Practical CFD predictions of a cyclist in a time trial position

Engineering of Sport 15 - Proceedings from the 15th International Conference on the Engineering of Sport (ISEA 2024)

Aerodynamic drag accounts for up to 90% of a cyclist’s resistance with 60‐80% of this coming from the cyclist’s body. Consequently, reducing drag is crucial to maximise speed for a given rider power. There are several methodsto determine drag, including wind tunnels, track and road testing, and Computational Fluid Dynamics (CFD). Wind tunnels are the most accurate for measuring drag force, but do not provide information on flow topology and the drag mechanisms without the use of expensive and complex optical measurement techniques. CFD can predict the entire flow field, is potentially better for knowledge‐based development, and has become popular in recent studies (e.g., Blocken et al.). However, cycling aerodynamics is complex and the effects of surface roughness, boundary layer transition, flow separation and turbulent wakes are challenging to accurately predict. Expectation is that this would require high fidelity computations such as large eddy simulations (LES), but these require expensive high‐performance computing and simulation times of the order of months, both out of reach even for many pro cycling teams . This paper examines if lower fidelity CFD can be used to predict, with sufficient accuracy, the aerodynamic drag of a cyclist within practical timescales on an affordable desktop PC.