翼型
空气动力学
俯仰力矩
气动中心
空气动力
攻角
Lift(数据挖掘)
高保真
航空航天工程
物理
机械
计算机科学
声学
工程类
机器学习
作者
Xu Wang,Jiaqing Kou,Weiwei Zhang
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2022-07-28
卷期号:34 (8)
被引量:25
摘要
Ice accretion on wind turbine blades and wings changes the effective shape of the airfoil and considerably deteriorates the aerodynamic performance. However, the unsteady performance of iced airfoil is often difficult to predict. In this study, the unsteady aerodynamic performance of iced airfoil is simulated under different pitching amplitudes and reduced frequencies. In order to efficiently predict aerodynamic performance under icing conditions, a multi-fidelity reduced-order model based on multi-task learning is proposed. The model is implemented using lift and moment coefficient of clean airfoil as low-fidelity data. Through using few aerodynamic data from iced airfoils as high-fidelity data, the model can achieve aerodynamic prediction for different ice shapes and pitching motions. The results indicate that, compared with single-fidelity and single-task modeling, the proposed model can achieve better accuracy and generalization capability. At the same time, the model can be generalized to different ice shapes, which can effectively improve the unsteady prediction efficiency.
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