A study on Key Disciplinary Parameters of AI-based Analysis Method for Dynamic Response Prediction of Floating Offshore Wind Turbines

IF 1.3 4区 工程技术 Q3 ENGINEERING, MECHANICAL Journal of Offshore Mechanics and Arctic Engineering-Transactions of the Asme Pub Date : 2022-10-17 DOI:10.1115/1.4055993
Peng Chen, Zhiqiang Hu
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引用次数: 1

Abstract

The dynamic performance prediction of Floating offshore wind turbines (FOWTs) is a challenging task, as the existing theories might not be fully reliable for FOWTs due to the high nonlinearities and coupling effects. The artificial intelligent method gives a promising solution for this issue, and Chen and Hu (2021) proposed a novel AI-based method, named SADA to overcome these challenges. This paper addresses a further and in-depth investigation on the key technologies of the Key Disciplinary Parameters (KDPs) in the SADA method, to obtain a novel and accurate analysis method for dynamic responses prediction of FOWTs. Firstly, the categorization of KDPs is introduced, which can be divided into three categories: Environmental KDPs, Disciplinary KDPs, and Specific KDPs. Secondly, two factors, the number of KDPs and boundary adjustment of KDPs are investigated through the reinforcement learning algorithm within the SADA method. Basin experimental data of a Spar-type FOWT is used for AI training. The results show that more proper KDPs set in the SADA method can lead to higher accuracy for the prediction of FOWTs. Besides, reasonable boundary conditions will also contribute to the convergence of the algorithms efficiently. Finally, the instruction on how to better choose KDPs and how to set and adjust their boundary conditions is given in the conclusion. The application of KDPs in the SADA method not only provides a deeper understanding of the dynamic response of the entire FOWTs system but also provides a promising solution to overcome the challenges of validation.
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基于人工智能的海上浮式风力发电机动态响应预测分析方法关键学科参数研究
浮式海上风力机的动态性能预测是一项具有挑战性的任务,由于浮式海上风力机的高度非线性和耦合效应,现有的理论对浮式海上风力机的动态性能预测可能并不完全可靠。人工智能方法为这一问题提供了一个有希望的解决方案,Chen和Hu(2021)提出了一种新的基于人工智能的方法,称为SADA来克服这些挑战。本文对SADA方法中关键学科参数(KDPs)的关键技术进行了进一步深入的研究,以获得一种新的、准确的FOWTs动态响应预测分析方法。首先,介绍了kdp的分类,将其分为三类:环境kdp、学科kdp和特定kdp。其次,通过SADA方法中的强化学习算法研究了kdp的数量和kdp的边界调整这两个因素。采用spar型FOWT的盆地实验数据进行人工智能训练。结果表明,在SADA方法中设置更合适的kdp可以提高fots的预测精度。此外,合理的边界条件也有助于算法的有效收敛。最后,在结论部分给出了如何更好地选择kdp以及如何设置和调整其边界条件的指导。kdp在SADA方法中的应用不仅提供了对整个FOWTs系统动态响应的更深入理解,而且为克服验证挑战提供了一个有希望的解决方案。
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来源期刊
CiteScore
4.20
自引率
6.20%
发文量
63
审稿时长
6-12 weeks
期刊介绍: The Journal of Offshore Mechanics and Arctic Engineering is an international resource for original peer-reviewed research that advances the state of knowledge on all aspects of analysis, design, and technology development in ocean, offshore, arctic, and related fields. Its main goals are to provide a forum for timely and in-depth exchanges of scientific and technical information among researchers and engineers. It emphasizes fundamental research and development studies as well as review articles that offer either retrospective perspectives on well-established topics or exposures to innovative or novel developments. Case histories are not encouraged. The journal also documents significant developments in related fields and major accomplishments of renowned scientists by programming themed issues to record such events. Scope: Offshore Mechanics, Drilling Technology, Fixed and Floating Production Systems; Ocean Engineering, Hydrodynamics, and Ship Motions; Ocean Climate Statistics, Storms, Extremes, and Hurricanes; Structural Mechanics; Safety, Reliability, Risk Assessment, and Uncertainty Quantification; Riser Mechanics, Cable and Mooring Dynamics, Pipeline and Subsea Technology; Materials Engineering, Fatigue, Fracture, Welding Technology, Non-destructive Testing, Inspection Technologies, Corrosion Protection and Control; Fluid-structure Interaction, Computational Fluid Dynamics, Flow and Vortex-Induced Vibrations; Marine and Offshore Geotechnics, Soil Mechanics, Soil-pipeline Interaction; Ocean Renewable Energy; Ocean Space Utilization and Aquaculture Engineering; Petroleum Technology; Polar and Arctic Science and Technology, Ice Mechanics, Arctic Drilling and Exploration, Arctic Structures, Ice-structure and Ship Interaction, Permafrost Engineering, Arctic and Thermal Design.
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