共固化粘弹性复合材料的结构多目标进化优化设计

共固化粘弹性复合材料结构兼具结构承载和阻尼减振能力,设计时需同时考虑强度、刚度、质量和阻尼性能等指标要求,且设计变量众多,因此传统的设计方法难以实现结构的优化设计。本文建立了共固化粘弹性复合材料结构的多目标优化模型,优化目标为结构质量最小化和模态损耗因子最大化,设计变量包括铺层厚度、方向角和阻尼层厚度,并考虑结构动刚度的约束条件。阻尼结构的分析采用基于有限元法的模态应变能法,进化优化采用改进的非支配排序多目标遗传算法(NSGA-II)。最后的优化算例表明将多目标遗传算法应用于共固化粘弹性复合材料结构阻尼/结构一体化设计的可行性。     

Identification of crystal orientation for turbine blades with anisotropy materials

A novel approach to identify the crystal orientation of turbine blades with anisotropy materials is proposed. Based on enhanced mode basis, with the main advantages of its efficiency, accuracy and general applicability, the blade vibration mode of each order is linearly constructed by several specified mode shapes, which are obtained from the considered turbine blade with specified crystal orientations correspondingly. Then, a surrogate model based on Kriging method is introduced for constructing the condensed perturbed matrix of stiffness in order to improve the efficiency even further. The constructed surrogate model allows to perform the modal analysis of turbine blades with arbitrary crystal orientations in higher efficiency, due to the fact that the elements of condensed perturbed matrix of stiffness are considered in construction of the surrogate model rather than concerning the perturbation of all the elements of the initial stiffness matrix for the blade. Genetic algorithm is finally employed to optimize the defined fitness functions in order to identify the crystal orientation angles of turbine blades. Several corresponding examples demonstrated the accuracy, efficiency and general applicability of the proposed method.     

基于协进化的管路系统智能寻径

在航空发动机外部管路敷设过程中,多条管路路径之间存在相互影响,并最终影响管路的整体最优路径。本文在遗传算法的基础上采用协进化的思想研究多条管路同时敷设的方法,提出各条管路分别以遗传算法进行路径寻优,管路之间以协作度衡量管路系的质量的方法。并在二维凸多边形障碍物的情况下验证了算法的可行性。      

面向空间目标相对轨道确定的编队优化方法

针对提高空间目标相对轨道确定精度的问题，研究了在主航天器轨道运动受限时，通过设计和优化辅航天器相对轨道要素的航天器编队优化方法。首先，介绍了基于扩展卡尔曼滤波的双视线测量相对轨道确定方法；之后，通过研究双视线测量下的空间目标定位误差变化规律，得到了减小定位误差的角度条件；然后，通过分析该角度条件和辅航天器相对轨道要素的关系，设计并采用遗传算法优化了辅航天器相对轨道；最后，数学仿真结果表明，设计的编队可保证目标相对位置估计误差收敛，优化后的编队可使目标相对位置估计误差减小至0.3 km且不超过1.2 km。     

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.     

Improving the low orbit satellite tracking ability using nonlinear model predictive controller and Genetic Algorithm

The satellite motion on the reference orbit (RO) with less energy consumption has always persuaded researchers to design optimal control systems. The nonlinear nature and time-varying equations of motion make this quest more challenging. The present study proposes a novel control system for satellite motion on the RO by considering a comprehensive model of its dynamics in orbit and a Nonlinear Model Predictive Controller (NMPC). The NMPC calculates the sub-optimal control inputs of satellite motion reference on the elliptic orbit by minimizing a convex cost function at each stage. Moreover, all weighting parameters of the cost function are optimized by the Genetic Algorithm (GA) to produce less perturbation and guarantee the best NMPC performance. Finally, the implemented NMPC has been compared to a Linear MPC (LMPC). The results show that not only can the NMPC resist against larger errors and perturbations, but it can also compensate for those errors by returning the satellite to its main orbit and maintaining it.     

Optimal planning for a multi-debris active removal mission with a partial debris capture strategy

In this paper, a new mission model, called a multi-debris active removal mission with partial debris capture strategy, is proposed. The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit. This process is then repeated until all of the scheduled debris is removed. A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission wit...     

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.