Statistical Analysis on Aerodynamics of Passenger Vehicles

Abstract: This thesis aims to use statistical methods to analyze wind tunnel data generated in automotive aerodynamics testing to understand the properties of aerodynamic force and pressure in a vehicle's working environment. The data used for analysis are visualized, clustered and finally analyzed for Granger causality to see whether a causal link exists between different variables. Then, the pressure measurements taken from the scaled vehicle model is visualized with heat maps and further quantified with K-means and K-medoids clustering. Using the reduced-dimension pressure data derived from cluster analysis, combined with aerodynamic force data, a VAR model is fitted, and the causal relationships between the variables in the data set is explored using Granger causality testing.