Fourier neural operator with boundary conditions for efficient prediction of steady airfoil flows  

作　　者：Yuanjun DAI Yiran AN Zhi LI Jihua ZHANG Chao YU 

机构地区：[1]Department of Machanics and Engineering Science,College of Engineering,Peking University,Beijing 100871,China [2]Beijing Institute of Mechanical and Electrical Engineering,Beijing 100074,China

出　　处：《Applied Mathematics and Mechanics(English Edition)》2023年第11期2019-2038,共20页应用数学和力学（英文版）

摘　　要：An efficient data-driven approach for predicting steady airfoil flows is proposed based on the Fourier neural operator(FNO),which is a new framework of neural networks.Theoretical reasons and experimental results are provided to support the necessity and effectiveness of the improvements made to the FNO,which involve using an additional branch neural operator to approximate the contribution of boundary conditions to steady solutions.The proposed approach runs several orders of magnitude faster than the traditional numerical methods.The predictions for flows around airfoils and ellipses demonstrate the superior accuracy and impressive speed of this novel approach.Furthermore,the property of zero-shot super-resolution enables the proposed approach to overcome the limitations of predicting airfoil flows with Cartesian grids,thereby improving the accuracy in the near-wall region.There is no doubt that the unprecedented speed and accuracy in forecasting steady airfoil flows have massive benefits for airfoil design and optimization.

关 键 词：deep learning(DL) Fourier neural operator(FNO) steady airfoil flow 

分 类 号：O368[理学—流体力学]