Aerodynamic optimization of rotor airfoil based on multi-layer hierarchical constraint method

Rotor airfoil design is investigated in this paper. There are many difficulties for this high-dimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis (PCA) dimensionality reduction and e-constraint method to translate the orig-inal high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is estab-lished by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the high-dimensional multi-objective engineering problem.     

采用改进非支配近邻免疫算法的低轨混合星座设计优化

针对低轨同构星座覆盖资源在纬度上分布不均匀的不足,提出采用低轨混合星座提升覆盖均匀性的设计方案,并推导了满足全球任意点平均每天覆盖一定次数的最小卫星规模估算公式。针对非支配近邻免疫算法（NNIA）约束处理方面的不足,提出基于约束支配的改进非支配近邻免疫算法（Modified NNIA）,并以此设计了一种低轨混合星座优化平台来优化带约束的星座设计问题。仿真结果表明,改进的NNIA算法在收敛速度和多样性上均优于非支配分层遗传算法（NSGA II）和多目标粒子群算法（MOPSO）,可大大提高星座设计的效率。同时优化结果也表明低轨混合星座可提高覆盖的均匀性和大部分区域的覆盖次数,进而减少特定覆盖要求所需的卫星数目。     

大攻角条件下的二维进气道型面优化设计

基于一维气体动力学,以进气道前体楔面楔角为优化设计参数,以总压恢复系数、流量系数最大及阻力系数最小为优化目标,对二维进气道外型面进行了优化设计,并综合考虑了大攻角条件下的位于背风面与迎风面上进气道面临的不同来流条件.在优化设计中,采用改进后的NSGA-Ⅱ遗传算法.在问题求解中,对NSGA-Ⅱ算法的交叉算子及优选策略进行了改进.优化结果表明.总压恢复系数与阻力系数的优化结果具有一致性,即二者可同时达到最优;第一级楔角的大小对流量系数的影响显著.对优化结果进行了数值模拟,数值模拟结果表明,文中设计的型面构型满足设计要求.该设计方法可用于二维进气道型面考虑攻角条件下的初步设计.     

Orbit Design for Responsive Space Using Multiple-objective Evolutionary Computation

Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets. Generally, there are several conflicting metrics to trade in the orbit design for responsive space. A special multiple-objective genetic algorithm, namely the Nondominated Sorting Genetic Algorithm Ⅱ (NSGAⅡ), is used to design responsive orbits. This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution, including the orbital elements and launch programs of responsive vehicles. Low-Earth fast access orbits and low-Earth repeat coverage orbits, two subtypes of responsive orbits, can be designed using NSGAⅡ under given metric tradeoffs, number of vehicles, and launch mode. By selecting the optimal solution from the obtained Pareto fronts, a designer can process the metric tradeoffs conveniently in orbit design. Recurring to the flexibility of the algorithm, the NSGAⅡ promotes the responsive orbit design further.      

遗传算法在雷达吸波涂层多目标优化设计中的应用

本文利用遗传算法实现了雷达吸波涂层的多目标优化设计.首先对目标函数进行合理设计,同时基于Pareto最优概念,采用非劣分层遗传算法(NSGA)进行多目标优化.对具体实例的优化结果表明,该方法利用共享函数和小生境技术,可以使优化结果均匀的收敛于Pareto域附近.同传统优化方法相比,该方法物理意义明确,对决策者来说更具科学性、针对性和实用价值.