Wing-fuselage drag prediction using artificial neural networks

Abstract

The conceptual design of an aircraft involves several multidisciplinary analisys in order to reach the optimum configuration according to market and certification requirements. An application is being developed in the Technological Institute of Aeronautics aiming to group such analisys in a single interface. Previously, aerodynamic drag was calculated through semi-analytical methods and empirical data interpolations, demanding strong computational resources, which became critical in optimizations processes. In this work, an artifical neural network was developed to replace the old aerodynamic module. This neural network was trained with a database containing more than one hundred thousand configurations, which were previously analysed with the BLWF 28. The metamodel gave acurrate results with low computational costs, thus attaining the required performance for multidisciplinary optimizations. Copyright © 2012 by the American Institute of Aeronautics and Astronautics, Inc.