# Aerodynamic Benefits by Optimizing Cycling Posture
## 6 Summary and Conclusions  
The overall performance in a bike or triathlon race is highly dependent on the cyclist's posture. Although the positioning on the bike is not static throughout the race, a clear trend is visible. By optimizing the cyclist's posture, depending on the athlete's bio-mechanical flexibility, from a comfortable to an aggressive position, the aerodynamic drag coefficient (CdA) can be reduced significantly by a factor of 2, which may result in substantial time savings of about 20%, or roughly 60 min over the course of IM Hawaii.  
This is also visible in the flow wake structure. For the TT position, the wake is much smoother, whereas in the HH position, it seems to be much more irregular. More extended  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CdA** & **Average** & **Weighted** & **Aerotune** & **BBS** & **Aerotune** & **BBS** \\
**Values** & **CdA [km/h]** & **CdA [km/h]** & **[km/h]** & **[km/h]** & **Deviation** & **Deviation** \\ \hline
0.242 & 42.72 & - & 42.60 & 42.28 & 0.28\% & 1.03\% \\ \hline
0.224 & - & 43.69 & 44.04 & 43.45 & 0.79\% & 0.56\% \\ \hline
0.276 & 41.02 & - & 41.10 & 40.68 & 0.19\% & 0.83\% \\ \hline
0.283 & - & 40.72 & 40.98 & 40.61 & 0.63\% & 0.27\% \\ \hline
0.383 & 37.08 & - & 37.10 & - & 0.05\% & - \\ \hline
0.412 & - & 36.27 & 36.47 & - & 0.56\% & - \\ \hline
0.413 & 36.24 & - & 36.30 & - & 0.17\% & - \\ \hline
0.450 & - & 35.28 & 35.48 & - & 0.55\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicted average speeds and comparison with tools Aerotune and Best Bike Splits.  
areas of reversed flow are accompanied by increased drag. Furthermore, within a position, the CdA tends to increase slightly at crosswind yaw angles of 5-10\({}^{\circ}\) and decrease at higher yaw angles compared to a straight head wind. These findings were observed for the HH, HD and DB positions. While the HH and HD positions showed negative performance in crosswinds, the DB position remains stable across yaw angles. The TT position, however, benefits from crosswinds and takes advantage presumably because of having the arms closer together, and therefore creating a smoother flow wake.  
Our approach showed accurate agreement with prevalent bike split prediction tools, making a simple, mainly open-access-based workflow via iPhone, BLENDER and Nablaflow (AeroCloud) a competitive method for 3D performance analysis of a cyclist's aerodynamics.  
To further simplify and improve the process of geometry acquisition via scanning techniques, a machine learning method for deriving 3D triangulation from 2D pictures was tested. The open-source machine-learning-based tool _ExPose_ was chosen. This approach proved to be feasible, but to improve its accuracy, model training by using more adapted body shapes seems to be necessary.  
Conceptualization, A.S.; methodology, A.S., S.K., L.O. and K.S.; software, S.K., L.O. and K.S.; validation, S.K., K.S. and A.S.; formal analysis, S.K.; investigation, S.K. and K.S.; resources, S.K., L.O. and K.S.; data curation, S.K. and K.S.; writing--original draft preparation, S.K.; writing--review and editing, S.K. and A.S.; visualization, S.K.; supervision, A.S. and L.O.; project administration, A.S.; funding acquisition, no applicable; All authors have read and agreed to the published version of the manuscript.  
This research received only internal funding by Kiel University of Applied Sciences, Kiel, Germany.  
This work was part of a Master of Engineering degree Thesis and formal approval was performed by University's Examination Office.  
Does not apply.  
Data Availability StatementData is available from the corresponding author.  
The Authors declare that there is no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CdA & Drag area = drag coefficient \(\times\) projected area \\ RANS & Reynolds Averaged Navier-Stokes \\ \end{tabular}