A numerical investigation of a wind turbine wake in non-neutral atmospheric conditions

Baungaard, Mads; Abkar, Mahdi; Laan, P. van der; Kelly, Mark

doi:10.1088/1742-6596/2265/2/022015

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Reynolds-averaged Navier-Stokes (RANS) is the prevalent CFD approach that is widely used in wind resource assessment and wake modelling [1]. Several studies have investigated single wake, wake flow within a wind farm, and the interaction between farms, using RANS CFD [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Generalization of single wake surrogates for multiple and farm-farm wake analysis

Pish,

Göçmen,

Van Der Laan

2024

J. Phys.: Conf. Ser.

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Wind farm efficiency is influenced by atmospheric turbulence and wake interactions from preceding turbines. Optimal performance necessitates effective control strategies, encompassing collective/individual pitch (and/or torque) control, yaw control, and innovative techniques, significantly boosting energy capture. Wake effects are crucial, impacting downstream turbines and reducing overall energy extraction. For this purpose, the study of wind farm flow control (WFFC) holds significant relevance in this context. In this study, a Deep Neural Network predicts downstream flow features of a wind turbine under diverse control scenarios, including varying thrust coefficient and yaw control. A feed-forward neural network models the deficit and added turbulent intensity of a single wake, trained using Computational Fluid Dynamics (CFD) simulations as reference data. Linear superposition establishes the wind farm flow field, allowing examination of downstream effects. Another feedforward neural network encompasses wind farm physics such as blockage and wake recovery in and around wind turbine arrays which are overlooked by the single wake model. The methodology employed in this study yields results that are more time-efficient compared to traditional CFD models while maintaining a higher level of accuracy (ideally) than the engineering models, especially when implemented for WFFC without calibration.

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Section: Introductionmentioning

confidence: 99%

Generalization of single wake surrogates for multiple and farm-farm wake analysis

Pish,

Göçmen,

Van Der Laan

2024

J. Phys.: Conf. Ser.

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Wind turbine wake simulation with explicit algebraic Reynolds stress modeling

et al. 2022

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Abstract. Reynolds-averaged Navier–Stokes (RANS) simulations of wind turbine wakes are usually conducted with two-equation turbulence models based on the Boussinesq hypothesis; these are simple and robust but lack the capability of predicting various turbulence phenomena. Using the explicit algebraic Reynolds stress model (EARSM) of Wallin and Johansson (2000) can alleviate some of these deficiencies while still being numerically robust and only slightly more computationally expensive than the traditional two-equation models. The model implementation is verified with the homogeneous shear flow, half-channel flow, and square duct flow cases, and subsequently full three-dimensional wake simulations are run and analyzed. The results are compared with reference large-eddy simulation (LES) data, which show that the EARSM especially improves the prediction of turbulence anisotropy and turbulence intensity but that it also predicts less Gaussian wake profile shapes.

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A numerical investigation of a wind turbine wake in non-neutral atmospheric conditions

Cited by 2 publications

References 21 publications

Generalization of single wake surrogates for multiple and farm-farm wake analysis

Generalization of single wake surrogates for multiple and farm-farm wake analysis

Wind turbine wake simulation with explicit algebraic Reynolds stress modeling

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