Improving Numerical Estimation of Cyclist Drag Area in Static Conditions Using Unsteady RANS

Abstract

Herein, we compare the drag area estimated using unsteady Reynolds-averaged Navier-Stokes (URANS), using the γ−ReΘ transitional shear stress transport (SST) k−ω (SSTLM) turbulence model with published experimental measurements of a static full-scale cyclist mannequin in an open test section wind tunnel, with the left leg fully extended. The turbulence model employs a local empirical correlation based upon a classical Blasius boundary layer behavior to predict flow transition. For a given mesh density, we aim to improve drag area estimation by modifying the empirical correlation coefficient to better capture actual boundary layer transition location around the arms and legs, to facilitate computationally economical cyclist simulations. Large Eddy Simulation (LES), in conjunction with experimental wake data in the vicinity of the arms and legs, is used to assess boundary layer shape factors, which are related to the empirical coefficient. Overall, the drag area predicted by LES is within 3.7% of the measured results, while the original SSTLM is within 7.8%. By tuning the correlation coefficient, the drag area error is improved to 6.0% at no additional computational cost. The tuning was relatively coarse, and was only considered for the appendages. In other regions, the original SSTLM coefficient seems to perform better, suggesting that local coefficient selection may lead to further improvements in results over the currently employed global value.

Keywords

• transitional turbulence modelling
• wake flow
• drag force
• cycling