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Lookup NU author(s): Peng Chen, Professor Zhiqiang Hu
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022, The Author(s). Floating offshore wind turbines (FOWTs) are a promising offshore renewable energy harvesting facility but requesting multiple-disciplinary analysis for their dynamic performance predictions. However, engineering-fidelity level tools and the empirical parameters pose challenges due to the strong nonlinear coupling effects of FOWTs. A novel method, named SADA, was proposed by Chen and Hu (2021) for optimizing the design and dynamic performance prediction of FOWTs in combination with AI technology. In the SADA method, the concept of Key Disciplinary Parameters (KDPs) is also proposed, and it is of crucial importance in the SADA method. The purpose of this paper is to make an in-depth investigation of the characters of KDPs and the internal correlations between different KDPs in the dynamic performance prediction of FOWTs. Firstly, a brief description of SADA is given, and the basin experimental data are used to conduct the training process of SADA. Secondly, categories and boundary conditions of KDPs are introduced. Three types of KDPs are given, and different boundary conditions are used to analyze KDPs. The results show that the wind and current in Environmental KDPs are strongly correlated with the percentage difference of dynamic response rather than that by wave parameters. In general, the optimization results of SADA consider the specific basin environment and the coupling results between different KDPs help the designers further understand the factors that have a more significant impact on the FOWTs system in a specific domain.
Author(s): Chen P, Hu Z-Q
Publication type: Article
Publication status: Published
Journal: China Ocean Engineering
Year: 2022
Volume: 36
Pages: 649-657
Print publication date: 01/08/2022
Online publication date: 10/08/2022
Acceptance date: 20/06/2022
Date deposited: 31/08/2022
ISSN (print): 0890-5487
ISSN (electronic): 2191-8945
Publisher: Haiyang Chubanshe, Ocean Press
URL: https://doi.org/10.1007/s13344-022-0045-4
DOI: 10.1007/s13344-022-0045-4
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