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.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-510323 |
| Date | January 2023 |
| Creators | Peng, Dingkang |
| Publisher | Uppsala universitet, Sannolikhetsteori och kombinatorik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | U.U.D.M. project report ; 2023:32 |
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