University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization

Wang, Handing, Doherty, John and Jin, Yaochu (2018) Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization In: 2018 IEEE World Congress on Computational Intelligence (WCCI 2018), 08-13 Jul 2018, Windsor Convention Centre, Rio de Janeiro, Brazil.

[img]
Preview
Text
Hierarchical surrogate-assisted evolutionary multi-scenario airfoil shape optimization.pdf - Accepted version Manuscript

Download (1MB) | Preview

Abstract

For multi-scenario airfoil shape optimization problems, an evaluation of a single airfoil is based on its full-scenario drag landscape. To obtain the full-scenario drag landscape, a large number of computational fluid dynamic simulations for different operating conditions must be conducted. Since a single computational fluid dynamic simulation is often time-consuming, evaluations for multi-scenario airfoil shape optimization will be computationally highly intensive. Although surrogate-assisted evolutionary algorithms have been widely applied to expensive optimization problems, existing surrogate-assisted evolutionary algorithms cannot be directly applied to multi-scenario airfoil shape optimization. Instead of using surrogate models to directly approximate the multi-scenario evaluations, we employ a hierarchical surrogate model consisting of a K-nearest neighbors classifier and a Kriging model to approximate the full-scenario drag landscape for each candidate design during the optimization. Then, the fitness of the candidate design is evaluated based on the approximated drag landscape to reduce the computational cost. The proposed hierarchical surrogate model is embedded in the covariance matrix adaptation evolution strategy and applied to the RAE2822 airfoil design problem. Our experimental results show that the proposed algorithm is able to obtain an airfoil design with limited computational cost that perform well in different operating conditions.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Wang, Handinghanding.wang@surrey.ac.uk
Doherty, Johnjohn.doherty@surrey.ac.uk
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 4 October 2018
DOI : 10.1109/CEC.2018.8477766
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Related URLs :
Depositing User : Clive Harris
Date Deposited : 13 Aug 2018 09:29
Last Modified : 16 Nov 2018 09:58
URI: http://epubs.surrey.ac.uk/id/eprint/848903

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800