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A PCA-ANN-based inverse design model of stall lift robustness for high-lift device

机译:基于PCA-ANN的高举装置失速举升鲁棒性逆设计模型

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The concept of stall lift robustness for high-lift device (HLD) measures the stability of lift values under a series of angles of attack (AoA) around stall, which plays a significant role in flight safety. In this article, a stall lift robustness design with the consideration of aerodynamic constraints (stall AoA, average lift, etc.) is carried out, where design targets are set as a series of lift values on Lift-AoA curve. A Principle Component Analysis (PCA)-Artificial Neutral Network (ANN)-based inverse design model is introduced. The design targets are transformed by PCA for data dimension reduction. Then, the new set of design targets are input into the surrogate model of ANN, and corresponding geometry of new HLD is predicted. The ANN is constructed through database and sample points are screened considering lift unsteadiness. The design procedure is iterated to meet the design accuracy. The process of stall lift robustness design with the proposed model is discussed in this article, and the design results are validated by Detached Eddy Simulation (DES). (C) 2018 Elsevier Masson SAS. All rights reserved.
机译:高升力设备失速举升鲁棒性(HLD)的概念衡量失速周围一系列迎角(AoA)下升程值的稳定性,这在飞行安全中起着重要作用。在本文中,进行了考虑空气动力学约束(失速AoA,平均升程等)的失速举升鲁棒性设计,其中设计目标设置为Lift-AoA曲线上的一系列升力值。介绍了一种基于主成分分析(PCA)-人工神经网络(ANN)的逆设计模型。 PCA转换了设计目标,以减少数据尺寸。然后,将新的设计目标集输入到ANN的代理模型中,并预测新HLD的对应几何形状。通过数据库构建人工神经网络,并考虑举升的不稳定性来筛选采样点。迭代设计过程以满足设计精度。本文讨论了用所提出的模型进行失速提升鲁棒性设计的过程,并通过Dedched Eddy Simulation(DES)验证了设计结果。 (C)2018 Elsevier Masson SAS。版权所有。

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