Estimating Aerodynamic Data via Supervised Learning

Abstract

Supervised learning extracts a relationship between the input and the output from a training dataset. We consider four models – Support Vector Machine, Random Forest, Gradient Boost, and K-Nearest Neighbor – and employ them on data pertaining to airfoils in two different cases. First, given data about several different airfoil configurations, our objective is to predict the aerodynamic coefficients of a new airfoil at different angles of attack. Second, we seek to investigate how the coefficients can be estimated for a specific airfoil if the Reynolds number dramatically changes. It is our finding that the Random Forest and the Gradient Boost show promising performance in both the scenarios.