双层梯度蜂窝吸波结构优化设计

设计了一种带蒙皮和金属底板的双层梯度蜂窝吸波结构，并利用Hashin-Shtrikman（HS）模型获得其等效电磁参数。在此基础上，以反射率低于-10dB带宽最大为优化目标，利用保收敛粒子群优化算法（Guarantee Convergence Particle Swarm Optimization，GCPSO）对四种不同排序方案的双层梯度蜂窝吸波结构进行优化设计。结果表明，入射电磁波的角度和极化方式以及吸波材料的选择、排序和厚度对双层梯度蜂窝结构的吸波性能有很大的影响；不同入射角度下TM极化时的吸波特性明显优于TE极化，在入射角为60^°时最为明显；对比四种方案优化结果显示，case 1方案由于选用损耗角较小的吸波材料充当透波层，分布于蒙皮下面，而选用损耗角较大的吸波材料作吸收层，并置于吸波结构底层，因而具有最大的优化目标函数，吸波效果最佳，且蜂窝高度仅为具有相同吸波效果的case 3方案的49.44%。因此，选择case 1方案作为本文最终优化结果。     

某型飞机作动筒管接头结构分析及优化改进

针对某型飞机主起落架收放作动筒管接头结构特点,本文从结构功能、连接强度方面对管接头进行分析,并提出了作动筒管接头配合结构优化改进措施, 改进后的管接头结构能满足预期使用要求。     

高体积分数SiCp/Al激光诱导氧化辅助铣削研究

为了解决高体积分数的SiC_p/Al复合材料在常规切削加工中存在切削力大、刀具磨损快、表面完整性差等问题。本文针对高体积分数SiC_p/Al材料开展激光诱导氧化辅助铣削技术研究，通过激光辐照铣削区域形成易于去除的疏松氧化层提高切削性能，同时开展氧化层调控策略及激光诱导氧化辅助下的铣削参数优化工艺研究，研究不同激光能量密度、辅助气体对氧化层质量的影响及铣削参数优化。结果表明随激光能量密度的增大热影响区宽度和烧蚀沟槽深度随之增加，在氧气辅助下易形成疏松且易于去除氧化层。选取较高的激光能量密度可获得较好的氧化效果，而使用PCD金刚石铣刀，主轴转速为10 000 r/min，在每齿进给量为7.5 μm /s可获得最佳表面质量。     

航天器材料应用验证特点及其指标体系设计与优化

“材料应用验证”是为适应复杂工程研制任务而建立的一种材料多参数指标在特定服役需求下应用适用度评估的综合评价方法，也是通过一系列的试验、测试与表征手段获得材料各项性能数据、曲线、图谱，并通过综合分析确定材料应用可行性的分析方法。文章从航天器发展对高性能、多样化材料快速应用转化需求出发，阐释材料应用验证任务具有指标体系的综合性、通用性、短周期、低成本以及闭环式验证特点，进而提出了覆盖性、关重性、精准性、独立性、经济性的指标体系设计原则，以及材料应用验证的三层级五要素即材料批次稳定性、工艺适用性、环境适应性、服役安全性及组件健壮性指标体系设计及优化方法。     

Conditional self-attention generative adversarial network with differential evolution algorithm for imbalanced data classification

Imbalanced data classification is an important research topic in real-world applications, like fault diagnosis in an aircraft manufacturing system. The over-sampling method is often used to solve this problem. It generates samples according to the distance between minority data. However, the traditional over-sampling method may change the original data distribution, which is harmful to the classification performance. In this paper, we propose a new method called Conditional Self-Attention Generative Adversarial Network with Differential Evolution (CSAGAN-DE) for imbalanced data classification. The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data. In CSAGAN-DE, the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class. Then, the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance. Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method. The results show that the new method can efficiently improve the classification performance compared with other related methods.     

Optimization of multi-element airfoil settings considering ice accretion effect

Almost half of all flight accidents caused by inflight icing occur at the approach and landing phases when high-lift devices are deployed. The present study focuses on the optimization of an ice-tolerant multi-element airfoil. Dual-objective optimization is carried out with critical horn-shaped ice accumulated during the holding phase. The optimization results show that the present optimization method significantly enhances the iced-state and clean-state performance. The optimal multi-element airfoil has a larger deflection angle and wider gap at the slat and the flap compared with the baseline configuration. The sensitivity of each design parameter is analyzed, which verifies the robustness of the design. The design is further assessed when ice is accreted during the approach and landing phases, which also shows performance improvement.     

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.