Rigid body dynamic response of a floating offshore wind turbine to waves: Identification of the instantaneous centre of rotation through analytical and numerical analyses

Patryniak, Katarzyna; Collu, Maurizio; Coraddu, Andrea

doi:10.1016/j.renene.2023.119378

Cited by 5 publications

(1 citation statement)

References 20 publications

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“…Currently, hydrodynamic factor prediction has made outstanding progress [152][153][154], and simulation of hydrodynamic processes can yield relatively accurate results. Kai Fei et al [155] established a numerical simulation model for storm surges coupled with hydrology and hydrodynamics, successfully calculating the relative contributions of each driving factor to water level using Extreme Gradient Boosting.…”

Section: Prediction Of Water Quality Through Coupling Hydrodynamics A...mentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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Section: Prediction Of Water Quality Through Coupling Hydrodynamics A...mentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

View full text Add to dashboard Cite

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Nonlinear analysis of hydrodynamics of a shallow-draft floating wind turbine

Terrero-Gonzalez,

Dai,

Papadopoulos

et al. 2024

Nonlinear Dyn

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This study investigates numerically the dynamic responses of the T-Omega Wind novel concept of Floating Offshore Wind Turbine. The turbine is light-weight, has a shallow-draft and a relatively high centre of gravity that allows it to glide over harsh marine environments. The turbine responses are studied under regular wave excitation, considering most probable ranges of discrete sea wave heights and periods representative of real ocean conditions. A multibody virtual model is developed, simplified to a rigid 6 DOF system and experimentally validated in the state-of-art Marine Simulator to define the types of dynamical responses for both “Low” and “High” Sea States. The dynamics of coupled heave and pitch DOFs are evaluated with time histories, phase-plane portraits, Poincaré sections and FFT analyses to conclude that period-1 stable solutions exist for all studied cases of “Low Sea States”, whereas period-2, period-3 and period-4 periodic responses are identified for short wave periods of excitation under “High Sea States” conditions. Simulation results show that regions where period-1 responses exist are highly sensitive to wave height and can widen as the wave amplitude reduces. Finally, the turbines’ nonlinearities generated by the floats’ geometry are observed in this dynamical system, which are identified to be related to variation in float waterplane area and particularly observable for “High Sea States”.

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Estimating the stationary distribution of pitch motion response for floating offshore wind platforms via the detailed balance method

Lyu,

Falzarano

2025

Ships and Offshore Structures

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Rigid body dynamic response of a floating offshore wind turbine to waves: Identification of the instantaneous centre of rotation through analytical and numerical analyses

Cited by 5 publications

References 20 publications

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Nonlinear analysis of hydrodynamics of a shallow-draft floating wind turbine

Estimating the stationary distribution of pitch motion response for floating offshore wind platforms via the detailed balance method

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