Implementation of a Mixing Length Turbulence Formulation Into the Dynamic Wake Meandering Model

Keck, Rolf-Erik; Veldkamp, Dick; Madsen, Helge Aagaard; Larsen, Gunner Chr.

doi:10.1115/1.4006038

Cited by 35 publications

(71 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This process is seen to be approximately 10 D, on the basis of AL simulations. The details and motivation for this turbulence formulation are given by Keck et al …”

Section: Introductionmentioning

confidence: 99%

Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build‐up in a row of wind turbines

et al. 2013

Self Cite

View full text Add to dashboard Cite

The dynamic wake meandering (DWM) model is an engineering wake model designed to physically model the wake deficit evolution and the unsteady meandering that occurs in wind turbine wakes. The present study aims at improving two features of the model: The effect of the atmospheric boundary layer shear on the wake deficit evolution by including a strain‐rate contribution in the wake turbulence calculation.The method to account for the increased turbulence at a wake‐affected turbine by basing the wake‐added turbulence directly on the Reynolds stresses of the oncoming wake. This also allows the model to simulate the build‐up of turbulence over a row of turbines in a physically consistent manner. The performance of the modified model is validated against actuator line (AL) model results and field data from the Lillgrund offshore wind farm. Qualitatively, the modified DWM model is in fair agreement with the reference data. A quantitative comparison between the mean flow field of the DWM model with and without the suggested improvements, to that of the AL model, shows that the root‐mean‐square difference in terms of wind speed and turbulence intensity is reduced on the order of 30% and 40%, respectively, by including the proposed corrections for a row of eight turbines. Furthermore, it is found that the root‐mean‐square difference between the AL model and the modified DWM model in terms of wind speed and turbulence intensity does not increase over a row of turbines compared with the root‐mean‐square difference of a single turbine. Copyright © 2013 John Wiley & Sons, Ltd.

show abstract

“…This process is seen to be approximately 10 D, on the basis of AL simulations. The details and motivation for this turbulence formulation are given by Keck et al …”

Section: Introductionmentioning

confidence: 99%

Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build‐up in a row of wind turbines

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…The quasi-steady wake deficit model applies the two-dimensional eddy viscosity model. 6 It also includes the strain-rate contribution from the ABL shear in the wake deficit calculation and the wake expansion as a function of downstream transport distance caused by turbulent diffusion and the rotor pressure field. 7 The wake meandering model describes the stochastic downstream wake transport driven by large scale turbulent structures and this wake meandering model applied is based on the model proposed by Larsen et al.…”

Section: Dwm Overviewmentioning

confidence: 99%

“…7 Based on the calibration by Keck et al, the following values were applied in the calculations: f U = 1.10, f R = 0.98, k 1 = 0.587 and k 2 = 0.0178. 6 The filter function used are shown in Fig. 3.…”

Section: A Wake Deficit Sub-model In the Dwm Modelmentioning

confidence: 99%

Implementing the Dynamic Wake Meandering Model in the NWTC Design Codes

Hao

Lackner

Keck

et al. 2014

32nd ASME Wind Energy Symposium

Self Cite

View full text Add to dashboard Cite

This paper focuses on implementing the dynamic wake meandering (DWM) model in the National Wind Technology Center (NWTC) design code framework. The DWM model calculates the wake deficit and the meandered wake center behind a wind turbine. The advantage of incorporating the DWM model into the NWTC design codes is to provide the ability to simulate unsteady wake effects and examine their impact on power generation, structural loads, and turbine control schemes, while maintaining an acceptably low computational cost. In the present paper, the method of implementing the DWM model into the NWTC design codes is described. In addition, the performance of the DWM model is verified by comparing the generated turbine power and blade loads with high-fidelity large-eddy simulation (LES) and field data for North Hoyle and Lillgrund wind farm. It was found that the results from the DWM model are in good agreement with the LES results and the field data. Further efforts are in pursuit to allow simulations of a full wind farm with an arbitrary wind turbine layout and incoming flow directions which are briefly described in this paper. Nomenclature U wake= velocity at the rotor plane normalized by ambient wind velocity R wake = wake radius at the rotor plane normalized by rotor radius a = turbine spanwise axial induction factor f u = boundary scale factor f R = boundary scale factor U amb = ambient wind velocity R rotor = rotor radius ̅ = spatial averaged induction factor U = velocity component in the mean flow direction 2 V = velocity component in the radial direction = eddy viscosity F 1 = filter function F 2 = filter function k 1 = DWM model constant k 2 = DWM model constant l * = length scale u * = velocity scale f c = the low pass filter cut-off frequency ̅ = mean wind speed D w = instantaneous wake deficit diameter U wake = wake velocity U b-wake = wake velocity at the boundary for a downstream turbine U LM = local mean wind velocity at the rotor plane U r = smoothed-out wake profile TI M = turbulence intensity due to the wake meandering TI W = turbulence intensity due to the wake mixing TI total = total turbulence intensity = skew angle D = rotor diameter δ = local instantaneous wake width θ = rotor yaw angle C T = rotor thrust coefficient dy yaw = lateral displacement due to the yaw error T gen = generator torque = generator speed K = generator torque constant in Region 2

show abstract

“…The concept was used by Bai et al to characterize the development of the wake flow behind a fractal tree and was found to be suitable in the description of transverse momentum fluxes [24]. The approach of describing the Reynolds stress using the eddy viscosity is also applied in the context of wind energy application, where it is in part used in the Dynamic Wake Meandering Model (DWD), which is a computationally inexpensive tool to describe wake effects [25]. Herein, the eddy viscosity and mixing length are calculated from data measured in wind tunnel experiments.…”

Section: Introductionmentioning

confidence: 98%

Wake to wake interaction of floating wind turbine models in free pitch motion: An eddy viscosity and mixing length approach

et al. 2016

View full text Add to dashboard Cite

a b s t r a c tExperiments were performed using two model wind turbines operated in tandem with a bottom-fixed configuration and a floating configuration with both turbines allowed to freely oscillate in the streamwise direction. Wakes of both turbines were measured using stereoscopic Particle Image Velocimetry. Turbulent characteristics of the far wake of the first turbine acting as the inflow for the downwind turbine were characterized calculating the eddy viscosity and mixing length profiles from the obtained data. The influence of the far wake on the statistical properties of the near wake of the second turbine are compared between the fixed and oscillating configurations. The incoming mixing length clearly influences the Reynolds stresses and turbulence production of the near wake in the shear layer. Below, the connection between incoming mixing length and the near wake is less evident, due to the impact of the nacelle and rotation of the rotor. For the oscillating turbine, the Reynolds stresses and turbulence production in the near wake of the downwind turbine are damped. Vertical fluctuations were found to decrease though an increase in the mean vertical component. New challenges arise in the design of a floating offshore wind farms, in terms of farm layout and load estimations.

show abstract

Implementation of a Mixing Length Turbulence Formulation Into the Dynamic Wake Meandering Model

Cited by 35 publications

References 12 publications

Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build‐up in a row of wind turbines

Two improvements to the dynamic wake meandering model: including the effects of atmospheric shear on wake turbulence and incorporating turbulence build‐up in a row of wind turbines

Implementing the Dynamic Wake Meandering Model in the NWTC Design Codes

Wake to wake interaction of floating wind turbine models in free pitch motion: An eddy viscosity and mixing length approach

Contact Info

Product

Resources

About