Airfoil sensitivity analysis to establish efficient angles of attack and velocity
Towards a more sustainable wing design
DOI:
https://doi.org/10.58445/rars.1788Keywords:
Airfoil, Sensitivity Analysis, Aviation Industry, Sustainability, Machine Learning, ML, Lift to Drag Ratio, L/DAbstract
Efficient aerodynamics around the airfoil can reduce the financial burden on the aviation industry, thereby aiding in sustainability. This study aimed at performing sensitivity analysis on the NACA0012 airfoil design to identify optimum mesh size, angle of attack (AOA) and velocity for best lift to drag ratio (L/D). Various machine learning (ML) algorithms were subsequently trained to predict L/D using the velocity and AOA. Our results showed that a mesh size of 225 divisions per unit, AOA of 2 degrees, provided the highest L/D. Velocity did not impact the L/D too much. Among ML algorithms, random forest had the highest accuracy in predicting L/D with a RMSE of 0.68. These results provide support to improvements to the airfoil aerodynamics that could potentially contribute positively towards the sustainability efforts of the aviation industry.
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