ARTICLE
Feed-forward artificial neural network as surrogate model to predict lift and drag coefficient of NACA airfoil and searching of maximum lift-to-drag ratio
1
Military University of Technology, Faculty of Mechatronics, Armament and Aerospace, Warsaw, Poland
Submission date: 2023-10-31
Final revision date: 2023-12-20
Acceptance date: 2024-06-07
Online publication date: 2024-07-31
Publication date: 2024-07-31
Corresponding author
Borys Syta
Faculty of Mechatronics, Armament and Aerospace, Institute of Aviation Technology, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908, Warsaw, Poland
Faculty of Mechatronics, Armament and Aerospace, Institute of Aviation Technology, Military University of Technology, gen. Sylwestra Kaliskiego 2, 00-908, Warsaw, Poland
KEYWORDS
TOPICS
ABSTRACT
The problem of computation time in numerical calculations of aerodynamics has been studied
by many research centres. In this work, a feed forward artificial neural network (FF-ANN)
was used to determine the dependence of lift and drag coefficients on the angle of attack for
NACA four-digit families. A panel method was used to generate the data needed to train
the FF-ANNs. Optimisation using a genetic algorithm and a neural metamodel resulted
in a non-standard NACA aerofoil for which the optimal angle of attack was determined
with a maximum L/D ratio. The optimisation results were validated using the finite volume
method.
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