On a flat road, at race speeds, aerodynamic drag is the main resistive force a cyclist must overcome. Computational fluid dynamics (CFD) can be a useful tool to predict and understand the complex flow and, therefore, drive developments to reduce drag. However, cycling aerodynamics is complex. The effects of Reynolds number, surface roughness, boundary layer transition, flow separation, and turbulent wakes are challenging to accurately predict. High fidelity time-resolved computations, such as Large eddy simulations (LES), require high-performance computing and lengthy simulation times. This paper examines whether lower fidelity CFD, such as Reynolds averaged approaches, can predict the drag of a cyclist with sufficient accuracy and within practical timescales on a desktop PC. Wind tunnel tests of a rider model (without bicycle) were conducted at Reynolds numbers equivalent to speeds of ~ 20-70 km/h. Measured drag showed a notable Reynolds number dependency with the drag coefficient reducing almost linearly by ~ 20% from 0.88 to 0.71. The computational accurately replicated this relationship but only when employing a boundary layer transition model. The steady computations underpredicted the magnitude of the measured drag coefficient by ~ 3% but the unsteady computations were within ~ 2%. Examination of the predicted flow field revealed variations in boundary layer transition, separation, and wake formation from each body part which combine in a complex wake system. Overall, the data confirm validity and suitable accuracy of the CFD, and therefore this provides a practical time and cost-effective tool for further examination of drag reduction within cycling.
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