Development of a Genetic Algorithm Code for the Design of Cylindrical Buoyancy Bodies for Floating Offshore Wind Turbine Substructures
The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for Floating Offshore Wind Turbine substructures. T...
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| Published in | Journal of physics. Conference series Vol. 2265; no. 4; pp. 42019 - 42030 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Bristol
IOP Publishing
01.05.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1742-6588 1742-6596 1742-6596 |
| DOI | 10.1088/1742-6596/2265/4/042019 |
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| Summary: | The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for Floating Offshore Wind Turbine substructures. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework using a particular Genetic Algorithm is developed to parameterize the cylindrical geometry of the buoyancy body and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. In this paper, the specific characteristics of this particular buoyancy body are highlighted and the implementation of the optimization process is detailed. Preliminary results for a given study case are presented and discussed before pointing out potential cost reductions and future improvements to be made with regards to the presented optimization work. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1742-6588 1742-6596 1742-6596 |
| DOI: | 10.1088/1742-6596/2265/4/042019 |