Print Email Facebook Twitter Computationally Aware Surrogate Models for the Hydrodynamic Response Characterization of Floating Spar-Type Offshore Wind Turbine Title Computationally Aware Surrogate Models for the Hydrodynamic Response Characterization of Floating Spar-Type Offshore Wind Turbine Author Ilardi, Davide (University of Genova) Kalikatzarakis, Miltiadis (University of Strathclyde) Oneto, Luca (University of Genova) Collu, Maurizio (University of Strathclyde) Coraddu, A. (TU Delft Ship Design, Production and Operations) Date 2024 Abstract Due to increasing environmental concerns and global energy demand, the development of Floating Offshore Wind Turbines (FOWTs) is on the rise. FOWTs offer a promising solution to expand wind farm deployment into deeper waters with abundant wind resources. However, their harsh operating conditions and lower maturity level compared to fixed structures pose significant engineering challenges, notably in the design phase. A critical challenge is the time-consuming hydromechanics analysis traditionally done using computationally intensive Computational Fluid Dynamics (CFD) models. In this study, we introduce Artificial Intelligence-based surrogate models using state-of-the-art Machine Learning algorithms. These surrogate models achieve CFD-level accuracy (within 3% difference) while dramatically reducing computational requirements from minutes to milliseconds. Specifically, we build a surrogate model for characterizing the hydrodynamic response of a floating spar-type offshore wind turbine (including added mass, radiation damping matrices, and hydrodynamic excitation) using computationally efficient shallow Machine Learning models, optimizing the trade-off between computational efficiency and accuracy, based on data generated by a cutting-edge potential-flow code. Subject accuracycomputational fluid dynamicscomputational requirementsFloating offshore wind turbineshydrodynamic responsemachine learningsurrogate models To reference this document use: http://resolver.tudelft.nl/uuid:b0b4b511-818e-4b6e-b7d7-62b4c1ae586f DOI https://doi.org/10.1109/ACCESS.2023.3343874 ISSN 2169-3536 Source IEEE Access, 12, 6494-6517 Part of collection Institutional Repository Document type journal article Rights © 2024 Davide Ilardi, Miltiadis Kalikatzarakis, Luca Oneto, Maurizio Collu, A. Coraddu Files PDF Computationally_Aware_Sur ... urbine.pdf 3.98 MB Close viewer /islandora/object/uuid:b0b4b511-818e-4b6e-b7d7-62b4c1ae586f/datastream/OBJ/view