Data-Driven Modeling of NACA 0005 Airfoil Aerodynamics Using Neural Networks
Signals and Communication Technology, Springer International Publishing Ag, ss.327-334, 2026
- Yayın Türü: Kitapta Bölüm / Araştırma Kitabı
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/978-3-032-18834-2_22
- Yayınevi: Springer International Publishing Ag
- Sayfa Sayıları: ss.327-334
- Anahtar Kelimeler: Aerodynamic coefficients, Angle of attack, Artificial neural network (ANN), Computational fluid dynamics (CFD), NACA 0005, Reynolds number
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
In this study, a two-layer artificial neural network (ANN) is used to predict the aerodynamic lift and drag coefficients for NACA0005. The first layer has nine neurons, while the number of neurons in the second layer is varied from 1 to 100 to find the best performance. Data from 28 angles of attack ranges 9° ≤ α ≤ 11°, and the Reynolds number between 1000 and 5000 are utilized. The ANN achieved R2 > 0.99 for lift coefficient (Cl) and R2 > 0.96 for drag coefficient (Cd), showing high prediction accuracy. Reynolds number of 1750 and 3000 are evaluated as validation and testing to assess the accuracy of ANN. The overall ANN predicts the computational fluid dynamics results, with errors below 1% for Cl and Cd in the testing phase.