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New methodology combining neural network and extended great deluge algorithms for the ATR-42 wing aerodynamics analysis

Published online by Cambridge University Press:  27 May 2016

A. Ben Mosbah ,
R.M. Botez  and
T.-M. Dao
A. Ben Mosbah
Affiliation:
Département de Génie de la Production Automatisée, École de Technologie Supérieure (ÉTS), LARCASE, Montreal, Quebec, Canada
R.M. Botez*
Affiliation:
École de Technologie Supérieure (ÉTS), LARCASE, Montreal, Quebec, Canada
T.-M. Dao
Affiliation:
Département de Génie Mécanique, École de Technologie Supérieure (ÉTS), Montreal, Canada
*
Ruxandra.Botez@etsmtl.ca
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Abstract

The fast determination of aerodynamic parameters such as pressure distributions, lift, drag and moment coefficients from the known airflow conditions (angles of attack, Mach and Reynolds numbers) in real time is still not easily achievable by numerical analysis methods in aerodynamics and aeroelasticity. A flight parameters control system is proposed to solve this problem. This control system is based on new optimisation methodologies using Neural Networks (NNs) and Extended Great Deluge (EGD) algorithms. Validation of these new methodologies is realised by experimental tests using a wing model installed in a wind tunnel and three different transducer systems (a FlowKinetics transducer, an AEROLAB PTA transducer and multitube manometer tubes) to determine the pressure distribution. For lift, drag and moment coefficients, the results of our approach are compared to the XFoil aerodynamics software and the experimental results for different angles of attack and Mach numbers. The main purpose of this new proposed control system is to improve, in this paper, wing aerodynamic performance, and in future to apply it to improve aircraft aerodynamic performance.

Keywords

aerodynamicsneural networkwind-tunnel testsextended great deluge
Type
Research Article
Information
The Aeronautical Journal , Volume 120 , Issue 1229 , July 2016 , pp. 1049 - 1080
Copyright
Copyright © Royal Aeronautical Society 2016 

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