Aerodynamic Benefits by Optimizing Cycling Posture
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/17/8475 |
| _version_ | 1797496508047687680 |
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| author | Alois Schaffarczyk Silas Koehn Luca Oggiano Kai Schaffarczyk |
| author_facet | Alois Schaffarczyk Silas Koehn Luca Oggiano Kai Schaffarczyk |
| author_sort | Alois Schaffarczyk |
| collection | DOAJ |
| description | 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 <i>BLENDER</i> 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. |
| first_indexed | 2024-03-10T03:04:39Z |
| format | Article |
| id | doaj.art-5f556d1d036c473c937ce8784ca7048e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:04:39Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5f556d1d036c473c937ce8784ca7048e2023-11-23T12:40:12ZengMDPI AGApplied Sciences2076-34172022-08-011217847510.3390/app12178475Aerodynamic Benefits by Optimizing Cycling PostureAlois Schaffarczyk0Silas Koehn1Luca Oggiano2Kai Schaffarczyk3Mechanical Engineering Department, Kiel University of Applied Sciences, Grenzstr. 3, D-24149 Kiel, GermanyMechanical Engineering Department, Kiel University of Applied Sciences, Grenzstr. 3, D-24149 Kiel, GermanyNablaflow, Løkkeveien 111, N-4007 Stavanger, NorwayInstitute of Computer Science, Julius-Maximilians-Universität Würzburg, D-97070 Würzburg, GermanyAn 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 <i>BLENDER</i> 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.https://www.mdpi.com/2076-3417/12/17/8475aerodynamic drag reductioncyclingmachine learningdrag area |
| spellingShingle | Alois Schaffarczyk Silas Koehn Luca Oggiano Kai Schaffarczyk Aerodynamic Benefits by Optimizing Cycling Posture Applied Sciences aerodynamic drag reduction cycling machine learning drag area |
| title | Aerodynamic Benefits by Optimizing Cycling Posture |
| title_full | Aerodynamic Benefits by Optimizing Cycling Posture |
| title_fullStr | Aerodynamic Benefits by Optimizing Cycling Posture |
| title_full_unstemmed | Aerodynamic Benefits by Optimizing Cycling Posture |
| title_short | Aerodynamic Benefits by Optimizing Cycling Posture |
| title_sort | aerodynamic benefits by optimizing cycling posture |
| topic | aerodynamic drag reduction cycling machine learning drag area |
| url | https://www.mdpi.com/2076-3417/12/17/8475 |
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