NEURAL NETWORK FOR PREDICTING AERODYNAMIC COEFFICIENTS OF CROSS-SECTIONS OF AIRCRAFT ENGINE PROPELLER BLADES
Pioquinto J.G. Quijada, O.E. Lukyanov, E.I. Kurkin, V.O. Chertykovtseva, V.H. Hoang, N.V. Shevchenko
Samara National Research University, Samara, Russia
Keywords: CST method, Variational Autoencoder, Multilayer Perceptron, aerodynamic coefficient prediction
Abstract
This paper presents the development of a neural network for predicting airfoil aerodynamic coefficients for use in the isolated-section method for calculating aircraft propeller blade characteristics. A key feature of this neural network is its ability to predict the lift and drag coefficients as functions of the angle of attack and flow Reynolds number in the form of two-dimensional raster images of pixel color distribution. This study aims to accelerate the calculation of airfoil aerodynamic coefficients while maintaining computational accuracy by replacing numerical models with a neural network. This study presents a method for airfoil parameterization, database development, and neural network architecture. The neural network training results and its ability to predict aerodynamic characteristics are presented.
|
