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A Comprehensive Dataset of the Aerodynamic and Geometric Coefficients of Airfoils in the Public Domain

  • Kanak Agarwal
  • , Vedant Vijaykrishnan
  • , Dyutit Mohanty
  • , Manikandan Murugaiah*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 105. 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. Dataset: https://github.com/kanakaero/Dataset-of-Aerodynamic-and-Geometric-Coefficients-of-Airfoils

Original languageEnglish
Article number64
JournalData
Volume9
Issue number5
DOIs
Publication statusPublished - 05-2024

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Information Systems and Management

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