Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration

This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. Data generated by computational fluid dynamics (CFD) is used to train the model for Reynolds numbers $Re \in [{1000-5000}]$ and angles of attack ranging from 9° to 11°. A robust Scaled Conjugate Gradient (SCG) algorithm is employed for efficient training of data. The ANN has a two-layer architecture, 9 fixed neurons in the first hidden layer and a varying number of neurons in the second layer to achieve optimal performance. The model yielded coefficients of determination ( $R^{2}$ ) of 0.994 (Coefficient of lift ( $C_{l}$ )) and 0.9615 (Coefficient of drag ( $C_{d}$ )) for training, and 0.9563 ( $C_{l}$ ) and 0.9085 ( $C_{d}$ ) for testing. Overall mean errors are found to be less than 1%. It offers a powerful surrogate modeling approach for aerodynamic studies at ultra-low Reynolds numbers. Moreover, it provides rapid and reliable alternatives to traditional CFD simulations in aerodynamic analysis for unseen cases.