Computational Optimization of Airfoil Aerodynamics via Machine Learning

Aerodynamic optimization is a critical aspect of airfoil design, traditionally relying on computational fluid dynamics (CFD) simulations to predict lift, drag, and pressure distributions. While effective, CFD methods are computationally expensive and time-intensive, limiting their practicality for rapid design iterations. This study explores the integration of machine learning, specifically convolutional neural networks (CNNs), to enhance aerodynamic predictions while significantly reducing computational costs. By training CNN models on datasets derived from CFD simulations of NACA 0024 and 0012 airfoils, this approach enables near-instantaneous predictions of aerodynamic properties, such as lift, drag, and pressure. The study examines the trade-offs between accuracy and efficiency, underscoring how machine learning can complement traditional CFD techniques. While challenges such as data quality and model generalization remain, this research demonstrates the potential of machine learning to streamline airfoil design, making aerodynamic optimization more accessible and efficient.