Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration

Kouser T., KURTULUŞ D. F., Goli S., Aliyu A., Imran I. H., Alhems L. M., ...Daha Fazla

IEEE Access, cilt.13, ss.131088-131101, 2025 (SCI-Expanded, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1109/access.2025.3592338
  • Dergi Adı: IEEE Access
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.131088-131101
  • Anahtar Kelimeler: aerodynamic coefficients, angle of attack, artificial neural network (ANN), NACA0005, Reynolds number
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. Data generated by computational fluid dynamics (CFD) is used to train the model for Reynolds numbers Re ∈ [1000 − 5000] and angles of attack ranging from 9° to 11°. A Scaled Conjugate Gradient (SCG) algorithm is employed for efficient training. The ANN has a two-layer architecture, with 9 fixed neurons in the first hidden layer and a varying number of neurons in the second layer to find the best configuration. The model yielded coefficients of determination (R2) of 0.994 (Coefficient of lift (Cl)) and 0.9615 (Coefficient of drag (Cd)) for training, and 0.9563 (Cl) and 0.9085 (Cd) for testing. Overall mean errors are found to be less than 1%. It offers a powerful surrogate modeling approach for aerodynamic studies at ultra-low Reynolds numbers. Moreover, it provides rapid and reliable alternatives to traditional CFD simulations in aerodynamic analysis for unseen cases.