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.     

Downwash modelling for three-lifting-surface aircraft configuration design

This paper introduces a semi-empirical model to predict the downwash gradient at the horizontal tail of a three-lifting-surface aircraft. The superposition principle applied to well established formulations valid for two lifting surfaces is not a reasonable approach to calculate the downwash of a canard-wing-tail layout, and this paper demonstrates that such a basic technique leads to incorrect results. Therefore, an ad hoc prediction model is proposed that considers the combined nonlinear effec...     

跨声速压气机叶片掠和机匣处理组合效应的机理研究

针对典型跨声速压气机转子，采用非定常数值模拟方法研究了叶片掠设计和机匣处理的组合作用对压气机转子性能的综合影响。分别探讨了转子前掠、后掠和掠叶片与机匣处理组合等形式对转子峰值效率和稳定工作裕度的影响效果和内在机制。研究发现，前掠转子与机匣处理组合能够以较小的效率损失（-0.56%）为代价获得最佳的扩稳效果；在前掠转子与机匣处理组合作用下，对间隙泄漏流的动量激励和叶顶低能堵塞流体的有效消除是组合机匣处理扩稳的本质原因。结果表明，采取前掠转子和机匣处理的组合形式，可以弥补前掠转子设计损失转子最大负荷的不足，同时以较少的效率损失代价充分发挥了机匣处理的扩稳潜力。     

复合材料风扇叶片高周疲劳薄弱点位置预测

提出了一种复合材料风扇叶片高周疲劳薄弱点位置预测方法.利用铺层信息文件,在ACP(ANSYS Composite Pre-Post)中通过壳单元法向拉伸建立全尺寸风扇叶片有限元模型,基于ANSYS-Work-bench和Tecplot底层函数接口,开发的后处理程序完成铺层叶片有限元数据提取和数据库建立.根据复合材料CL...