Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow

The stability-control of a fluid-loaded flexible panel has been studied to determine the effectiveness of adding localized stiffening to control or postpone instability. In our previous work for the 2-D system a stabilisation strategy has been demonstrated by localised stiffening with a spring suppo...

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Main Authors: Tan, Ben Hoea, Lucey, Anthony, Howell, Richard
Format: Book Chapter
Published: Springer 2016
Online Access:http://hdl.handle.net/20.500.11937/4526
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author Tan, Ben Hoea
Lucey, Anthony
Howell, Richard
author_facet Tan, Ben Hoea
Lucey, Anthony
Howell, Richard
author_sort Tan, Ben Hoea
building Curtin Institutional Repository
collection Online Access
description The stability-control of a fluid-loaded flexible panel has been studied to determine the effectiveness of adding localized stiffening to control or postpone instability. In our previous work for the 2-D system a stabilisation strategy has been demonstrated by localised stiffening with a spring support. Similarly for the 3-D system, the effectiveness of the stabilisation method has also been shown by adding a transverse or streamwise stiffening strip. The most important goal for such stabilisation methods, for both 2-D and 3-D systems, is to refine the localised stiffening strategy to achieve the best use of multiple springs and stiffeners. In this paper we build upon our previous 2-D and 3-D work to develop and apply multi-objective genetic algorithm tools that are able to optimise the stabilisation strategy of added localised stiffness for different design problems: full solution spaces are presented for these problems from which optimal points are readily located.
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institution Curtin University Malaysia
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publishDate 2016
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spelling curtin-20.500.11937-45262017-09-13T14:44:36Z Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow Tan, Ben Hoea Lucey, Anthony Howell, Richard The stability-control of a fluid-loaded flexible panel has been studied to determine the effectiveness of adding localized stiffening to control or postpone instability. In our previous work for the 2-D system a stabilisation strategy has been demonstrated by localised stiffening with a spring support. Similarly for the 3-D system, the effectiveness of the stabilisation method has also been shown by adding a transverse or streamwise stiffening strip. The most important goal for such stabilisation methods, for both 2-D and 3-D systems, is to refine the localised stiffening strategy to achieve the best use of multiple springs and stiffeners. In this paper we build upon our previous 2-D and 3-D work to develop and apply multi-objective genetic algorithm tools that are able to optimise the stabilisation strategy of added localised stiffness for different design problems: full solution spaces are presented for these problems from which optimal points are readily located. 2016 Book Chapter http://hdl.handle.net/20.500.11937/4526 10.1007/978-3-662-48868-3_55 Springer restricted
spellingShingle Tan, Ben Hoea
Lucey, Anthony
Howell, Richard
Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title_full Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title_fullStr Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title_full_unstemmed Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title_short Application of a Multi-objective Genetic Algorithm in a Stabilisation Strategy for Flexible Panels in a Mean Flow
title_sort application of a multi-objective genetic algorithm in a stabilisation strategy for flexible panels in a mean flow
url http://hdl.handle.net/20.500.11937/4526