类C-HGB布局锐边化气动隐身优化设计

C-HGB （Common Hypersonic Glide Body）作为美国现今试验成功率最高的高超声速滑翔飞行器已被美国防部列入重点发展项目，并预计发展为三军通用武器。本文针对此类"球+双锥+弹翼"的气动外形和隐身性能进行了研究。采用考虑交互作用的正交设计进行了多约束下参数敏感性分析，并采用改进的基于DE差分变异算子的具有自适应学习机制的优化算法进行气动优化，对优化后的外形采用简单高效的锐边化设计，在提高升阻比的同时可降低雷达散射截面积，极大提高突防能力。锐边设计改变了弹体部分表面温度分布，降低制造成本的同时对热防护系统的设计和红外隐身具有一定积极意义。结果表明：锐边化设计可以得到较好的气动隐身性能，是未来高超声速滑翔飞行器的潜在设计方案。     

Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping

This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos.     

月面无人自主采样返回任务动力下降点确定及验证

动力下降点确定是实施月面软着陆的重要环节，是多系统间复杂迭代的过程，涉及轨道设计、制导律设计、着陆目标的采样区确定、着陆及起飞安全分析。其设计结果直接影响了最终着陆点的位置和着陆过程的着陆安全，也间接影响采样安全和采样工程目标的实现结果。针对嫦娥五号在实施月面软着陆前确定动力下降点的任务需求，提出了通过多次轨道控制与最优标称制导轨迹搜索联合控制策略的动力下降点确定方法。首先，根据月面无人自主采样返回任务设计总结了动力下降点确定原理和约束条件；然后，详细论述了月面无人自主采样返回任务软着陆过程动力下降点确定方法；最后，通过嫦娥五号在着陆前主要的几次轨控实施结果分析了其对动力下降点的影响，同时综合了着陆区地形分析及着陆、起飞安全性分析，对动力下降点进行确定并根据最终在轨飞行结果进行验证。验证结果表明，基于“逐次逼近寻优方法”的月面软着陆环节动力下降点的确定方法有效，可以为后续地外天体软着陆等任务提供参考和借鉴。     

水陆两栖飞机静力试验优化机翼变形的载荷配平

水陆两栖飞机全机静力试验的浮筒着水工况试验中，在试验允许的常规载荷配平方案下，压向大量级水载荷作用于浮筒结构引起机翼较大变形，影响试验精度，因此进行以减少机翼变形为目标的载荷配平研究。将机翼近似为悬臂梁结构，在构建的机翼力学模型基础上应用Green函数建立机翼的挠度曲线方程。首次以考核区域边界肋的挠度和转角为优化目标并建立多目标函数，通过层次分析法和极差变换标准化处理方法将其转化为单目标函数后，采用引入交叉和变异因子的改进蚁群算法进行载荷配平方案优化运算。有限元分析及试验结果显示，优化得到的载荷配平方案可以显著地减少机翼变形，配平载荷不影响考核区域的真实变形，证明了该优化方案的正确性和可行性。     

General optimal design of solar-powered unmanned aerial vehicle for priority considering propulsion system

This paper describes the general optimization design method of Solar-Powered Unmanned Aerial Vehicle which priority considering propulsion system planning. Based on the traditional solar powered aircraft design method, the propulsion system top-level target parameters which affect the path planning are integrated into the general optimization design. According to the typical mission requirements of Solar-Powered Unmanned Aerial Vehicle, considering the design variables such as wing area, aspect ratio, design mission date and so on, the general optimization is carried out with the minimum aircraft weight as the optimization objective. The influence of wing area and aspect ratio on the optimal design results is analyzed and compared with the traditional design method. The results show that the general design method of Solar-Powered Unmanned Aerial Vehicle for priority considering propulsion system can greatly reduce the electricity demand of energy storage battery, greatly reduce the aircraft weight of Solar-Powered Unmanned Aerial Vehicle.     

石英陶瓷与碳纤维增强复合材料间胶接增强的优化工艺

为提高硅橡胶胶黏剂胶接石英陶瓷和碳纤维增强复合材料的强度和耐温性能,研究了胶接面湿热状态、打磨程度、胶层厚度、操作等待时间等工艺参数对胶接强度的影响。在试片正常烘干、打磨的情况下,当胶层厚度为0.4 mm、操作等待时间小于0.5 h时,胶接效果最优,其压缩剪切强度达到了3.06 MPa。探索了底涂剂处理对胶接强度和耐温性能的影响。在温度不高于100℃时,底涂剂A的效果较好。经底涂剂A处理后,胶接试片常温下的强度提高了13.7%,达到3.48 MPa;100℃时的强度提升率达到37.7%。在更高温度下,底涂剂B的效果更为显著。经底涂剂B处理后,胶接试片常温下的强度提升了10.1%,达到3.37 MPa;300℃时的强度提升了44.0%,达到1.57 MPa;200℃时的强度提升率最高,为49.0%。测试结果为高温条件下石英陶瓷胶接结构的胶接工艺优化及其应用提供了参考。     

利用集群内测距和对目标测向的协同定位方法

当多个飞行器间相对位置测量精度显著高于飞行器绝对定位精度时，在飞行器间增加相对测量，对各飞行器的定位测量协同处理，能提高绝对定位精度。本文针对集群飞行器对非合作目标不能测距而仅能测向的情况，通过在集群飞行器间引入相互测距，来提高集群飞行器自身和对非合作目标的定位精度。对于这种不完整相对测量情况，给出了能提高定位精度的协同定位求解方法，包括非线性静态优化估计和线性化后最小二乘估计方法。这种协同定位方案，不需要进行完整的相对位置测量，对测距和测向在群体相对测量中进行合理分解，降低了飞行器测量设备配置的要求。通过仿真验证了该方法的有效性。     

基于神经网络和人工势场的协同博弈路径规划

协同博弈路径规划是空战自主决策、机器人体育比赛等应用场景中的重要问题，其难点在于对环境对抗性反馈的实时自适应和多智能体的相互配合。提出一种基于神经网络和人工势场的协同博弈路径规划方法，使用反向传播（BP）神经网络自适应调整人工势场函数系数，并将人工势场作为神经网络输出端的特征提取。为解决真实样本质量和数量不足的问题，基于遗传算法仿真生成样本数据用于神经网络训练，并通过滚动时域的思路面向动态博弈优化样本性能。从样本数据中提炼出距离差与航向差以反映协同和博弈特性，利用神经网络的黑盒特性和学习能力解决协同博弈问题。应用于二对一反隐身超视距空战路径规划，比经典人工势场法有明显性能提升，且计算开销可接受，计算复杂度分析表明该方法可以较好扩展到多机对抗场景。     

基于微分进化算法的二自由度PID控制器参数优化

针对二自由度PID控制器参数多、调节困难等问题,提出了一种基于微分进化算法参数调节优化方法。证明了微分进化算法能够收敛到平稳点,并具有概率全局收敛性。研究了二自由度PID控制器参数与抑制特性、跟踪特性的关系,并将微分算法与遗传算法、粒子群算法的参数优化结果进行了比较。仿真验证表明,微分算法能够更好地实现二自由度PID控制器参数的调节,具有优异的跟踪特性和抑制特性。     

Adaptive optimization methodology based on Kriging modeling and a trust region method

Surrogate-Based Optimization (SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-region-based refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO methods.