TY  - JOUR
A1  - Schaffarczyk, Alois
A1  - Koehn, Silas
A1  - Oggiano, Luca
A1  - Schaffarczyk, Kai
T1  - Aerodynamic benefits by optimizing cycling posture
JF  - 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
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285942
SN  - 2076-3417
VL  - 12
IS  - 17
ER  - 
TY  - JOUR
A1  - McIlroy, Benjamin
A1  - Passfield, Louis
A1  - Holmberg, Hans-Christer
A1  - Sperlich, Billy
T1  - Virtual training of endurance cycling – A summary of strengths, weaknesses, opportunities and threats
JF  - Frontiers in Sports and Active Living
N2  - Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and “e-coaching.” Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.
KW  - algorithms
KW  - cycling
KW  - e-coach
KW  - e-health
KW  - ergometer
KW  - simulation
KW  - virtual training
KW  - SWOT
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-258876
VL  - 3
ER  -