Application of Kriging to Motorsport Aerodynamic Analysis

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volume 553)
• Pages: 217-222
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.553.217
• Online since: May 2014
• Authors: Matthew Cruickshank*, Graham Doig
• Keywords: Aerodynamic Correlation, CFD, Kriging, Motorsport Aerodynamics, Wind Tunnel

References

[1] W. Toet, Aerodynamics and aerodynamic research in Formula 1, Aeronautical Journal, vol. 117, pp.1-26, (2013).

[2] N. Petrone, M. Capuzzo, E. De Paoli, and N. Biliato. (2002). Acquisition and Analysis of Aerodynamic Loads on Formula 3 Racing Car Wings using Dynamometric Load Cells.

DOI: 10.4271/2002-01-3331

[3] D. G. Krige, A statistical approach to some mine valuation and allied problems on the Witwatersrand, University of the Witwatersrand, (1951).

[4] G. Matheron, Les variables régionalisées et leur estimation: Paris, (1965).

[5] I. Watson, Spatial Verification and Validation of Datasets in Fluid Dynamics, PhD, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney Australia, (2009).

[6] J. Laurenceau and P. Sagaut, Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging, AIAA Journal, vol. 46, pp.498-507, (2008).

DOI: 10.2514/1.32308

[7] A. R. Crowell and J. J. McNamara, Model Reduction of Computational Aerothermodynamics for Hypersonic Aerothermoelasticity, AIAA Journal, vol. 50, pp.74-84, (2012).

DOI: 10.2514/1.j051094

[8] Z. Han, R. Zimmerman, and S. Görtz, Alternative Cokriging Method for Variable-Fidelity Surrogate Modeling, AIAA Journal, vol. 50, pp.1205-1210, (2012).

DOI: 10.2514/1.j051243

[9] Z. -H. Han and S. Görtz, Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling, AIAA Journal, vol. 50, pp.1885-1896, (2012).

DOI: 10.2514/1.j051354

[10] G. Doig and T. J. Barber, Considerations for Numerical Modeling of Inverted Wings in Ground Effect, AIAA Journal, vol. 49, pp.2330-2333, (2011).

DOI: 10.2514/1.j051273

[11] S. Diasinos, The aerodynamic interection of a rotating wheel and downforce producing wing in ground effect, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia, (2009).

[12] J. Keogh, Compressibility Effects Around an Open-Wheeled Race-Car, B. Eng, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney Australia, (2012).

[13] G. Doig, T. J. Barber, and A. J. Neely, The influence of compressibility on the aerodynamics of an inverted wing in ground effect, Journal of fluids engineering, vol. 133, (2011).

DOI: 10.1115/1.4004084

[14] Fluent 12. 0 User Guide: ANSYS Inc, (2009).

[15] D. Nowlan, Refining aeromaps from pitch data, Racecar Engineering, vol. March, (2013).

[16] E. H. Isaaks and R. M. Srivastava, Applied geostatistics: Oxford University Press, (1989).