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Title: Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets
Authors: Quevedo Reina, Román 
Álamo Meneses, Guillermo Manuel 
Padrón Hernández, Luis Alberto 
Aznárez González, Juan José 
UNESCO Clasification: 332202 Generación de energía
330510 Cimientos
Keywords: Fatigue
Offshore Wind Turbine, et al
Issue Date: 2023
Journal: Computers and Structures 
Abstract: The design of the support structure of offshore wind turbines (OWT) requires the dynamic characteriza-tion of the complete structural system, including the soil-foundation subsystem. To minimize resonance phenomena, the structural natural frequencies should be sufficiently apart from those that characterize the different loads. The obtention of natural frequencies can be a computationally expensive procedure, more so if dynamic soil-structure interaction (SSI) phenomena are included. In order to reduce the com-putational cost, a surrogate model based on Artificial Neural Networks (ANN) is proposed to estimate the fundamental frequency of the assembly formed by the wind turbine, the jacket support structure and the pile foundation. The training dataset is obtained by a finite element substructuring approach in which the dynamic SSI is incorporated through impedance functions obtained from a continuous model. The ability of the proposed ANN-based surrogate model to reproduce the influence that the main variables of the problem have on the fundamental frequency in a sufficiently precise way is shown by comparing its pre-dictions and the results of the finite elements model for new configurations. The high accuracy and sig-nificant computational cost reduction justify the use of the surrogate model in applications where a large number of evaluations is required.
ISSN: 0045-7949
DOI: 10.1016/j.compstruc.2022.106917
Source: Computers & Structures [ISSN 0045-7949], v. 274, 106917, (Enero 2023)
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