基于遗传算法的寻优解卫星星座优化设计策略

卫星星座优化设计与直接部署卫星或普通迭代计算后部署卫星等方法不同,目的 是在有限的资源中达到更好的星座观测效果.通过使用遗传算法(Genetic Algorithm),在卫星星座构型模型的基础上,得到星座种群内对目标观测实效性较强、重访周期较短的卫星个体,并使用较优的卫星个体生成Walker星座组网,实现了生成的星座对目标区域的高精度观测与覆盖.这种方法避免了复杂的计算与主观上的加权计算,在经济成本和观测效果相互制约的前提下,得到了更加高效的卫星星座构型设计策略.将此优化设计策略用于选定的卫星星座构型中,通过仿真实验表明,基于遗传算法优化后的"深圳一号"卫星星座相较于其优化前的部署,其对目标区域及全球区域的整体观测性能提升了90％以上.     

复合材料双钉胶螺混合连接接头多参数优化设计北大核心CSCD

为提高复合材料双钉胶螺混合连接接头的承载能力,本文结合实验和有限元方法,利用ABAQUS建立了有限元模型并验证了其可行性,基于代理模型和遗传算法,对碳纤维复合材料板与钛合金板双钉混合连接结构的板宽、端距和孔距等参数开展了多参数优化设计。结果表明:二次多项式代理模型的预测结果精度最高;与优化前试验得到的常规混合连接接头的载荷性能相比,优化后的结构承载能力提升了44.35%。     

Low Earth orbit constellation design using a multi-objective genetic algorithm for GNSS reflectometry missions

Spaceborne global navigation satellite system reflectometry (GNSS-R) is an innovative bistatic radar remote sensing technique utilizing low Earth orbit (LEO) based GNSS-R instruments to acquire GNSS L-band opportunistic signals for measuring geophysical parameters. A GNSS-R LEO constellation with an optimization design for its specialized missions is very significant and necessary. However, the constellation design involves multi-parameter and multi-objective optimization, and the classical analytic solution is not capable of such a complicated issue. This study proposes a multi-objective LEO constellation design method with a genetic algorithm (GA) and presents a framework for designing two GNSS-R LEO constellations, termed “lower-latitude constellation” for typhoons and hurricanes observation in the tropics and “global constellation” for global geophysical parameter measurements. Then, the observation capability of both designed constellations is evaluated in terms of the number of reflection points, spatial coverage density, and revisit time to verify the GA efficiency in LEO constellation design. Results show that the two designed LEO constellations with high fitness function values possess optimal orbit parameter set configuration and outperform the existing CyGNSS constellations in observation performance. Compared with CyGNSS, the number of reflection points observed by the lower-latitude constellation and the global constellation increases by 38% and 45%, as well as the spatial coverage density increases by 28% and 36%. The revisit time for the lower-latitude constellation is reduced by 0.29 h, whereas the revisit time for the global constellation increases by one hour.     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.     

一种基于遗传算法的最小熵自聚焦方法

自聚焦算法是获得高质量合成孔径雷达SAR(Synthetic Aperture Radar)图像的重要步骤.针对复杂成像场景和复杂运动误差SAR自聚焦问题,提出一种基于遗传算法的最小熵自聚焦方法.方法 通过高阶多项式函数拟合相位误差,以图像熵为代价函数,利用遗传算法最小化代价函数进而估计出相位误差,尤其适合于成像场景没...