By Topic

Aerodynamic optimization design of the aerofoil based on genetic algorithms and neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chen Lihai ; Sch. of Aeroengine & Energy, Northwestern Polytech. Univ., Xi'an, China ; Yang Qingzhen ; Sun Zhiqiang ; Ji Xinjie

Genetic algorithm has a primary disadvantage that computational cost increases greatly for overmuch evaluation of objective functions and their fitness. To improve efficiency of optimization by means of genetic algorithm, an improved method in aerodynamic optimization design of aerofoil is constructed by combining artificial neural network with genetic algorithm. B-Spline method was adopted to parameterize the airfoil, then, followell the uniform experimental design method,with the help of computational program of two-dimensional cascade profile flow field, the distribution of the artificial neural network sample points were founded. Optimize an initial aerofoil by choosing the power coefficient of the curve reference points as optimize variables, and using the lift-drags ratio and change, rate of the aerofoil area as optimization objectives. The examples indicate that the hybrid algorithm is effective and trustiness. It is proved that the improved method is valuable on engineering application.

Published in:

Control Conference (CCC), 2010 29th Chinese

Date of Conference:

29-31 July 2010