@article{SchaffarczykKoehnOggianoetal.2022,
  author    = {Schaffarczyk, Alois and Koehn, Silas and Oggiano, Luca and Schaffarczyk, Kai},
  title     = {Aerodynamic benefits by optimizing cycling posture},
  series = {Applied Sciences},
  volume    = {12},
  journal   = {Applied Sciences},
  number    = {17},
  issn      = {2076-3417},
  doi       = {10.3390/app12178475},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285942},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}