Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Effects of ice geometry on airfoil performance using neural networks prediction

Effects of ice geometry on airfoil performance using neural networks prediction Purpose – The purpose of this paper is to describe a methodology for predicting the effects of glaze ice geometry on airfoil aerodynamic coefficients by using neural network (NN) prediction. Effects of icing on angle of attack stall are also discussed. Design/methodology/approach – The typical glaze ice geometry covers ice horn leading‐edge radius, ice height, and ice horn position on airfoil surface. By using artificial NN technique, several NNs are developed to study the correlations between ice geometry parameters and airfoil aerodynamic coefficients. Effects of ice geometry on airfoil hinge moment coefficient are also obtained to predict the angle of attack stall. Findings – NN prediction is feasible and effective to study the effects of ice geometry on airfoil performance. The ice horn location and height, which have a more evident and serious effect on airfoil performance than ice horn leading‐edge radius, are inversely proportional to the maximum lift coefficient. Ice accretions on the after‐location of the upper surface of the airfoil leading edges have the most critical effects on the airfoil performance degradation. The catastrophe of hinge moment and unstable hinge moment coefficient can be used to predict the stall effectively. Practical implications – Since the simulation results of NNs are shown to have high coherence with the tunnel test data, it can be further used to predict coefficients at non‐experimental conditions. Originality/value – The simulation method by using NNs here can lay the foundation of the further research about the airfoil performance in different ice cloud conditions through predicting the relations between the ice cloud conditions and ice geometry. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aircraft Engineering and Aerospace Technology Emerald Publishing

Effects of ice geometry on airfoil performance using neural networks prediction

Loading next page...
 
/lp/emerald-publishing/effects-of-ice-geometry-on-airfoil-performance-using-neural-networks-YEfNshnYK0
Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
0002-2667
DOI
10.1108/00022661111159870
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to describe a methodology for predicting the effects of glaze ice geometry on airfoil aerodynamic coefficients by using neural network (NN) prediction. Effects of icing on angle of attack stall are also discussed. Design/methodology/approach – The typical glaze ice geometry covers ice horn leading‐edge radius, ice height, and ice horn position on airfoil surface. By using artificial NN technique, several NNs are developed to study the correlations between ice geometry parameters and airfoil aerodynamic coefficients. Effects of ice geometry on airfoil hinge moment coefficient are also obtained to predict the angle of attack stall. Findings – NN prediction is feasible and effective to study the effects of ice geometry on airfoil performance. The ice horn location and height, which have a more evident and serious effect on airfoil performance than ice horn leading‐edge radius, are inversely proportional to the maximum lift coefficient. Ice accretions on the after‐location of the upper surface of the airfoil leading edges have the most critical effects on the airfoil performance degradation. The catastrophe of hinge moment and unstable hinge moment coefficient can be used to predict the stall effectively. Practical implications – Since the simulation results of NNs are shown to have high coherence with the tunnel test data, it can be further used to predict coefficients at non‐experimental conditions. Originality/value – The simulation method by using NNs here can lay the foundation of the further research about the airfoil performance in different ice cloud conditions through predicting the relations between the ice cloud conditions and ice geometry.

Journal

Aircraft Engineering and Aerospace TechnologyEmerald Publishing

Published: Sep 6, 2011

Keywords: Neural network; Aerodynamics; Ice accretion; Stall; Fluid dynamics

References