Deep Learning CNN-based aerodynamics parameters prediction method

CNN for airfoil lift-to-drag-ratio prediction

This repository contains data, code, and results for implementing an airfoil lift-to-drag ratio prediction method based on Convolutional Neural Network. The network model can take into cnn model the airfoil contour and predict its areodynamics parameters such as lift-to-drag ratio.

Model Architecture:_

The data has been taken from the work of Zi Li @Zili and I modified the CNN using Keras and Tensorflow.

Model building: This CNN models runs 5000 times faster than commercial CFD software with relative low error (i.e Test MSE 0.002 after 50 epoch's training)

Contents

/data/raw_data/foil_figure.rar:
 a file of all filled-in grayscale airfoil contour figures generated from coordinates txt files from UIUC Airfoil Data Site.
/data/raw_data/csv.zip:
 a file of all samples' lift-to-drag ratio calculated by Xflr5 /data/parsed_data/1_300.mat
 the above raw data is parsed into .mat file for loading
 Please unzip raw data and modify directory and then run cnn-airfoil.ipynb it.

Result

Licence

The source code is released under MIT Licence.