A review of challenges and framework development for corrosion fatigue life assessment of monopile-supported horizontal-axis offshore wind turbines

Digital tools such as machine learning and the digital twins are emerging in asset management of offshore wind structures to address their structural integrity and cost challenges due to manual inspections and remote sites of offshore wind farms. The corrosive offshore environments and salt-water ef...

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Bibliographic Details
Published inShips and offshore structures Vol. 19; no. 1; pp. 1 - 15
Main Authors Okenyi, Victor, Bodaghi, Mahdi, Mansfield, Neil, Afazov, Shukri, Siegkas, Petros
Format Journal Article
LanguageEnglish
Published Cambridge Taylor & Francis 02.01.2024
Taylor & Francis Ltd
Subjects
artificial neural network
Artificial neural networks
Asset management
condition monitoring
Corrosion
Corrosion fatigue
Corrosion tests
digital twin
Digital twins
fatigue
Fatigue failure
Fatigue life assessment
Finite element method
Inspection
Internet of Things
Life assessment
Machine learning
Offshore
Offshore energy sources
Offshore structures
Offshore wind turbine
Saline water
Smart sensors
Structural integrity
Turbine engines
Turbines
Wind farms
Wind power
Wind turbines
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Summary:Digital tools such as machine learning and the digital twins are emerging in asset management of offshore wind structures to address their structural integrity and cost challenges due to manual inspections and remote sites of offshore wind farms. The corrosive offshore environments and salt-water effects further increase the risk of fatigue failures in offshore wind turbines. This paper presents a review of corrosion fatigue research in horizontal-axis offshore wind turbines (HAOWT) support structures, including the current trends in using digital tools that address the current state of asset integrity monitoring. Based on the conducted review, it has been found that digital twins incorporating finite element analysis, material characterisation and modelling, machine learning using artificial neural networks, data analytics, and internet of things (IoT) using smart sensor technologies, can be enablers for tackling the challenges in corrosion fatigue (CF) assessment of offshore wind turbines in shallow and deep waters.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1744-5302
1754-212X
DOI:10.1080/17445302.2022.2140531