ewea ra2013 dublin 4 2 soren ott dtu wind energy
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
1/24
28-06-2013
Fuga- Validating a wake model foroffshore wind farms
Sren Ott, Morten Nielsen & Kurt Shaldemose Hansen
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
2/24
Danmarks Tekniske Universitet
Outline
What is Fuga?
Model validation: which assumptions are tested?
Met data interpretation: Is it important?
Simple models for the impact of large scales
Illustrated with Fuga tests
Conclusions
2 28 June2013
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
3/24
Danmarks Tekniske Universitet
Fuga features*
Solves linearized RANS equations
Latest version incorporates: atmosphericstability, meandering, effects of non-stationarity and spatial de-correlation ofthe flow field.
No computational grid, no numericaldiffusion, no spurious pressure gradients
Integration with WAsP: import of windclimate and turbine data.
Fast, mixed-spectral solver:
106times faster than conventional
RANS! 108to 1010times faster than LES! Fuga development
partly sponsored by
*Sren Ott, Jacob Berg and Morten Nielsen: Linearised CFD Models for Wakes, Risoe-R-1772(EN), 2011
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
4/24
Danmarks Tekniske Universitet
Variable atmospheric stability horizontalprofiles
4
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
5/24
Danmarks Tekniske Universitet
Variable atmospheric stability verticalprofiles
5
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
6/24
Danmarks Tekniske Universitet
Model testing and assumptions
6 28 June2013
Measurements
Met mast
SCADA
PredictionsReal world
4D fields
Weather
Model world
Idealized flow
Homogeneous
Stationary
Two layers of assumptions:
1) interpretation of data interms of an idealizedmodel world
2) Flow model assumptions
Controlled experiments test onlyone assumption at a time!
Is data
interpretationimportant?
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
7/24
Danmarks Tekniske Universitet
Two different interpretations of met data
7 28 June2013
Wind direction time series
Wind direction measurements
Interpretation 1: each ten minutes period can be regarded asa piece of a stationary time series with mean = ten minutes
average. We only need to simulate the measured meandirection.
Interpretation 2: the mean value changes following the redcurve. The deviation from the red curve is statistically stationary.We need to simulate a range of directions for each measuremean direction.
Ten minutes average wind direction
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
8/24
EERA-DTOC:17/1-2013/ksh
Flow cases part 1- wide inflow sector
Thanks
to
K
urtS.Hansen
f
orthis
slide
Interpretation 1
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
9/24
EERA-DTOC:17/1-2013/ksh
Flow cases part 1 narrow inflow sector
Thanks
to
K
urtS.Hansen
f
orthis
slide
Interpretation 1
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
10/24
EERA-DTOC:17/1-2013/ksh
Flow cases part 1 narrow inflow sector
Thanks
to
K
urtS.Hansen
f
orthis
slide
Interpretation 2
Fuga + 5deg filter
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
11/24
Danmarks Tekniske Universitet
Effect of drifting winddirection can beestimated from data
Dqa= Difference of twoconsecutive ten minutesaverages of the wind direction
Width sDqa= (Dqa)2
sDqa can be obtained from 10
minutes averages.
Horns Rev 1 met mast data
Exponential pdf
Dqa
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
12/24
Danmarks Tekniske Universitet
Spatial de-correlations of wind direction
12 28 June2013
Wind dir. dif. between M6 and M7
Pdf at M7
Predicted
pdf at M6
Data
Problem: wind direction at met mast wind directions at the turbines.
This uncertainty can be estimated using Taylors hypothesis:
Delay=Dx/U
M6 M7
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
13/24
Technical University of Denmark
Spectral regimes
Courtney & Troen 1990
Larsn, Vincent & Larsen 2011
2D 3D
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
14/24
Technical University of Denmark
Meandering
14 28 June2013
Measurements with SgurrEnergys
Galion Lidar.
C
reech
etal.(2013):High-
resolutionCFD
m
odellingofLillgrundWind
farm,
RE&PQJ,
11
LES
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
15/24
Technical University of Denmark
Lillgrund wind farm
15 28 June,2013
B8
B7
B1
B6
B5
B4
B3
B2
Photo by Kurts wife
D8
D7
D1
D6
D4
D3
D2
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
16/24
Technical University of Denmark
D7 being shadowed by D8
16 28 June,2013
Fuga
SCADA
median
SCADA
LES*
*Creech et al.(2013): High-resolution CFD modelling of Lillgrund Wind farm, RE&PQJ, 11
CPU time:
Fuga: < 1 minute
LES: 105000 hours
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
17/24
Technical University of Denmark
Stochastic meandering
17
But something like this is more realistic:
The model says:
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
18/24
Technical University of Denmark
Meandering model
18 28 June2013
1) Make spectra of the differencebetween the green and the redcurves.
2) Fit to the Mann model spectra*
3) Make tracer particle diffusion inMann turbulence field using thespectra
4) Compute Lagrangian velocity auto-correlation function
5) Fit a model to it
* Pea, Gryning & Mann (2010):On the length-scale of the wind profile, Q. J. R. Meteorol. Soc. 136: 21192131
Simulating U-U
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
19/24
Technical University of Denmark
A model for turbulent diffusion in Mann turbulence
Fitting the Lagrangian velocity auto-correlation function:
v(t+t)v(t) = s2(1 + lt)lt
This model says: the acceleration is an Ornstein-Uhlenbeck process.
Analytical solution in terms of stochastic numbers.
19 28 June2013
0 50 100 150 200 250
0.1
0.0
0.1
0.2
0.3
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
20/24
Technical University of Denmark
Validation Horns Rev 1
20 28 June2013
Fuga 2.5
bin
Measurements
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
21/24
Technical University of Denmark
Validation Horns Rev 1
21 28 June2013
Fuga 2.5
bin, meandering
Measurements
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
22/24
Technical University of Denmark
Validation Horns Rev 1
22 28 June2013
Fuga 2.5
bin, meandering, decorrelation
Measurements
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
23/24
Technical University of Denmark
Horns Rev 1. Efficiency for individual turbines
23 28 June2013
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7/25/2019 EWEA RA2013 Dublin 4 2 Soren Ott DTU Wind Energy
24/24
Technical University of Denmark
Conclusions
The interpretation of met data can have a profound impact on the resultsof validation exercises for offshore turbine wake models.
The interpretation of met data can and should be backed up by on-siteobservations.
Data for narrow bins around down-the-rows wind directions are the mostuncertain.
Sampling in very small wind direction bins introduces a large uncertaintybecause we dont really know what true wind direction we should feedthe models with.
We need to know more about spatial and temporal wind field correlationsacross wind farms. The Doppler lidar is the key instrument.
Fuga is fast and yields good results. It is a useful tool.
24 28 June2013
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