Version 1
: Received: 29 July 2024 / Approved: 30 July 2024 / Online: 30 July 2024 (10:28:17 CEST)
How to cite:
Medina-Manuel, A.; Molina Sánchez, R.; Souto-Iglesias, A. AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind. Preprints2024, 2024072362. https://doi.org/10.20944/preprints202407.2362.v1
Medina-Manuel, A.; Molina Sánchez, R.; Souto-Iglesias, A. AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind. Preprints 2024, 2024072362. https://doi.org/10.20944/preprints202407.2362.v1
Medina-Manuel, A.; Molina Sánchez, R.; Souto-Iglesias, A. AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind. Preprints2024, 2024072362. https://doi.org/10.20944/preprints202407.2362.v1
APA Style
Medina-Manuel, A., Molina Sánchez, R., & Souto-Iglesias, A. (2024). AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind. Preprints. https://doi.org/10.20944/preprints202407.2362.v1
Chicago/Turabian Style
Medina-Manuel, A., Rafael Molina Sánchez and Antonio Souto-Iglesias. 2024 "AI-Driven Model Prediction of Motions and Mooring Loads of a Spar Floating Wind Turbine in Waves and Wind" Preprints. https://doi.org/10.20944/preprints202407.2362.v1
Abstract
This paper describes a Long Short-Term Memory (LSTM) neural network model used to simulate the dynamics of the OC3 reference design of a Floating Offshore Wind Turbine Spar unit. It crafts an advanced neural network with an encoder-decoder architecture capable of predicting the Spar’s motion and fairlead tensions time series. These predictions are based on wind and wave excitations across various operational and extreme conditions. The LSTM network, trained on an extensive dataset from over 300 fully coupled simulation scenarios using OpenFAST, ensures a robust framework that captures the complex dynamics of floating platform under diverse environmental scenarios. This framework’s effectiveness is further verified by thoroughly evaluating the model’s performance, leveraging comparative statistics and accuracy assessments to highlight its reliability. This methodology contributes to substantial reductions in computational time. This research aims to provide critical insights that facilitate the optimization of offshore wind turbines, marking a step forward in the quest for more efficient and dependable renewable energy solutions.
Keywords
Floating Offshore Wind Turbine; FOWT; FWT; Renewable Energy; LSTM Neural Networks; Seakeeping; Deep Learning; Time-Series prediction; Machine Learning; Data-Driven Model
Subject
Engineering, Marine Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
