On the Feasibility of Using Large-Eddy Simulations for Real-Time Turbulent-Flow Forecasting in the Atmospheric Boundary Layer

Bauweraerts, Pieter; Meyers, Johan

doi:10.1007/s10546-019-00428-5

Cited by 17 publications

(16 citation statements)

References 59 publications

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“…While these studies achieved successful dynamic power production increases over baseline operation, the computational expense of adjoint LES is similar to standard LES and is currently a challenge to use in real-time wind farm control. Bauweraerts and Meyers (2019) showed that coarse LES can potentially be used for real-time prediction and control but this requires future investigation and is not the focus of the present study. Ciri et al (2017) used a model-free formulation and dynamic control to increase the power production of a model wind farm and found that downstream wind turbines may also need to change their operational strategy to increase farm performance.…”

Section: Introductionmentioning

confidence: 95%

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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Section: Introductionmentioning

confidence: 95%

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

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

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show abstract

“…Diffusive and convective terms are discretized using second-order central differences and a blend of third-order QUICK (10 %) and fourth-order central differences (90 %), respectively. The blended scheme for the convective term was shown to provide sufficient numerical stability while keeping numerical diffusion to a minimum (Troldborg et al, 2010;Bechmann et al, 2011). The pressure correction is solved using the SIMPLE algorithm.…”

Section: Ellipsys3dmentioning

confidence: 99%

Actuator line simulations of wind turbine wakes using the lattice Boltzmann method

2020

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Abstract. The high computational demand of large-eddy simulations (LESs) remains the biggest obstacle for a wider applicability of the method in the field of wind energy. Recent progress of GPU-based (graphics processing unit) lattice Boltzmann frameworks provides significant performance gains alleviating such constraints. The presented work investigates the potential of LES of wind turbine wakes using the cumulant lattice Boltzmann method (CLBM). The wind turbine is represented by the actuator line model (ALM). The implementation is validated and discussed by means of a code-to-code comparison to an established finite-volume Navier–Stokes solver. To this end, the ALM is subjected to both laminar and turbulent inflow while a standard Smagorinsky sub-grid-scale model is employed in the two numerical approaches. The resulting wake characteristics are discussed in terms of the first- and second-order statistics as well the spectra of the turbulence kinetic energy. The near-wake characteristics in laminar inflow are shown to match closely with differences of less than 3 % in the wake deficit. Larger discrepancies are found in the far wake and relate to differences in the point of the laminar-turbulent transition of the wake. In line with other studies, these differences can be attributed to the different orders of accuracy of the two methods. Consistently better agreement is found in turbulent inflow due to the lower impact of the numerical scheme on the wake transition. In summary, the study outlines the feasibility of wind turbine simulations using the CLBM and further validates the presented set-up. Furthermore, it highlights the computational potential of GPU-based LBM implementations for wind energy applications. For the presented cases, near-real-time performance was achieved using a single, off-the-shelf GPU on a local workstation.

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“…In the transition region, an internal boundary layer grows from the height of the wind farm at z = z h until it reaches the final boundary layer height at z = δ. The classical internal boundary layer growth model dictates that: (34) until δ ibl = δ. The friction velocities (30) and (31) and planar-averaged velocity (32) evolve with streamwise distance x, where δ is replaced with the internal boundary layer height δ ibl (x).…”

Section: The Steady-state Model For Design Applicationsmentioning

confidence: 99%

“…The simplest models are discrete-time extensions of steady-state wake models [31] and continuous-time one-dimensional models [8,32]. Higher fidelity, yet more computationally-expensive, models include two-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations [33] and very coarse large-eddy simulations (LES) [34].…”

Section: Introductionmentioning

confidence: 99%

A Wake Modeling Paradigm for Wind Farm Design and Control

et al. 2019

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Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications.

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On the Feasibility of Using Large-Eddy Simulations for Real-Time Turbulent-Flow Forecasting in the Atmospheric Boundary Layer

Cited by 17 publications

References 59 publications

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

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

Actuator line simulations of wind turbine wakes using the lattice Boltzmann method

A Wake Modeling Paradigm for Wind Farm Design and Control

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