The University of Southampton
University of Southampton Institutional Repository

Stochastic optimisation based control of boundary layer transition

Record type: Article

Suction is one of the most promising techniques for delaying the transition from laminar to turbulent flow and hence reducing the drag force on aircraft. However, in order to achieve an overall reduction in energy consumption and thereby operating costs, it is necessary to apply the suction in an efficient if not optimal manner. Here an investigation is conducted into the use of distributed surface suction with multiple suction panels where the suction distribution is optimised to satisfy a desired objective Cost functions based on minimising the total energy consumption of the system are employed. Evolutionary methods, Genetic Algorithms and Simulated Annealing, are used to perform the optimisation. These methods were successful in cases where gradient based search techniques fail. Physically sensible suction distributions were produced. In general, better behaviour was found with the Genetic Algorithm than with Simulated Annealing, which was prone to premature convergence.

Full text not available from this repository.

Citation

MacCormack, W., Tutty, O.R., Rogers, E. and Nelson, P. (2002) Stochastic optimisation based control of boundary layer transition Control Engineering Practice, 10, (3), pp. 243-260. (doi:10.1016/S0967-0661(01)00140-X).

More information

Published date: 2002
Keywords: optimisation, genetic algorithms, simulated annealing, controller design, flow control, transition control
Organisations: Inst. Sound & Vibration Research, Computational Engineering & Design Group, Southampton Wireless Group

Identifiers

Local EPrints ID: 256027
URI: http://eprints.soton.ac.uk/id/eprint/256027
ISSN: 0967-0661
PURE UUID: 7e0a56ca-26c0-4c8f-a5b4-a89d71153627

Catalogue record

Date deposited: 05 Mar 2002
Last modified: 18 Jul 2017 09:49

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

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×