Estimating cycling aerodynamic performance using anthropometric measures

Garimella, Raman; Peeters, Thomas; Parrilla, Eduardo; Uriel, Jordi; Sels, Seppe; Huysmans, T.; Verwulgen, Stijn

doi:10.3390/app10238635

Estimating cycling aerodynamic performance using anthropometric measures

Title

Estimating cycling aerodynamic performance using anthropometric measures

Author

Garimella, Raman (Universiteit Antwerpen; VoxDale)
Peeters, Thomas (Universiteit Antwerpen)
Parrilla, Eduardo (Universitat Politécnica de Valencia)
Uriel, Jordi (Universitat Politécnica de Valencia)
Sels, Seppe (Universiteit Antwerpen)
Huysmans, T. (TU Delft Applied Ergonomics and Design)
Verwulgen, Stijn (Universiteit Antwerpen)

Date

2020

Abstract

Aerodynamic drag force and projected frontal area (A) are commonly used indicators of aerodynamic cycling efficiency. This study investigated the accuracy of estimating these quantities using easy-to-acquire anthropometric and pose measures. In the first part, computational fluid dynamics (CFD) drag force calculations and A (m²) values from photogrammetry methods were compared using predicted 3D cycling models for 10 male amateur cyclists. The shape of the 3D models was predicted using anthropometric measures. Subsequently, the models were reposed from a standing to a cycling pose using joint angle data from an optical motion capture (mocap) system. In the second part, a linear regression analysis was performed to predict A using 26 anthropometric measures combined with joint angle data from two sources (optical and inertial mocap, separately). Drag calculations were strongly correlated with benchmark projected frontal area (coefficient of determination R² = 0.72). A can accurately be predicted using anthropometric data and joint angles from optical mocap (root mean square error (RMSE) = 0.037 m²) or inertial mocap (RMSE = 0.032 m²). This study showed that aerodynamic efficiency can be predicted using anthropometric and joint angle data from commercially available, inexpensive posture tracking methods. The practical relevance for cyclists is to quantify and train posture during cycling for improving aerodynamic efficiency and hence performance.

Subject

3D shape modeling
Aerodynamics
Computational fluid dynamics
Cycling
Inertial sensors
Joint biomechanics
Motion capture system
Projected frontal area

To reference this document use:

http://resolver.tudelft.nl/uuid:f56e014b-08ef-45f1-af7c-acedf55d8d5b

DOI

https://doi.org/10.3390/app10238635

ISSN

2076-3417

Source

Applied Sciences, 10 (23), 1-16

Part of collection

Institutional Repository