9 citations · 9 across the 3 of their papers we have counts for
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physics.flu-dyn2024
QLingNet: An efficient and flexible modeling framework for subsonic airfoils
Kuijun Zuo, Zhengyin Ye, Linyang Zhu +2
Artificial intelligence techniques are considered an effective means to accelerate flow field simulations. However, current deep learning methods struggle to achieve generalization…
physics.flu-dyn2021
High Reynolds number airfoil turbulence modeling method based on machine learning technique
Xuxiang Sun, Wenbo Cao, Yilang Liu +2
In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-A…
physics.flu-dyn2018★ 9 cited
Machine learning methods for turbulence modeling in subsonic flows over airfoils
Weiwei Zhang, Linyang Zhu, Yilang Liu +1
Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism…