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We present a large-eddy simulation framework capable of control co-design of large wind turbines, coupling the turbulent flow environment with blade aeroelastics and turbine controllers. The geometry and aerodynamics of the rotor blades and the turbine nacelle are parameterized using an actuator surface model. The baseline collective pitch control and individual pitch control (IPC) algorithms, consisting of a single-input, single-output proportional–integral controller and two integral controllers, respectively, are incorporated into the simulation framework. Furthermore, a second-order model based on the Euler–Bernoulli beam theory is implemented to describe the blade deformation. Simulations are carried out to investigate the impact of collective and individual pitch control strategies on the deflection of turbine blades. Our results show that the IPC reduces the blade tip deflection fluctuations in the out-of-plane direction, while the fluctuations of the blade tip deflection along the in-plane direction are barely affected by the IPC. Furthermore, the blade out-of-plane deformation fluctuation is underestimated by the one-way coupling approach compared to the two-way coupling approach. The findings of this study reveal the importance of advanced control systems in reducing the dynamic loads on wind turbine blades and underscore the potential of control co-design to reduce the levelized cost of wind energy. [ABSTRACT FROM AUTHOR]

Large eddy simulations (LES) of the flow past a wind turbine with and without tower and nacelle have been performed at 2 tip speed ratios (TSR [ABSTRACT FROM AUTHOR]

Large-eddy simulations of the flow past an array of three aligned turbines have been performed. The study is focused on below rated (Region 2) wind speeds. The turbines are controlled through the generator torque gain, as usually done in Region 2. Two operating strategies are considered: (i) preset individual optimum torque gain based on a model for the power coefficient (baseline case) and (ii) real-time optimization of torque gain for maximizing each individual turbine power capture during operation. The real-time optimization is carried out through a model-free approach, namely, extremum-seeking control. It is shown that ESC is capable of increasing the power production of the array by 6.5% relative to the baseline case. The extremum-seeking control reduces the torque gain of the downstream turbines, thus increasing the angular speed of the blades. This results in improved aerodynamics near the tip of the blade that is the portion contributing mostly to the torque and power. In addition, an increase in angular speed leads to a larger entrainment in the wake, which also contributes to provide additional available power downstream. It is also shown that the tip speed ratio may not be a reliable performance indicator when the turbines are in waked conditions. This may be a concern when using optimal parameter settings, determined from isolated turbine models, in applications with waked turbines. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments. • Brute-force LES is augmented via a machine-learning to reduce computational costs of obtaining wind turbulence statistics. • A convolutional neural network was trained for computing turbulence statistics from just few snapshots of flow fields. • The proposed machine-learning algorithms can successfully predict oceanic mean wind velocity, fluctuation, and TKE fields. [ABSTRACT FROM AUTHOR]

Tidal and riverine flows are viable energy sources for consistent energy production. Installing and operating marine hydrokinetic (MHK) turbines requires assessing any potential impact of debris accumulation on turbine performance and sediment transport. More specifically, MHK devices may alter the natural sediment transport processes and cause debris accumulation, disrupting the natural sediment dynamic. In turn, these processes could affect the turbine's performance. We carried out a series of large-eddy simulations coupled with bed morphodynamics, introducing various debris loads lodged on the upstream face of a utility-scale turbine tower. The objective is to systematically investigate the impact of debris accumulation on the performance and hydro- and morpho-dynamics interactions of the horizontal-axis MHK turbine under rigid and live bed conditions. To that end, we (1) employed the actuator line and surface methods for modeling turbine blades and the nacelle, respectively, (2) directly resolved individual logs, and (3) solved the Exner equation to obtain the instantaneous bed deformation of the live bed. Our analysis revealed that while the spinning rotor amplifies scour around the pile, debris accumulation modifies the sediment dynamics of the system. Also, it found that morphodynamic processes accelerate the wake recovery, slightly enhancing the turbine's performance. [ABSTRACT FROM AUTHOR]