航空燃油离心泵参数化设计方法研究综述

燃油离心泵是航空发动机控制系统的关键动力元件，结合当前航空燃油离心泵技术发展和设计需要，本文总结对比了离心泵典型参数化设计及优化设计方法。首先分析了离心泵四种参数化设计方法的技术特点及其局限，考虑到离心泵参数设计方法中存在的随机性和盲目性问题，进一步补充分析了离心泵的结构优化方法，包括基于启发式算法的全局优化方法、基于梯度的优化设计方法和基于代理模型的优化设计方法三类，并指出了三类优化设计方法亟待解决的科学和技术问题。在此基础上，通过分析国外航空发动机燃油离心泵的发展状况，指出了我国航空发动机燃油离心泵所面临的技术挑战、问题和技术发展方向，并立足于现有国内设计基础，给出了夯实我国航空燃油离心泵设计能力需开展的基础性研究工作。     

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.     

面向在轨靠泊加注的管路力学分析与优化设计

面向在轨伸出式靠泊加注任务，针对对接过程中加注管路因同步外伸而产生附加力的问题，提出了一种偏置椭圆螺旋构型的加注管路。首先，推导了管路的轴向刚度和应力数学表达式，通过有限元仿真和实验校验了上述两个表达式的准确性。之后，将偏置椭圆螺旋管路与其他三种适用管路在同一设计空间下分别进行了基于最小轴向刚度的构型优化设计，得到了上述四种管路各自的最优构型与最小附加力，形成了该类在轨加注螺旋管路的完整设计方法。与仿真和实验结果对比,所推导的管路轴向刚度与应力数学表达式的相对误差均小于5%,可用于该类管路的设计分析。与无偏圆螺旋管路相比，所提出的偏置椭圆螺旋管路将靠泊加注对接过程中的管路附加力降低了约83.5%，更适用于在轨靠泊加注任务。     

Aerodynamic optimization of a high-lift system with adaptive dropped hinge flap

The Adaptive Dropped Hinge Flap (ADHF) is a novel trailing edge high-lift device characterized by the integration of downward deflection spoiler and simple hinge flap, with excellent aerodynamic and mechanism performance. In this paper, aerodynamic optimization design of an ADHF high-lift system is conducted considering the mechanism performance. Shape and settings of both takeoff and landing configurations are optimized and analyzed, with considering the kinematic constraints of ADHF mechanism, and the desired optimization results were obtained after optimization. Sensitivity analysis proves the robustness of the optimal design. Comparison shows that the ADHF design has better comprehensive performance of both mechanism and aerodynamics than the conventional Fowler flap and simple hinge flap design.     

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.