Subfilter-scale enrichment of planetary boundary layer large eddy simulation using discrete Fourier–Gabor modes

Ghate, Aditya S.; Lele, Sanjiva K.

doi:10.1017/jfm.2017.187

Cited by 47 publications

(39 citation statements)

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“…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: Simulation Methodsmentioning

confidence: 99%

“…The Reynolds number is infinite, meaning there are no viscous forces on resolved scales. Full details regarding subgrid scale model, time stepping, and other aspects of the solver can be found in Appendix A of Ghate and Lele [22].…”

Section: Simulation Methodsmentioning

confidence: 99%

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

West

Lele

2020

Energies

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“…Temporal integration uses a fourth order strong stability preserving Runge-Kutta variant (Gottlieb et al, 2011). The LES code has previously been utilized for high Reynolds number ABL flows (Howland et al, 2020b;Ghaisas et al, 2020) and is described in detail by Ghate and Lele (2017). The ABL is modeled as an incompressible, high Reynolds number limit (Re → ∞)…”

Section: Large Eddy Simulation Setupmentioning

confidence: 99%

“…The subfilter scale stress tensor is given by τ ij and the sigma model is employed (Nicoud et al, 2011). The turbulent Prandtl number used in the subfilter scale model is P r = 0.4 (Ghate and Lele, 2017). Surface stress and heat flux is computed using a local wall model based on Monin-Obukhov similarity theory with appropriate treatment based on the state of stratification (Basu et al, 2008).…”

Section: Large Eddy Simulation Setupmentioning

confidence: 99%

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

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

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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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“…One approach is to 'enrich' these scales with synthetic turbulence (see Q5), see e.g. [86]. Data assimilation tools such as Kalman filtering and ideas from control represent active areas of research.…”

Section: Question 6: How Can Information About Regional Meso-scale Phmentioning

confidence: 99%

Big wind power: seven questions for turbulence research

Meneveau

2019

Journal of Turbulence

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The accelerating growth of wind energy in recent years mandates improved understanding of wind turbine, wind farm and atmospheric turbulence interactions. Fluid turbulence plays a vital role in these interactions, motivating the present formulation of several pertinent questions for turbulence research. These questions touch upon the need for better analytical, synthetic and reduced order models of turbulence, better model coupling methods and basic understanding of flow phenomena governing kinetic energy entrainment and limiting power densities. Responding to the formulated questions may lead to improvements in wind energy harvesting.

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Subfilter-scale enrichment of planetary boundary layer large eddy simulation using discrete Fourier–Gabor modes

Cited by 47 publications

References 50 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

Big wind power: seven questions for turbulence research

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