optimization design of high pressure turbines based on S2surface governed by the Euler equations with source terms is presented.The objective function is defined as an integral function along the boundaries,and the adjoint equations and the boundary conditions are derived by introducing the adjoint variable vectors.The gradient expression of the objective function then includes only the terms related to physical shape variations.The numerical solution of the adjoint equation is conducted by a finitedifference method with the Jameson spatial scheme employing the first and the third order dissipative fluxes.A gradient-based aerodynamic optimization system is established by integrating the blade stagger angles,the stacking lines and the passage perturbation parameterization with the quasi-Newton method of Broyden–Fletcher–Goldfarb–Shanno(BFGS).The application of the continuous adjoint method is validated through a single stage high pressure turbine optimization case.The adiabatic efficiency increases from 0.8875 to 0.8931,whilst the mass flow rate and the pressure ratio remain almost unchanged.The optimization design is shown to reduce the passage vortex loss as well as the mixing loss due to the cooling air injection.     

自由返回轨道的P平面参数快速设计方法

针对自由返回轨道求解过程中地心轨道类型变化造成的B平面参数方法计算失败问题,提出一种基于P平面参数的自由返回轨道快速设计方法。首先,基于轨道半通径参数的普适性,给出了不同轨道类型的P平面参数定义,建立了以P平面参数为求解目标量的自由返回轨道求解模型。其次,给出了基于P平面参数的自由返回轨道快速设计方法,在构建的瞬时地月惯性系下,以平面双二体自由返回轨道作为初值,实现了高精度力模型下的自由返回轨道快速求解。对8种构型自由返回轨道的设计结果表明,P平面参数具有类似于B平面参数的大收敛域,且有效解决了轨道类型变化对计算的影响,可直接应用于中国后续月球探测任务轨道设计。     

改进PointNetLK的点云智能配准与位姿图优化方法

针对空间在轨服务任务中的非合作目标相对位姿测量问题,提出一种目标可测部位点云的智能配准方法。首先,通过Straight Through滤波算法对半物理仿真平台采集得到的点云进行目标提取,以消除背景数据等杂乱信息;其次,改进PointNetLK神经网络点云配准算法,将提取后的点云数据作为输入,从而获得初步配准结果,解决非合作目标先验信息缺失导致的无法配准问题;最后,建立基于位姿图的优化模型,以降低配准误差,提高配准精度。实验结果表明,与传统迭代最近点(ICP)算法相比,配准综合误差从6.3598降低到1.7291,精度提高约 72.81% 单次耗时从33.16 s降低到4.2 s,效率提升约87.33%,与当前SM ICP等其他算法相比,也具有一定的优势。     

基于高效数值方法的一种高超声速飞行器外形气动力/热综合优化设计研究

基于所发展的高效气动力/热快速预测方法,建立了适用于高超声速飞行器的综合考虑气动力与气动热特性的外形优化设计技术,并在一种高超声速飞行器外形设计中应用,通过优化设计改变了初始外形纵向静不稳定特性,优化后外形实现了纵向静稳定、大攻角自配平和非驻点表面最大热流下降的设计目标。所发展的方法为适应气动外形快速选型设计需求提供了有效的途径。     

基于左手材料的翼面隐身结构设计及优化

翼面隐身结构能同时满足飞行器机翼气动、结构和隐身的要求,但在空间有限的机翼中应用时,由于结构特点使其隐身效果受到限制。为了解决该问题,在传统隐身结构中加入左手材料(LHM)进行改进。首先选取一种典型的LHM,从隐身设计角度出发,利用其雷达散射截面(RCS)计算不同入射角度下的后向吸收率,对其电磁特性进行研究。然后根据LHM的电磁特性,将其应用于翼面隐身结构,在相同的RCS减缩效果下,应用LHM可有效降低隐身结构体积。最后,为了进一步提高隐身效果,提出一种夹芯型LHM翼面隐身结构,并对该隐身结构中的结构参数利用代理模型进行优化。研究结果表明,相较金属翼面段RCS降低了15dB以上,较相同结构的翼面隐身结构RCS降低了10dB以上。     

A strategic flight conflict avoidance approach based on a memetic algorithm

Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.