03.4 - Applied Aerodynamics
ABOUT MODELLING OF EMPIRICAL CORRELATIONS WITHIN AERODYNAMIC PROFILES USING HIGHER ORDER ARTIFICIAL NEURAL NETWORKS
P. Kovar, Center of Advanced Aerospace Technology, Czech Republic; J. Fürst, Department of Technical Mathematics, Faculty of Mechanical Engin, Czech Republic
Nowadays, numerical simulations remain time-consuming. Consequently, empirical correlations keep their importance as a valuable tool for compressor design and for estimating flow parameters. This contribution utilizes higher order neural networks to predict flow parameters for new family of airfoils, offering an alternative to time-expensive simulations or inaccurate empirical correlations.