Derivative Free Global Optimisation of CFD Simulations

Morgans, R. C., Doolan, C. J. and Stephens, D. W. (2007). Derivative Free Global Optimisation of CFD Simulations. In: Peter Jacobs, Tim McIntyre, Matthew Cleary, David Buttsworth, David Mee, Rose Clements, Richard Morgan and Charles Lemckert, 16th Australasian Fluid Mechanics Conference (AFMC). , Gold Coast, Queensland, Australia, (1432-1435). 3-7 December, 2007.

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Author Morgans, R. C.
Doolan, C. J.
Stephens, D. W.
Title of paper Derivative Free Global Optimisation of CFD Simulations
Conference location Gold Coast, Queensland, Australia
Conference dates 3-7 December, 2007
Proceedings title 16th Australasian Fluid Mechanics Conference (AFMC)
Place of Publication Brisbane, Australia
Publisher School of Engineering, The University of Queensland
Publication Year 2007
Year available 2007
Sub-type Fully published paper
ISBN 978-1-864998-94-8
Editor Peter Jacobs
Tim McIntyre
Matthew Cleary
David Buttsworth
David Mee
Rose Clements
Richard Morgan
Charles Lemckert
Start page 1432
End page 1435
Total pages 4
Collection year 2007
Language eng
Abstract/Summary This work reports on the use of numerical optimisation techniques to optimise objective functions calculated by Computational Fluid Dynamics (CFD) simulations. Two example applications are described, the first being the shape optimisation of a low speed wind tunnel contraction. A potential flow and viscous flow solver have been coupled to produce a robust computational tool, with the contraction shape defined by a two parameter B´ezier curve. The second application is a simplified test case with a known minimum calculated using a commercial CFD code. For the optimisation of complex CFD simulations, it is sometimes advantageous to use an efficient derivative free global optimisation algorithm because of potentially long simulation times, the objective function may contain multiple local minima and it is often difficult to evaluate analytical or numerical gradients. The Efficient Global Optimisation (EGO) algorithm sequentially samples results from an expensive calculation, does not require derivative information, uses an inexpensive surrogate to search for a global optimum, and is used in this current work. For both applications, the EGO algorithm is able to efficiently and robustly find a global optimum that satisfies any constraints.
Subjects 290501 Mechanical Engineering
290702 Mineral Processing
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Conference Paper
Collection: 16th Australasian Fluid Mechanics Conference
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Created: Thu, 20 Dec 2007, 08:57:14 EST by Laura McTaggart on behalf of School of Engineering