A numerical model for the time-dependent wake of a pedalling cyclist

Martin D. Griffith, Timothy N. Crouch, David Burton, John Sheridan, Nicholas A.T. Brown, Mark C. Thompson

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


A method for computing the wake of a pedalling cyclist is detailed and assessed through comparison with experimental studies. The large-scale time-dependent turbulent flow is simulated using the Scale Adaptive Simulation approach based on the Shear Stress Transport Reynolds-averaged Navier–Stokes model. Importantly, the motion of the legs is modelled by joining the model at the hips and knees and imposing solid body rotation and translation to the lower and upper legs. Rapid distortion of the cyclist geometry during pedalling requires frequent interpolation of the flow solution onto new meshes. The impact of numerical errors, that are inherent to this remeshing technique, on the computed aerodynamic drag force is assessed. The dynamic leg simulation was successful in reproducing the oscillation in the drag force experienced by a rider over the pedalling cycle that results from variations in the large-scale wake flow structure. Aerodynamic drag and streamwise vorticity fields obtained for both static and dynamic leg simulations are compared with similar experimental results across the crank cycle. The new technique presented here for simulating pedalling leg cycling flows offers one pathway for improving the assessment of cycling aerodynamic performance compared to using isolated static leg simulations alone, a practice common in optimising the aerodynamics of cyclists through computational fluid dynamics.

Original languageEnglish
Pages (from-to)514-525
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology
Issue number4
Publication statusPublished - 1 Dec 2019


Dive into the research topics of 'A numerical model for the time-dependent wake of a pedalling cyclist'. Together they form a unique fingerprint.

Cite this