We propose a hybrid approach based on meta-modelling techniques and machine-learning algorithms to determine the best car configuration for each circuit. By a specific interpolation model, we obtain an accurate estimation of the car’s speed as a function of the front wing configuration and the bend curvature. Some high-fidelity fluid dynamic simulations train the model and extend it to the entire design space. These data are then used as input for a simplified car dynamics model, providing an accurate estimate of the ideal lap time. Comparison with actual telemetry data confirms that the resulting tool is reliable, fast and easy to use.
DiasinosSDoigGBarberTJ. On the interaction of a racing car front wing and exposed wheel. Aeronaut J2014; 118(1210): 1385–1407.
2.
KeoghJBarberTDiasinosS, et al. Techniques for aerodynamic analysis of cornering vehicles. SAE technical paper 2015-01-0022, 2015.
3.
KeoghJDoigGDiasinosS, et al. The influence of cornering on the vortical wake structures of an inverted wing. Proc IMechE Part D: J Automobile Engineering2015; 229(13): 1817–1829.
4.
KeoghJBarberTDiasinosS, et al. The aerodynamic effects on a cornering Ahmed body. J Wind Eng Ind Aerodyn2016; 154: 34–46.
5.
CarlinoGCogottiA. Simulation of transient phenomena with the turbulence generation system in the pininfarina wind tunnel. SAE technical paper 2006-01-1031, 2006.
6.
JosefssonEHagvallRUrquhartM, et al. Numerical analysis of aerodynamic impact on passenger vehicles during cornering. SAE technical paper 2018-37-0014, 2018.
7.
NakashimaTMutsudaHKanehiraT, et al. Fluid-dynamic force measurement of Ahmed model in steady-state cornering. Energies2020; 13(24): 6592.
8.
PatelDGarmoryAPassmoreM. The effect of cornering on the aerodynamics of a multi-element wing in ground effect. Fluids2021; 6(1): 3–24.
9.
BorsboomOFahdzyanaCAHofmanT, et al. A convex optimization framework for minimum lap time design and control of electric race cars. IEEE Trans Veh Technol2021; 70(9): 8478–8489.
MenterFRKuntzMLangtryR. Ten years of industrial experience with the SST turbulence model. In: Proceedings of the fourth international symposium on turbulence, heat and mass transfer, Antalya, Turkey, 12–17 October 2003, pp.625–632. Redding, CT: Begell House.
12.
RoachePJ. Quantification of uncertainty in computational fluid dynamics. Annu Rev Fluid Mech1997; 29(1): 123–160.
13.
RaheemATSapitAMohammedAN. Aerodynamics of a Formula One car front cascade wing during cornering. J Adv Res Fluid Mech Therm Sci2019; 1: 53–60.
14.
KeoghJDoigGBarberTJ, et al. The aerodynamics of a cornering inverted wing in ground effect. Appl Mech Mater2014; 553: 205–210.
15.
KeoghJBarberTDiasinosS, et al. A new type of wind tunnel for the evaluation of curved motion. In: 54th AIAA aerospace sciences meeting, AIAA 2016-1774, San Diego, CA, USA, 4–8 January 2016.
16.
MatheronG. Principles of geostatistics. Econ Geol1963; 58(8): 1246–1266.
17.
NielsenHBLophavenSNSøndergaardJ. DACE - a Matlab Kriging toolbox. Informatics and Mathematical Modelling, Technical University of Denmark, DTU, 2002.
18.
PeriD. Self-learning metamodels for optimization. Ship Technol Res2009; 56(3): 95–109.
19.
HabermannCKindermannF. Multidimensional spline interpolation: theory and applications. Comput Econ2007; 30: 153–169.
20.
PeriD. Easy-to-implement multidimensional spline interpolation with application to ship design optimisation. Ship Technol Res2018; 65(1): 32–46.
21.
PeriD. Machine learning algorithms in design optimization. arXiv:2203.11005v22021.
22.
KieferJ. Sequential minimax search for a maximum. Proc Am Math Soc1953; 4(3): 502–506.
23.
DiasinosSGattoA. Experimental investigation into wing span and angle-of-attack effects on sub-scale race car wing/wheel interaction aerodynamics. Exp Fluids2008; 45(3): 537–546.
24.
TaherkhaniARGilkesonCGaskellP, et al. Aerodynamic CFD based optimization of police car using Bezier curves. SAE Int J Mater Manuf2017; 10(2): 85–93.
