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Geometric filtration using proper orthogonal decomposition for aerodynamic design optimization

Geometric filtration using proper orthogonal decomposition for aerodynamic design optimization
Geometric filtration using proper orthogonal decomposition for aerodynamic design optimization
When carrying out design searches, traditional variable screening techniques can find it extremely difficult to distinguish between important and unimportant variables. This is particularly true when only a small number of simulations is combined with a parameterization which results in a large number of variables of seemingly equal importance. Here the authors present a variable reduction technique which employs proper orthogonal decomposition to filter out undesirable or badly performing geometries from an optimization process. Unlike traditional screening techniques, the presented method operates at the geometric level instead of the variable level. The filtering process uses the designs which result from a geometry parameterization instead of the variables which control the parameterization. The method is shown to perform well in the optimization of a two dimensional airfoil for the minimization of drag to lift ratio, producing designs better than those resulting from traditional kriging based surrogate model optimization and with a significant reduction in surrogate tuning cost
0001-1452
916-928
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Holden, C.M.E
294336fa-a085-46a1-82dc-4caa78db4439
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Holden, C.M.E
294336fa-a085-46a1-82dc-4caa78db4439

Toal, David J.J., Bressloff, N.W., Keane, A.J. and Holden, C.M.E (2010) Geometric filtration using proper orthogonal decomposition for aerodynamic design optimization. AIAA Journal, 48 (5), 916-928. (doi:10.2514/1.41420).

Record type: Article

Abstract

When carrying out design searches, traditional variable screening techniques can find it extremely difficult to distinguish between important and unimportant variables. This is particularly true when only a small number of simulations is combined with a parameterization which results in a large number of variables of seemingly equal importance. Here the authors present a variable reduction technique which employs proper orthogonal decomposition to filter out undesirable or badly performing geometries from an optimization process. Unlike traditional screening techniques, the presented method operates at the geometric level instead of the variable level. The filtering process uses the designs which result from a geometry parameterization instead of the variables which control the parameterization. The method is shown to perform well in the optimization of a two dimensional airfoil for the minimization of drag to lift ratio, producing designs better than those resulting from traditional kriging based surrogate model optimization and with a significant reduction in surrogate tuning cost

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More information

Submitted date: 22 April 2010
Published date: 1 May 2010
Organisations: Computational Engineering and Design, Faculty of Engineering and the Environment

Identifiers

Local EPrints ID: 146793
URI: http://eprints.soton.ac.uk/id/eprint/146793
ISSN: 0001-1452
PURE UUID: 6806608b-2508-4552-8cd2-5e370964bf83
ORCID for David J.J. Toal: ORCID iD orcid.org/0000-0002-2203-0302

Catalogue record

Date deposited: 22 Apr 2010 13:52
Last modified: 10 Dec 2019 01:42

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