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Understanding the unsteady characteristics of the mid-to-far wake and comprehension of the aerodynamic performance of floating offshore wind turbines is essential for the further development of offshore wind farms. In this perspective, a developed Actuator line model is utilized to analyze an offshore turbine on four different platforms. The model reliability was examined through three sets of validations involving turbine output in fixed and floating conditions and wake expansion in terms of size and rate. The affected relative velocity by the platform motion contributes to the wake deformation and temporal effects on induced velocity. Angular platform motions produce a non-axisymmetric helical wake that raises the chance of meandering wake patterns. It was founded although platform movement generally can boost the recovery of mean velocity value, it may amplify the amplitude of velocity deficit fluctuation at further downstream by encouraging interactions and merging vortex rings. Consequently, the wake propagates into a form of stronger circles whose period, strength, and center are functions of turbine movement and operation conditions. By providing a computationally efficient tool, the findings emphasize the importance of wake propagation in designing and assessing farm layouts that operate in the presence of significant multi-motions of floating offshore wind turbines. [ABSTRACT FROM AUTHOR]

The unsteady aerodynamic characteristics and interference effects of floating offshore wind turbine (FOWT) are mainly affected by the pitch motion of the ocean platform. Based on computational fluid dynamics (CFD) and advanced overset grid technology, the aerodynamic performance and wake characteristics of a fully configured wind turbine with rotating blades, nacelle , and tower are studied in this paper. The effects of the amplitude and frequency of pitch motion on the wind turbine aerodynamic loads and flow field are investigated herein in detail. The power and thrust between numerical simulation and experiment are compared. The results show that the grid and simulation parameters used in this study can accurately capture the aerodynamic characteristics and the flow field around wind turbines. The influence of the pitch amplitude and frequency on the performance of wind turbines is discussed. The complex flow interaction between the tip vortex, tower shedding vortex, and the turbulent wake was observed. The present results indicate that the pitch motion amplitude and frequency have a great influence on the power, thrust, and wake characteristics. [ABSTRACT FROM AUTHOR]

Fast and effective numerical models describing the effect of platform motion on the performance of floating offshore wind turbines (FOWTs) are fundamental to assess energy harvesting potential in large offshore wind farms. The purpose of this paper is to implement a CFD-based Computationally-Efficient approach based on an actuator line model (ALM) for FOWTs aerodynamics. Such a tool aims at complementing reasonable accuracy and affordable computational effort while being able to investigate the effects of the platform motions on the wake evolution. The actuator line model for FOWTs is developed by implementing a dedicated C++ library in the OpenFOAM toolbox. In addition, a tip treatment is applied to involve the tip effects induced by the pressure equalization from the suction and pressure sides. Results show that employing ALM decreases computational cost and preprocessing time for producing appropriate computational grids, as just about 400k and 600k grids are necessary for solving two representative test cases of fixed-bottom turbines (NREL Phase VI and NREL 5-MW) with reasonable accuracy. The inclusion of platform motion is then introduced, and the results showed that ALM is capable of capturing vortices trajectory, potential blade-vortex interactions, and vortex pairing and vortex ring state phenomenon in FOWTs. [ABSTRACT FROM AUTHOR]

Turbines in a wind farm dynamically influence each other through wakes. Therefore trade-offs exist between energy output of upstream turbines and the health of downstream turbines. Using both model-based predictive control (MPC) and machine learning techniques, existing works have explored the energy-fatigue trade-off either in a single turbine or only with few turbines due to issues of scalability and complexity. To address this gap, this paper proposes a multi-agent deep reinforcement learning-based coordinated control for wind farms, called FALCON. FALCON addresses the multi-objective optimization problem of maximizing energy while minimizing fatigue damage by jointly controlling pitch and yaw of all turbines. FALCON achieves scale by using multiple reinforcement learning agents; capturing the global state-space efficiently using an auto-encoder; and pruning the action-space using domain knowledge. FALCON is evaluated through a real-world wind-farm case study with 21 turbines; and performs better than the default baseline PID controller and a learning-based distributed control. • One of the first works using multiagent Reinforcement Learning for wind farm control. • Multi-objective optimization of both power and turbine life. • Novel implementation of RL and auto-encoder for solution scalability. • Performance of the approach is demonstrated on real-world wind farm data. • Results show that 6% more energy with 31 years of extra turbine life can be achieved. [ABSTRACT FROM AUTHOR]

We propose a data-driven model to predict the short-term fatigue Damage Equivalent Loads (DEL) on a wake-affected wind turbine based on wind field inflow sensors and/or loads sensors deployed on an adjacent up-wind wind turbine. Gaussian Process Regression (GPR) with Bayesian hyperparameters calibration is proposed to obtain a surrogate from input random variables to output DELs in the blades and towers of the up-wind and wake-affected wind turbines. A sensitivity analysis based on the hyperparameters of the GPR and Kullback-Leibler divergence is conducted to assess the effect of different input on the obtained DELs. We provide qualitative recommendations for a minimal set of necessary and sufficient input random variables to minimize the error in the DEL predictions on the wake-affected wind turbine. Extensive simulations are performed comprising different random variables, including wind speed, turbulence intensity, shear exponent and inflow horizontal skewness. Furthermore, we include random variables related to the blades lift and drag coefficients with direct impact on the rotor aerodynamic induction, which governs the evolution and transport of the meandering wake. In addition, different spacing between the wind turbines and Wöhler exponents for calculation of DELs are considered. The maximum prediction normalized mean squared error, obtained in the tower base DELs in the fore-aft direction of the wake affected wind turbine, is less than 4%. In the case of the blade root DELs, the overall prediction error is less than 1%. The proposed scheme promotes utilization of sparse structural monitoring (loads) measurements for improving diagnostics on wake-affected turbines. • Predicting loads on unmeasured wake-affected WT from sensors on an adjacent WT. • Gaussian Process Regression model with Bayesian hyperparameters calibration. • Propagation of uncertain inflow, aerodynamic, layout and fatigue random variables. • Recommended minimal set of input to predict the DEL on a wake-affected WT. • The overall prediction error of DELs of the wake-affected WT is less than 4%. [ABSTRACT FROM AUTHOR]

An experimental study of the wake characteristics of a semi-submersible offshore wind turbine model was performed in a wind tunnel and wave flume. The velocity distribution in the vertical direction was obtained with a four-hole pressure probe. First, a self-similarity analysis of the streamwise velocity deficit disclosed the inability of the Gaussian-like wake models for predicting the velocity deficit in regions influenced by the platform. The shelter model can be adopted for the consideration of the platform wake. And the linear self-similarity of the velocity deficit caused by the platform also suggests that a linear model is feasible. Moreover, recently-discovered existence and analytical solution of the Reynolds stress self-similarity in the turbine wake were confirmed experimentally in this study. As the wake develops, the center of the Reynolds stress increment profiles drifts upward while the center of mean velocity deficit profiles remains at the same height. Furthermore, energy transport analysis confirmed the interaction effects between the rotor wake and platform wake. The results of this study will be useful for the design of the whole wind farm with more accuracy, which considers the influence of the platform on the flow field between neighbor wind turbines. • A novel analytical wind turbine wake model including support structure is developed. • Interaction between the platform and rotor wakes is revealed with energy transport analysis. • Self-similarity in Reynolds stress of turbine wake is experimentally confirmed. [ABSTRACT FROM AUTHOR]

The wake characteristics of a variable-speed pitch-regulated floating offshore wind turbine (FOWT) under surge motions are studied using a Computational Fluid Dynamics (CFD) method which combines the Actuator Curve Embedding (ACE) method and collective blade pitch control strategy. Verifications and validations show the method is accurate with affordable computational cost. Comparative analysis of a FOWT under specified surge motion at rated wind speed indicates the controller significantly reduces the mean wake deficits, which highlights the necessity of considering controller for FOWT(s) wake analysis and floating offshore wind farm layout optimizations. The mean wake deficits and wake widths are observed to reduce with increasing surge amplitudes for a controlled FOWT. At rated wind speed, the helical tip vortices of a controlled FOWT under surge motions are observed transitioning into well-organized vortex ring structures and the vortex ring distances are found almost independent on surge amplitudes. The total wake vortex transition distance and vortex ring distance are observed increasing linearly with surge periods. These findings contribute to a better understanding of FOWT wake dynamics in real operating conditions. [ABSTRACT FROM AUTHOR]

We investigate the turbulent dynamics of the coupled atmosphere-ocean-sediment system around a wind turbine. To this end, a coupled two-dimensional idealized numerical model of the ocean and sediment layers, forced by an idealized offshore wind turbine wake is used. The turbine wake impacts the ocean surface and for strong wind and water layer thickness higher than 20 m, large scale eddies of the size comparable to the wake thickness are generated, leading to a turbulent dynamics in the ocean. The turbulence in the ocean is controlled by the shallow wake parameter S. The turbulent ocean dynamics is numerically integrated using time dependent simulations at fine horizontal resolution (1 m). From these simulations, eddy coefficients parametrizating the turbulent fluxes are proposed to be used in larger-scale (RANS) models. The ocean dynamics and the parameter values depend mainly on S. The ocean dynamics is laminar (S > 7.10 −2 ), has a localized (7.10 −2