Version 1
: Received: 8 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (07:55:22 CET)
Version 2
: Received: 17 March 2024 / Approved: 18 March 2024 / Online: 19 March 2024 (01:27:39 CET)
How to cite:
Agarwal, K.; Vijaykrishnan, V.; Mohanty, D.; Murugaiah, M. A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain. Preprints2024, 2024030896. https://doi.org/10.20944/preprints202403.0896.v2
Agarwal, K.; Vijaykrishnan, V.; Mohanty, D.; Murugaiah, M. A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain. Preprints 2024, 2024030896. https://doi.org/10.20944/preprints202403.0896.v2
Agarwal, K.; Vijaykrishnan, V.; Mohanty, D.; Murugaiah, M. A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain. Preprints2024, 2024030896. https://doi.org/10.20944/preprints202403.0896.v2
APA Style
Agarwal, K., Vijaykrishnan, V., Mohanty, D., & Murugaiah, M. (2024). A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain. Preprints. https://doi.org/10.20944/preprints202403.0896.v2
Chicago/Turabian Style
Agarwal, K., Dyutit Mohanty and Manikandan Murugaiah. 2024 "A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain" Preprints. https://doi.org/10.20944/preprints202403.0896.v2
Abstract
This study presents an extensive collection of data on the aerodynamic behavior at a low Reynolds number and geometric coefficients for 2900 airfoils obtained through the class shape transformation (CST) method. By employing a verified OpenFOAM-based CFD simulation framework, lift and drag coefficients were determined at a Reynolds number of 10^5. Considering the limited availability of data on low Reynolds number airfoils, this dataset is invaluable for a wide range of applications, including unmanned aerial vehicles (UAVs) and wind turbines. Additionally, the study offers a method for automating CFD simulations that could be applied to obtain aerodynamic coefficients at higher Reynolds numbers. The breadth of this dataset also supports the enhancement and creation of machine learning (ML) models, further advancing research into the aerodynamics of airfoils and lifting surfaces.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
