Comparison between linear (WAsP) and CFD model (Meteodyn) on a potential project RANS equation with one-equation closure scheme (k-L turbulence model) Project covers an area of 11km x 8km Equipped with 12 meteorological masts (recording from 6 months to 6 years of data) Relatively complex (deep valleys, ridges, rolling mountains) Mix of coastal and inland areas

1. Comparison and Terrain Influence on
Predictions with Linear and CFD
Models
CANWEA Annual Conference, Vancouver, BC
October 04, 2011
GILLES BOESCH, M.Eng, Wind Project Analyst
Hatch (Montreal), Canada

2. Overview
• Introduction
• Presentation of a test case
• Model comparison, terrain influence
• Conclusions and investigations
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3. Introduction
• CFD is now well established in the wind
industry
• Need to quantify the uncertainty associated
to these models
• Compare the errors with linear models
• Influence of the errors with topography
complexity – And how to deal with it
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4. Test case
• Comparison between linear (WAsP) and CFD
model (Meteodyn) on a potential project
• RANS equation with one-equation closure
scheme (k-L turbulence model)
• Project covers an area of 11km x 8km
• Equipped with 12 meteorological masts
(recording from 6 months to 6 years of data)
• Relatively complex (deep valleys, ridges,
rolling mountains)
• Mix of coastal and inland areas
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5. Test case
Altitude RIX
Masts
(m) (%) • Forest diversity:
M1 540 10.1
– Logged area
M2 560 11.0
M3 421 22.4
– 15m high trees
M4 420 17.9 – Regrowth
M5 448 15.1
M6 521 16.6
• RIX (Ruggedness Index)
M7 560 8.0 – % of slopes >30% in a 3500m radius
M8 433 22.1
– RIX Variations:
M9 440 11.8
• 2 to 25 over the entire project
M10 665 14.3
M11 567 2.7 • 2.7 to 22.4 at the meteorological masts
M12 540 12.1
Variety of conditions to evaluate the
behavior of the models
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6. Test Case
• Meteodyn settings :
– Structured Mesh (30m cell size within the
project area)
– Use of a forest model (windflow over canopy)
– Neutral stability class assumed (can induce
errors for sea shore sites) – Resulting shear
verified for some masts
• Data :
– Measured and Quality controlled
– At 50m or 60m high (to avoid extrapolation
errors)
– Adjusted to long term with standard MCP
method (to have the same reference)
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7. Results – Methodology
• Cross-Prediction Matrix
– Predictors : Mast that predicts the others
– Predicted : Wind Speed at the « Predicted Mast »
Predicted
M1 M2 M3 … M12
M1 M1 measured
M1 predicts
M2
M2 M2 predicts
M2 measured
Predictor
M1
M3 M3 measured
… …
M12 M12 measured
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8. Results - Methodology
• Cross-Prediction Matrix
– 12 x 12 matrix 132 cross predictions
– For both WAsP and Meteodyn
– No correction is applied to both models output
– Correction often applied with WAsP because of
wind speed inconsistencies in complex terrain
• Converted into a Relative Error Matrix :
V predicted Vmeasured
%E
Vmeasured
• Resulting in 132 relative error values for
each cross-prediction
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9. Altitude
Masts RIX (%)
(m)
M1 540 10.1
M2 560 11.0
Results - Comparison M3
M4
M5
M6
421
420
448
521
22.4
17.9
15.1
16.6
M7 560 8.0
M8 433 22.1
M9 440 11.8
M10 665 14.3
• Mean absolute errors
M11 567 2.7
M12 540 12.1
Prediction Errors
25.0%
20.0%
15.0%
Error (%)
WAsP Error
10.0% Meteodyn Error
5.0%
0.0%
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
< 2km from
water
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10. Results - Comparison
• Absolute errors (direct output from models)
WAsP Meteodyn
Min Error 0.0% 0.0%
Max Error 34.0% 14.1%
Average 7.7% 4.6%
• On average, reduction of the error by 40%.
• Some exceptions : 33 cases out of 132
show better results with WAsP
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11. Results - Comparison
• Generally, errors from both models have
the same sign (positive/negative)
40.0%
30.0%
20.0%
Relative Error (%)
WAsP
10.0%
Meteodyn
0.0%
-10.0%
-20.0%
• The difference is in the magnitude
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12. Results – RIX Analysis
• RIX dependency:
– WAsP : Error increase sharply when RIX > 15%
– Meteodyn : Error is more constant
RIX influence on cross-prediction errors
25.0%
20.0%
Average Error (%)
15.0%
Wasp
Meteodyn
10.0%
5.0%
0.0%
0.0 5.0 10.0 15.0 20.0 25.0
RIX (%)
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13. Results – RIX Analysis
• RIX dependency:
– Possibility to correct WAsP with ΔRIX (between
2 masts)
– Correction based on a correlation between
logarithmic error and ΔRIX for each cross-
prediction : E(%) = A* ΔRIX + B
– Can we correct Meteodyn based on the RIX ?
Error vs dRIX - Meteodyn Error vs dRIX - Wasp
40.0% 40.0%
30.0% y = 0.5552x 30.0% y = 1.0632x
R² = 0.6345 R² = 0.7025
20.0% 20.0%
Error (%)
Error (%)
10.0% 10.0%
0.0% 0.0%
-30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0%
-10.0% -10.0%
-20.0% -20.0%
-30.0% -30.0%
ΔRIX (%) ΔRIX (%)
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14. Results – RIX Analysis
• CFD RIX dependency:
– Error increases when ΔRIX increases
– Error and ΔRIX seem to be correlating (not as
good than Wasp however)
– The slope is lower for Meteodyn
 Influence of site topography differences is lower
Error vs dRIX - Meteodyn Error vs dRIX - Wasp
40.0% 40.0%
30.0% y = 0.5552x 30.0% y = 1.0632x
R² = 0.6345 R² = 0.7025
20.0% 20.0%
Error (%)
Error (%)
10.0% 10.0%
0.0% 0.0%
-30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% 30.0%
-10.0% -10.0%
-20.0% -20.0%
-30.0% -30.0%
ΔRIX (%) ΔRIX (%)
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15. Results – RIX Analysis
• Wasp RIX Correction:
– 12 towers available
– Equation based on 11 towers and evaluate how
it corrects the 12th tower
Prediction Errors
25.0%
20.0%
15.0%
Error (%)
WAsP Error
Meteodyn Error
10.0% WAsP RIX Corrected Error
5.0%
0.0%
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
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16. Results – RIX Analysis
• Meteodyn RIX Correction:
– Same methodology with updated correction
equation
Prediction Errors
25.0%
20.0%
15.0%
WAsP Error
Error (%)
WAsP RIX Corrected Error
Meteodyn Error
10.0%
Meteodyn RIX Corrected Error
5.0%
0.0%
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12
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17. Results – RIX Analysis
• Summary of average error:
Wasp 7.7 %
Wasp RIX Corrected 4.3 %
Meteodyn 4.6 %
Meteodyn RIX Corrected 3.1 %
– RIX correction with Meteodyn produces
promising results
– Reduction by 44% of the error after correcting
Wasp with the RIX.
– Reduction by 33% of the error after correcting
Meteodyn with the RIX.
– RIX correction with Wasp compared to
Meteodyn direct output shows similar errors.
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18. Conclusions
• In general, a project in complex terrain
requires lots of masts
• An alternative is the use of a CFD model
but linear corrected models can give good
results too
• Only few litterature over relation between
RIX and CFD models
• But quantification of CFD errors is more
complex (topography / volume
discretisation, forest model etc.)
 In some cases error is bigger
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19. Conclusions
• To go further :
– Try with concurrent data (when possible) to
avoid MCP related errors
– How does RIX correction with CFD performs for
other sites ?
– Introduction of new complexity index (takes
into account RIX, distance, vegetation,
stability…)
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20. Thank you for your attention
Gilles Boesch, M.Eng
Wind Project Analyst
Hatch Ltd
GBoesch@hatch.ca
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