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Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning
https://orcid.org/http://orcid.org/0000-0001-7646-6489
https://orcid.org/http://orcid.org/0000-0001-5591-8781
https://orcid.org/http://orcid.org/0000-0001-5204-1910
https://orcid.org/http://orcid.org/0000-0002-6917-5066
Offshore wind turbine monopiles require structural health monitoring throughout their lifespan, yet direct structural measurements are limited. This paper combines numerical modeling and machine learning to present an approach to obtain rapid estimations of monopile fatigue using hourly metocean conditions. Aero-hydro-servo-elastic numerical simulations for a reference turbine provide the meta-model training dataset that encompasses wind-wave conditions applicable to the North Sea. Analysis reveals conditions whereby higher-order fully non-linear wave kinematics produce larger damage values compared to linear waves. This increase in damage is absent when implementing a simple probabilistic data lumping method. The prototype meta-model is developed based on convolutional neural networks to determine the monopile damage from measured wind-wave conditions at high temporal frequency. The proof-of-concept meta-model provides a step-change that demonstrates a promising approach to estimate monopile fatigue accumulation at high temporal resolution with scope for development to specific real-world offshore wind farms where validation data is available.
Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning
https://orcid.org/http://orcid.org/0000-0001-7646-6489
https://orcid.org/http://orcid.org/0000-0001-5591-8781
https://orcid.org/http://orcid.org/0000-0001-5204-1910
https://orcid.org/http://orcid.org/0000-0002-6917-5066
Offshore wind turbine monopiles require structural health monitoring throughout their lifespan, yet direct structural measurements are limited. This paper combines numerical modeling and machine learning to present an approach to obtain rapid estimations of monopile fatigue using hourly metocean conditions. Aero-hydro-servo-elastic numerical simulations for a reference turbine provide the meta-model training dataset that encompasses wind-wave conditions applicable to the North Sea. Analysis reveals conditions whereby higher-order fully non-linear wave kinematics produce larger damage values compared to linear waves. This increase in damage is absent when implementing a simple probabilistic data lumping method. The prototype meta-model is developed based on convolutional neural networks to determine the monopile damage from measured wind-wave conditions at high temporal frequency. The proof-of-concept meta-model provides a step-change that demonstrates a promising approach to estimate monopile fatigue accumulation at high temporal resolution with scope for development to specific real-world offshore wind farms where validation data is available.
Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning
https://orcid.org/http://orcid.org/0000-0001-7646-6489
https://orcid.org/http://orcid.org/0000-0001-5591-8781
https://orcid.org/http://orcid.org/0000-0001-5204-1910
https://orcid.org/http://orcid.org/0000-0002-6917-5066
Offshore wind turbine monopiles require structural health monitoring throughout their lifespan, yet direct structural measurements are limited. This paper combines numerical modeling and machine learning to present an approach to obtain rapid estimations of monopile fatigue using hourly metocean conditions. Aero-hydro-servo-elastic numerical simulations for a reference turbine provide the meta-model training dataset that encompasses wind-wave conditions applicable to the North Sea. Analysis reveals conditions whereby higher-order fully non-linear wave kinematics produce larger damage values compared to linear waves. This increase in damage is absent when implementing a simple probabilistic data lumping method. The prototype meta-model is developed based on convolutional neural networks to determine the monopile damage from measured wind-wave conditions at high temporal frequency. The proof-of-concept meta-model provides a step-change that demonstrates a promising approach to estimate monopile fatigue accumulation at high temporal resolution with scope for development to specific real-world offshore wind farms where validation data is available.
Rapid Assessment of Offshore Monopile Fatigue Using Machine Learning
Lecture Notes in Civil Engineering
Rizzo, Piervincenzo (editor) / Milazzo, Alberto (editor) / Houseago, Robert C. (author) / Mockute, Agota (author) / Cross, Elizabeth J. (author) / Dethlefs, Nina (author)
European Workshop on Structural Health Monitoring ; 2022 ; Palermo, Italy
2022-06-19
12 pages
Article/Chapter (Book)
Electronic Resource
English
Corrosion fatigue crack growth mechanisms in offshore monopile steel weldments
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