The University of Southampton
University of Southampton Institutional Repository

Automated optimization system for aircraft wing design

Automated optimization system for aircraft wing design
Automated optimization system for aircraft wing design
The optimization of a transonic civil transport aircraft is a complex and difficult task, due to the complexity of the cost surfaces and the human expertise that is necessary in order to achieve high quality results. In this paper we describe an Automated Optimization System that provide recommendations to design engineers on the choice of optimization search technique to sue, especially when searching within familiar domains. The Automated optimization System uses Artificial Intelligence, specifically machine learning techniques to perform knowledge discovery from past optimization search and reuses this knowledge to facilitate intelligent recommendations for search routines selection. Results of a case study on the design of a transonic civil transport aircraft wing using the Automated Optimization System are presented in the paper. It is shown that the Automated Optimization System not only aids design engineers to make improved decisions when working on complex aircraft wing but also helps improve design search performance.
Centre for Computational Intelligence, University of Essex
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def

Ong, Y.S. and Keane, A.J. (2002) Automated optimization system for aircraft wing design. In Proceedings of 7th International Conference on Artificial Intelligence in Design, Cambridge, UK, 13-17 Jul 2002. Centre for Computational Intelligence, University of Essex. 4 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

The optimization of a transonic civil transport aircraft is a complex and difficult task, due to the complexity of the cost surfaces and the human expertise that is necessary in order to achieve high quality results. In this paper we describe an Automated Optimization System that provide recommendations to design engineers on the choice of optimization search technique to sue, especially when searching within familiar domains. The Automated optimization System uses Artificial Intelligence, specifically machine learning techniques to perform knowledge discovery from past optimization search and reuses this knowledge to facilitate intelligent recommendations for search routines selection. Results of a case study on the design of a transonic civil transport aircraft wing using the Automated Optimization System are presented in the paper. It is shown that the Automated Optimization System not only aids design engineers to make improved decisions when working on complex aircraft wing but also helps improve design search performance.

Text
ong_02b.pdf - Accepted Manuscript
Download (2MB)

More information

Published date: 2002
Additional Information: CD-Rom
Venue - Dates: conference; 2002-01-01, 2002-01-01

Identifiers

Local EPrints ID: 22261
URI: http://eprints.soton.ac.uk/id/eprint/22261
PURE UUID: 71c93463-ef28-419b-b2ed-6fbaff32d7e9
ORCID for A.J. Keane: ORCID iD orcid.org/0000-0001-7993-1569

Catalogue record

Date deposited: 11 Jul 2006
Last modified: 16 Mar 2024 02:53

Export record

Contributors

Author: Y.S. Ong
Author: A.J. Keane ORCID iD

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.

×