二元喷管对发动机性能影响的实验研究

用涡喷-6发动机，在地面台架上对二元收敛喷管和轴对称收敛喷管进行了发动机性能的对比试验，研究了二元喷管的气动特性和喷口面积的影响。结果表明，二元喷管对发动机推力和耗油率影响不大，在发动机最大状态，二元喷管推力约比轴对称喷管小1．0％，耗油率增加约0．7％。     

搅拌摩擦焊在电机制造中的应用前景

介绍了搅拌摩擦焊的原理、特点和发展情况；分析了该技术用于焊接紫铜／紫铜接头的可行性。并对其在电机制造业中的应用前景作了初步探讨。     

基于磁路法与等效热网络法的航天 永磁同步电机设计与仿真

针对航天永磁同步电机方案初步设计耗时长、过度依赖商业软件的问题，基于磁路法和热网络法，提出了一套方案设计阶段航天永磁同步电机磁热性能快速预估与仿真方法。给出了定子内径、定子外径、铁芯长度、匝数等关键参数的取值准则，建立了包含36个节点集总参数热网络模型，并以端部绕组为例给出了热平衡方程的详细推导过程。通过与成熟商业软件对比，其电磁计算最大误差出现在电流有效值上，偏差值为607%；与样机实测值对比，绕组温升最大误差为73%，满足方案设计阶段预示精度要求，为方案设计阶段航天永磁同步电机快速性能预估提供有力支撑。     

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.