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

Ardalan Javadi,Sean D Peterson,Andrew J M Buckrell

doi:10.3390/proceedings2020049139

Ardalan Javadi, Sean D Peterson + Show 1 more

Open Access

https://doi.org/10.3390/proceedings2020049139

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Publication Date: Jun 15, 2020
Citations: 2	License type: CC BY 4.0

Affiliation: University of Waterloo, Cochrane

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.

Highlights

Resolved computational fluid dynamics (CFD) has gained popularity as a tool for aerodynamic assessment in sport over the past decade or so, with considerable effort focused on cycling [1,2,3]
The experimental value was reduced by 1%, to account for the contribution to the drag of the fixed bars between the force plate and the wheel hub to support the mannequin and bike in the experiments, which is not included in the numerical simulations
For Large Eddy Simulation (LES), two-layer wall modeling (TLM) produces better results in comparison with Spalding’s law, whereas the trend is reversed for unsteady Reynolds-averaged NavierStokes (URANS), with Spalding’s law yielding slightly better results than TLM

Summary

Introduction

Resolved computational fluid dynamics (CFD) has gained popularity as a tool for aerodynamic assessment in sport over the past decade or so, with considerable effort focused on cycling [1,2,3]. CFD affords practitioners insights into the detailed flow features involved in producing observed bulk effects, such as drag. A wide range of small scale surface roughness characteristics and non-linear textile response compound the issues numerically. To this end, considerable effort has been devoted to assessing the performance of various turbulence modeling strategies for cyclist aerodynamics [3,4,5]. Defraeye et al [5] studied various cyclist postures using

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