Aerodynamic analysis in motorsport is conducted using three methods, computational, scaled experimental and full-scale operational. However, the varying fidelity, different sampling resolutions and unavoidable errors of each technique make valid comparisons between datasets from each method difficult and time consuming. Kriging is a geostatistical method to estimate values within a data field by examining and applying the trends of the dataset. This research examines how such techniques can be used to aid comparison between aerodynamic measurements of a race car. It examines how kriging can be used to transform discrete measurements, of varying fidelity and sampling resolution, into semi-continuous measurements, thus allowing computational results to be compared across a wider range of conditions than initially tested. This work explores how kriging can allow the trends from highly sampled data, such as track running, to be applied to less sampled data, such as CFD to improve computational and overall aerodynamic analysis.


Aerospace Engineering



URL: https://digitalcommons.calpoly.edu/aero_fac/140