TY  - JOUR
A1  - Schaffarczyk, Alois
A1  - Koehn, Silas
A1  - Oggiano, Luca
A1  - Schaffarczyk, Kai
T1  - Aerodynamic benefits by optimizing cycling posture
T2  - Applied Sciences
N2  - An approach to aerodynamically optimizing cycling posture and reducing drag in an Ironman (IM) event was elaborated. Therefore, four commonly used positions in cycling were investigated and simulated for a flow velocity of 10 m/s and yaw angles of 0–20° using OpenFoam-based Nabla Flow CFD simulation software software. A cyclist was scanned using an IPhone 12, and a special-purpose meshing software BLENDER was used. Significant differences were observed by changing and optimizing the cyclist’s posture. Aerodynamic drag coefficient (CdA) varies by more than a factor of 2, ranging from 0.214 to 0.450. Within a position, the CdA tends to increase slightly at yaw angles of 5–10° and decrease at higher yaw angles compared to a straight head wind, except for the time trial (TT) position. The results were applied to the IM Hawaii bike course (180 km), estimating a constant power output of 300 W. Including the wind distributions, two different bike split models for performance prediction were applied. Significant time saving of roughly 1 h was found. Finally, a machine learning approach to deduce 3D triangulation for specific body shapes from 2D pictures was tested.
KW  - aerodynamic drag reduction
KW  - cycling
KW  - machine learning
KW  - drag area
Y1  - 2022
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/28594
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-285942
SN  - 2076-3417
VL  - 12
IS  - 17
ER  -