Vrije Universiteit Brussel

CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscalepredictionsStergiannis, Nikolaos; Van Beeck, Jeroen; Runacres, Mark

DOI:10.13140/RG.2.2.29343.97441

Publication date:2017

Document Version:Final published version

Link to publication

Citation for published version (APA):Stergiannis, N., Van Beeck, J., & Runacres, M. (2017). CFD simulations of the Egmond aan Zee wind farm usinghigh resolution mesoscale predictions. Poster session presented at Resource Assessment 2017, Edinburgh ,United Kingdom. https://doi.org/10.13140/RG.2.2.29343.97441

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.

• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal

Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.

Download date: 17. Sep. 2020

Download the poster

windeurope.org/summit2016#windsummit2016

Minimum4.199 m/s

Maximum10.213 m/s

Average7.028 m/s

Free-stream flow1D upstream

8.282 m/s

WT-8Rotor position

6.236 m/s

Wake velocity1D downstream

5.056 m/s

0

2

4

6

8

10

12

14

-90 -45 0 45 90

Velo

city [

m/s

]

Axial distance over the hub heigth [m]

WT-8 measurements WT-8 CFD simulations

Minimum4.814 m/s

Maximum12.006 m/s

Average8.373 m/s

Free-stream flow1D upstream

8.704 m/s

WT-7Rotor position

6.429 m/s

Wake velocity1D downstream

4.968 m/s

0

2

4

6

8

10

12

14

-90 -45 0 45 90

Velo

city [

m/s

]

Axial distance over the hub heigth [m]

WT-7 measurements WT-7 CFD simulations

21 m height7.43 m/s

70 m height8.455 m/s

116 m height9.118 m/s

21 m height9.019 m/s

70 m height8.755 m/s

116 m height9.599 m/s

0

70

140

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Heig

ht

[m]

Axial velocity [m/s]

Wind Mast CFD simulations Wind Mast measurements

WT-7 WT-8

Wind Mast

windeurope.org/ra17#windra17

CFD simulations of the Egmond aan Zee wind farm using high resolution mesoscale predictions

N. Stergiannis1,2,3, J. van Beeck2, M. C. Runacres1

1Vrije Universiteit Brussel, 2Von Karman Institute for Fluid Dynamics, 33E S.A.

[1] Weather Research & Forecasting Model. URL: www.wrf-model.org

[2] Openfoam.org. URL: www.openfoam.org

[3] Hargreaves D. M. and Wright N. G.: On the use of the k–ε model in commercial CFD software to model the neutral atmospheric boundary layer, 2007.

[4] Prospathopoulos J. M., Politis E. S. and Chaviaropoulos P. K.: Modelling wind turbine wake in complex terrain.

[5] Stergiannis N., Lacor C., Van Beeck J. and Donnelly R.: CFD modelling approaches against single wind turbine wake measurements using RANS, 2016.

[6] Stergiannis N., Van Beeck J. and Runacres M. C.: Full HAWT rotor CFD simulations using different RANS turbulence models compared with actuator disk and experimental measurements, 2017.

Due to the complexity and uncertainty involved in the process of making power fromwind, more and more advanced tools are being developed to maintain thesustainability and the growing trend of the wind industry. Prior to the development ofa wind farm project, measured data are provided by limited installed wind masts atthe site under investigation and by other nearby weather stations. Therefore, thewind resource assessment depends on the uncertainties and limitations of thosemeasurements. To improve the reliability and limit the risks, weather predictionforecasting models can be employed in parallel with measurements, to investigate thelocal wind map and the potential wind power. Nevertheless, the physics involved atthe inter-turbine or smaller scales cannot be captured by mesoscale modelling. Toobtain predictions of such scales, high resolution mesoscale models are coupled withmicro-scale computational fluid dynamics (CFD) simulations.

Results of neutral atmospheric stability over a defined wind sector have beenaveraged in time and extracted from mesoscale simulations using the WeatherResearch and Forecasting model (WRF) [1] with a very fine resolution (150 m). Thoseresults were used to provide the inlet conditions of the micro-scale CFD simulationswhich were performed using the open-source CFD software OpenFOAM [2]. Thepredicted time-averaged atmospheric flow within the Egmond aan Zee wind farm iscompared for both numerical approaches. The wind farm’s total power estimationsare compared to operational SCADA data.

PO.007

Abstract CFD results and conclusions

References

Computational domain and boundary conditions

Table 1: Boundary conditions Table 2: Initial conditions

Previous studies indicate that the single wake expansion is very sensitive to the choice ofturbulence model [5, 6]. The impact of using the k-ε turbulence model in multiple wake effectsshould be accounted for.

Table 3: Governing equations

Table 4: Modified k-ε turbulence model coefficients (Ref. [4])

Figure 3: CFD simulations against SCADA data (top) and wind mast measurements (bottom)

Figure 1: Inlet velocity profile

The computational mesh was generated with blockMesh and snappyHexMesh. Thefinal grid size was 15Mi cells, covering a distance of 15 km length, 11 km width and1 km height. An inlet velocity profile has been derived for the Atmospheric BoundaryLayer (ABL) using high-resolution mesoscale modelling and the governing equationsof Table 3. Under the assumption of a homogeneous flow and of local equilibriumbetween the production 𝑘 and dissipation 𝜀 of turbulence, Eq. 1, 2 and 3 can beused [3]. For the ABL parametrization [Table 2], a plane at a distance of 25𝐷(𝐷 = 90 m) upstream has been extracted from the mesoscale model, using a time-window of 5 hours in which an average wind speed of 9.37 m/s and wind direction of50° (±5°) was observed. The velocities have been averaged over the plane and overavailable mesoscale heights in the range of 0 − 1 km height [Figure 1]. An averageturbulence intensity (TI) of 8.943% has been extracted over the hub height (65 m)and used to estimate the turbulence kinetic energy production from Eq. 4. The 𝑢∗ iscalculated by using Eq. 2 and 4. Then Eq. 5 is used to estimate a consistent 𝑧0.

Figure 2: Velocity contours over the hub height (𝑧ℎ𝑢𝑏 = 65 m)(Top: wind farm layout. Bottom: WT-7, WT-8 and wind mast locations)

A qualitative comparison of measured and modelled wind speeds has been done. Preliminaryresults over the selected wind sector, suggest that WT-7, WT-8 as well as the wind mast arewithin multiple wake effects from the upstream wind turbines [Figure 2].

The predicted free-stream flows from the CFD simulations are in good agreement with theaveraged measured velocities and within the minimum-maximum range of the available SCADAdata for WT-7 and WT-8 [Figure 3, top] using a wind direction of 50° (±5°). On the other hand,the agreement between the measurements and the simulations in not so good at 21 m height(below the WT rotors) [Figure 3, bottom].

DirichletFixed value

von NeumannZero gradient

Inlet 𝑈, 𝑘, 𝜀 𝑝

Top 𝑈, 𝑘, 𝜀 𝑝

Outlet 𝑝 𝑈, 𝑘, 𝜀

Bottom ABL wall function

Sides symmetry conditions

WT actuator disk model

𝑈𝑟𝑒𝑓 9.37 m/s

𝑧𝑟𝑒𝑓 65 m

𝑘 1.0432 m2/s2

𝜀 0.0031 m2/s3

𝑧0 0.00956 m

𝑢 =𝑢∗𝜅ln

𝑧 + 𝑧0𝑧0

(Eq. 1)

𝑘 =𝑢∗2

𝐶𝜇(Eq. 2)

𝜀 =𝑢∗3

𝜅 𝑧 + 𝑧0(Eq. 3)

𝑘 =3

2ഥ𝑈𝑇𝐼 2 (Eq. 4)

𝑢∗ =𝜅𝑈𝑟𝑒𝑓

𝑙𝑛𝑧𝑟𝑒𝑓 + 𝑧0

𝑧0

(Eq. 5)

𝐶𝜇 𝐶𝜀1 𝐶𝜀2 𝜎𝜅 𝜎𝜀 𝜅

0.033 1.44 1.92 1.0 1.3 0.41