An optimization approach for geometry design of multi-axis wave energy converter

Shadmani, A; Nikoo, MR; Gandomi, AH; Chen, M

An optimization approach for geometry design of multi-axis wave energy converter

Shadmani, A Nikoo, MR Gandomi, AH Chen, M

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Energy, 2024, 301
Issue Date:: 2024-08-15

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Field	Value	Language
dc.contributor.author	Shadmani, A
dc.contributor.author	Nikoo, MR
dc.contributor.author	Gandomi, AH
dc.contributor.author	Chen, M
dc.date.accessioned	2025-03-12T02:48:12Z
dc.date.available	2025-03-12T02:48:12Z
dc.date.issued	2024-08-15
dc.identifier.citation	Energy, 2024, 301
dc.identifier.issn	0360-5442
dc.identifier.issn	1873-6785
dc.identifier.uri	http://hdl.handle.net/10453/185714
dc.description.abstract	The current global energy crisis necessitates a shift to renewable energy sources to mitigate climate change impacts. Wave energy emerges as a promising renewable resource to fulfill increasing energy demands. This energy can be extracted using wave energy converters (WECs), with multi-axis WECs (MA-WECs) being more effective than single-axis versions due to their capacity to harness energy from waves in various directions. The challenge lies in determining the ideal geometric design for MA-WECs, that can be tackled through multi-objective optimization (MOO) techniques. This research focuses on evaluating different MOO algorithms for the optimal geometric design of MA-WECs. To assess the structural response of different geometries and sizes, the study utilized the NEMOH boundary element method solver, aiming to maximize power output, lower the levelized cost of energy (LCOE), and optimize the geometry configuration. Findings indicate that the choice of optimization algorithm considerably influences the MA-WEC's optimal design, enhancing power efficiency, reducing device volume, and cutting costs more effectively than the initial design.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Energy
dc.relation.isbasedon	10.1016/j.energy.2024.131714
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0913 Mechanical Engineering, 0914 Resources Engineering and Extractive Metallurgy, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4012 Fluid mechanics and thermal engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	An optimization approach for geometry design of multi-axis wave energy converter
dc.type	Journal Article
utslib.citation.volume	301
utslib.for	0913 Mechanical Engineering
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	closed_access	*
dc.date.updated	2025-03-12T02:48:07Z
pubs.publication-status	Published
pubs.volume	301

Abstract:

The current global energy crisis necessitates a shift to renewable energy sources to mitigate climate change impacts. Wave energy emerges as a promising renewable resource to fulfill increasing energy demands. This energy can be extracted using wave energy converters (WECs), with multi-axis WECs (MA-WECs) being more effective than single-axis versions due to their capacity to harness energy from waves in various directions. The challenge lies in determining the ideal geometric design for MA-WECs, that can be tackled through multi-objective optimization (MOO) techniques. This research focuses on evaluating different MOO algorithms for the optimal geometric design of MA-WECs. To assess the structural response of different geometries and sizes, the study utilized the NEMOH boundary element method solver, aiming to maximize power output, lower the levelized cost of energy (LCOE), and optimize the geometry configuration. Findings indicate that the choice of optimization algorithm considerably influences the MA-WEC's optimal design, enhancing power efficiency, reducing device volume, and cutting costs more effectively than the initial design.

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http://hdl.handle.net/10453/185714