Prediction of Flow Field Parameters for Aerodynamic Flap of Airfoil based on Deep Learning
Flap is an important technical tool to solve the flow separation of airfoil,and reasonable flap parameters are especially important for the pressure distribution on the airfoil surface.The combination of data-driven deep learning method and computational fluid dynamics(CFD)can quickly and effectively complete the feature identification and extraction of complex flow fields.In this paper,we proposed a convolutional neural network(CNN)-based method for predicting the pressure distribution on the airfoil surface,by extracting the flow features such as wake velocity and pressure of the flow field to build a pre-diction model for the pressure distribution on the airfoil surface.Firstly,the flow fields of flap of NACA 0012 airfoil with eight different lift angles were calculated by numerical simulation;secondly,the CNN predic-tion model was built using the extracted flow field data;finally,the predicted values were compared with the CFD calculated values.Results show that the convolutional neural network-based model has a high prediction accuracy to pressure coefficient distribution on the airfoil surface,and the predicted root mean square error(RMSE)of wake velocity model is only 0.1 when flat lift angle is 15°,indicating the wake velocity contains abundant flow field information.
deep learningconvolutional neural network(CNN)flap of airfoilflow field identificationunsteady
NETL
NSTL
万方数据
