Optimization using surrogate models and partially converged computational fluid dynamics simulations
Optimization using surrogate models and partially converged computational fluid dynamics simulations
Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics results. These allow surrogate models to be built in a fraction of the time required for models based on converged results. The proposed optimization methodologies increase the speed of convergence to a global optimum while the computer resources expended in areas of poor designs are reduced. A strategy which combines a global approximation built using partially converged simulations with expected improvement updates of converged simulations is shown to outperform a traditional surrogate-based optimization.
computational fluid dynamics, data fusion, design of experiment, Kriging
2177-2204
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
6 March 2006
Forrester, A.I.J.
176bf191-3fc2-46b4-80e0-9d9a0cd7a572
Bressloff, N.W.
4f531e64-dbb3-41e3-a5d3-e6a5a7a77c92
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Forrester, A.I.J., Bressloff, N.W. and Keane, A.J.
(2006)
Optimization using surrogate models and partially converged computational fluid dynamics simulations.
Proceedings of the Royal Society A, 462 (2071), .
(doi:10.1098/rspa.2006.1679).
Abstract
Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics results. These allow surrogate models to be built in a fraction of the time required for models based on converged results. The proposed optimization methodologies increase the speed of convergence to a global optimum while the computer resources expended in areas of poor designs are reduced. A strategy which combines a global approximation built using partially converged simulations with expected improvement updates of converged simulations is shown to outperform a traditional surrogate-based optimization.
Text
RSPA20061679p.pdf
- Other
More information
Submitted date: 28 April 2005
Published date: 6 March 2006
Keywords:
computational fluid dynamics, data fusion, design of experiment, Kriging
Identifiers
Local EPrints ID: 23873
URI: http://eprints.soton.ac.uk/id/eprint/23873
ISSN: 1364-5021
PURE UUID: df6922ea-2479-45e6-a44d-d29364f98697
Catalogue record
Date deposited: 14 Mar 2006
Last modified: 16 Mar 2024 02:53
Export record
Altmetrics
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
