Influence of the geostrophic wind direction on the atmospheric boundary layer flow

Howland, Michael F.; Ghate, Aditya S.; Lele, Sanjiva K.

doi:10.1017/jfm.2019.889

Cited by 34 publications

(40 citation statements)

References 53 publications

(66 reference statements)

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“…The MKE budget for the larger streamtube highlights that the turbine power largely comes from the energy flux due to Reynolds stresses, and that some fraction of that energy is lost into production of TKE. This echoes the analysis of horizontally averaged MKE budgets [11,23]. These results are also consistent with experimental evidence that TKE production and dissipation increase in wind turbine wakes [13,32].…”

Section: Effect Of C T On Budget Termssupporting

confidence: 89%

“…The incompressible grid (igrid) branch is used in this work. This code has been validated on a number of incompressible flow problems, and has been previously used to study wind energy and atmospheric boundary layer flows [22][23][24]. The turbines are implemented as regularized actuator disks without rotation, using the same method as the JHU-LES code [20].…”

Section: Simulation Methodsmentioning

confidence: 99%

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Wind Turbine Performance in Very Large Wind Farms: Betz Analysis Revisited

West

Lele

2020

Energies

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The theoretical limit for wind turbine performance, the so-called Betz limit, arises from an inviscid, irrotational analysis of the streamtube around an actuator disk. In a wind farm in the atmospheric boundary layer, the physics are considerably more complex, encompassing shear, turbulent transport, and wakes from other turbines. In this study, the mean flow streamtube around a wind turbine in a wind farm is investigated with large eddy simulations of a periodic array of actuator disks in half-channel flow at a range of turbine thrust coefficients. Momentum and mean kinetic energy budgets are presented, connecting the energy budget for an individual turbine to the wind farm performance as a whole. It is noted that boundary layer turbulence plays a key role in wake recovery and energy conversion when considering the entire wind farm. The wind farm power coefficient is maximized when the work done by Reynolds stress on the periphery of the streamtube is maximized, although some mean kinetic energy is also dissipated into turbulence. This results in an optimal value of thrust coefficient lower than the traditional Betz result. The simulation results are used to evaluate Nishino’s model of infinite wind farms, and design trade-offs described by it are presented.

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Section: Effect Of C T On Budget Termssupporting

confidence: 89%

Section: Simulation Methodsmentioning

confidence: 99%

Wind Turbine Performance in Very Large Wind Farms: Betz Analysis Revisited

West

Lele

2020

Energies

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“…The traditional approximation, which neglects the horizontal component of Earth's rotation (Leibovich and Lele, 1985), is not enforced. Therefore, Earth's full rotational vector is included resulting in wind farm dynamics which are sensitive to the direction of the geostrophic wind (Howland et al, 2020a). For simplicity all simulations are performed with west to east geostrophic wind.…”

Section: Large Eddy Simulation Setupmentioning

confidence: 99%

“…The uniform inflow domain is initialized with u = 1 in the streamwise direction. Detailed comments on the initialization for the conventionally neutral case are given by Howland et al (2020a). The simulation cases are run until statistical stationarity and quasi-stationarity is reached for the uniform and conventionally neutral cases, respectively.…”

Section: Large Eddy Simulation Setupmentioning

confidence: 99%

“…The number of grid points is 480×240×192 with a grid spacing of 25m×25m×12.5m. The grid spacing is uniform and the mesh size is similar to previous studies (Allaerts and Meyers, 2015) and a grid convergence study was performed by (Howland et al, 2020a) for the conventionally neutral ABL. Six model wind turbines are incorporated in the domain and the layout within the computational domain is shown in Figure 6.…”

Section: Dynamic Wake Steering Uniform Inflow Lesmentioning

confidence: 99%

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Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

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Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

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A physical wind‐turbine wake growth model under different stratified atmospheric conditions

Liu

2022

Wind Energy

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The wind‐turbine wake growth is crucial for wake assessment. At present, it can only be determined empirically, which is the primary source of prediction errors in the analytical wake model, and a physically‐based method is urgently needed. Recently, the plume model is proposed for wake width prediction in the neutral boundary layer based on Taylor's diffusion theory. However, this model is not applicable for high‐roughness neutral and strongly convective conditions, which is mainly related to the fact that the specified far wake point in the plume model is too close to the virtual wake origin. In this condition, the wake width prediction has evident convex function characteristics, which is inconsistent with the actual linear expansion of wake width. To this end, we propose a coupled model of the plume model and the traditional wake model (CPT model) to calculate the wake growth rate iteratively, thereby obtaining the wake width and velocity deficits in the far‐wake region. The average wake width prediction error decreases from 11.75% to 3.1% in these conditions. Since the wind‐turbine‐induced‐turbulence contribution is dominant in the wake recovery in the very stable boundary layer, both models have low but engineering acceptable prediction accuracy. Except for the above conditions, both the plume and CPT models can predict the wake width well, and their average wake width prediction errors are 2.5% and 1.9%, respectively. This implies that the proposed CPT model has higher prediction accuracy and a broader application range.

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Influence of the geostrophic wind direction on the atmospheric boundary layer flow

Cited by 34 publications

References 53 publications

Wind Turbine Performance in Very Large Wind Farms: Betz Analysis Revisited

Wind Turbine Performance in Very Large Wind Farms: Betz Analysis Revisited

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

A physical wind‐turbine wake growth model under different stratified atmospheric conditions

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