Design of Risk Mitigation Strategies for Wind Farms Using … · 2020-02-05 · Design of Risk Mitigation Strategies for Wind Farms Using Detached-Eddy Simulation with Turbulent Inflow

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Design of Risk Mitigation Strategies for Wind Farms Using Detached-Eddy Simulation with Turbulent Inflow

Yavor Hrsitov

Vestas Power Solutions, DK

([email protected])

mailto:[email protected]

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VestasFOAM- CFD platform for siting and new concept development

• A fully integrated OpenFOAM-based CFD package developed by Vestas’ CFD group.

• Interfaces designed for streamlined and automated procedures including pre- and post-processing.

2 Vindkraftnet, June 01, 2017

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Accuracy vs. Cost

• Steady RANS (Level 1/2)

• Unsteady RANS (Level 2.5)

• Hybrid RANS-LES /Detached-Eddy Simulation (DES) (Level 3)

• Large-Eddy Simulation (LES) (Level 4/5)

• Direct Numerical Simulation (DNS)

Accuracy

Cost

Win

d s

peed

Time

Vindkraftnet, June 01, 2017 3

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Why DES? • A combination of RANS and LES to compromise between accuracy and cost.

• Start from a basic RANS model (e.g. k-ε, k-ω, Spalart-Allmaras, etc.);

• Estimate the turbulence length scale (eddy size) lT as a function of turbulence variables (e.g. lT ~ k1/2/ω for the k-ω RANS model).

• If lT < Δ, eddies cannot be resolved with the current mesh: remain as RANS.

If lT > Δ, eddies can be resolved with the current mesh: switch to LES.

ￚ Increase the dissipation;

ￚ Reduce TKE;

ￚ Reduce the eddy viscosity.

• k-ω SST-based DES model by Menter et al. (2003) was chosen in order to maintain the analogy with two-equation RANS models (e.g. for direct term-by-term comparisons with our RANS CFD results):

• In-house OpenFOAM class kOmegaSSTDES was created by converting the RANS class kOmegaSST.

RANS mode LES mode


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Level 3 (DES) set-up and report with results

X

Z

301200 301400 301600 301800

200

400

600


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Mann turbulent inflow vs. Log low inflow


Log Low inflow

Mann turbulent inflow

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Probability-density distributions of wind speed and wind direction


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Probability-density risk analysis for extreme wind conditions


,)(64.1

%5

VmV

DES

V

V VdVPP

,)(64.1

%5

dPP DES

.)(64.1

%5

dPP DES

∆Vm: mean velocity difference; V : sdv velocity difference; : sdv direction difference. (from the IEC standard Gaussian curve)

WTG09, sector 270

!!%!4.45%5 VP !!%!8.33%5 P !!%!1.25%5 P

WTG16, sector 060 WTG14, sector 210

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Probability-density risk analysis for extreme wind conditions


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Validation case 1


• Negative wind shear measured at #19353

3000 3050 3100 3150 3200 3250 3300 3350 34000

10

20

30

40

Time [s]

WindHub

Sy s413

3000 3050 3100 3150 3200 3250 3300 3350 34001800

1900

2000

2100

Time [s]

GenSpd

GenSpdRf

3000 3050 3100 3150 3200 3250 3300 3350 34000

1000

2000

3000

Time [s]

APMGrid

APMGRef

3000 3050 3100 3150 3200 3250 3300 3350 34000

10

20

30

Time [s]

Sy s238

3000 3050 3100 3150 3200 3250 3300 3350 34002069

2069.5

2070

2070.5

2071

Time [s]

Sy s50

3000 3050 3100 3150 3200 3250 3300 3350 3400-25-20-15-10-505

10152025

Time [s]

Win

d S

peed d

iff. [m

/s]

*VDif f *

JP: VDAT 2.4

06-Dec-2005 13:20:03

#19353 V80; SW-rel:20764;

C:\VMP\19353\vdf\05092902.VDF; 2005-Sep-29 14:56:31 - 2005-Sep-29 17:36:32 (9600.564 [s])

SmartScaling:0.01*Sys238

0.01*Sys2390.01*Sys240

0.1*Sys413

Win

dH

ub

Max.=32.70; Min.=5.20 Avg.=20.94 m/s; 'Turb.Int.=19 %

GenS

pd

Max.=2015.40; Min.=1807.10 Avg.=1876.22; Std.=39.44

AP

MG

rid

Max.=2532.80; Min.=770.60 Avg.=1793.38; Std.=177.36

Sys238: P

itchP

osM

easS

ysA

*

Max.=28.31; Min.=9.67 Avg.=21.40; Std.=2.80

Sys50: [d

ø] N

acelle

pos.

Max.=2070.00; Min.=2070.00 Avg.=2070.00; Std.=0.00

Max.=12.80; Min.=-20.90Avg.=-3.56; Std.=4.66

WTG name sector240 sector270 sector300

WTG19359 0 0 0

WTG19365 0 0 0

WTG19351 0 0 0

WTG19344 0 0 0

WTG19346 0 0 0

WTG19354 29.947 0 0

WTG19352 31.215 0 0

WTG19356 1.123 0 0

WTG19350 3.274 0 0

WTG19349 38.934 0 0

WTG19369 42.995 18.671 0

WTG19348 1.284 0.945 0

WTG19361 0 1.537 0

WTG19363 0 11.369 0

WTG19367 2.889 14.219 0

WTG19368 0.947 4.404 0

WTG19357 0.465 60.48 0

WTG19355 1.733 0.224 25.442

WTG19343 1.268 0 54.815

WTG19353 59.653 0 5.769

WTG19370 3.049 0 0.385

WTG19342 0.818 0 0

WTG19341 0 0 9.231

WTG19345 2.215 1.473 0.726

WTG19364 6.965 0 0.1

WTG19360 4.333 0 8.818

WTG19362 3.659 0 0

WTG19366 0.193 0 0

WTG19358 0 0 5.157

WTG19347 0 0 2.222

WTG name sector240 sector270 sector300

WTG19359 0 0 0

WTG19365 0 0 0

WTG19351 0 0 0

WTG19344 0 0 0

WTG19346 0 0 0

WTG19354 29.947 0 0

WTG19352 31.215 0 0

WTG19356 1.123 0 0

WTG19350 3.274 0 0

WTG19349 38.934 0 0

WTG19369 42.995 18.671 0

WTG19348 1.284 0.945 0

WTG19361 0 1.537 0

WTG19363 0 11.369 0

WTG19367 2.889 14.219 0

WTG19368 0.947 4.404 0

WTG19357 0.465 60.48 0

WTG19355 1.733 0.224 25.442

WTG19343 1.268 0 54.815

WTG19353 59.653 0 5.769

WTG19370 3.049 0 0.385

WTG19342 0.818 0 0

WTG19341 0 0 9.231

WTG19345 2.215 1.473 0.726

WTG19364 6.965 0 0.1

WTG19360 4.333 0 8.818

WTG19362 3.659 0 0

WTG19366 0.193 0 0

WTG19358 0 0 5.157

WTG19347 0 0 2.222

Results for sector 240 for #19353 show 59.653 probability for negative wind shear outside IEC norm which is in line with the Negative wind shear report.

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Validation case 2


Negative wind shear measured at WTG15 ( #6134)

600 sec. statistics from turbine #6134 (V47)

Data: C:\MatlabApp\VdatOut\Stat\6134\600s

(1782 points in 35 f iles)

0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

45

WindHubAvg

Win

dH

ubS

tdN

orm

WindHubStdNorm

0 5 10 15 20 25 300

5

10

15

20

25

30

35

40

45

Sys325Avg

Sys325S

tdN

orm

Sy s325StdNorm

0 5 10 15 20 25 30-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

WindHubAvg

*VD

iff*

Avg

*VDif f *Av g

*VDif f *Min

*VDif f *Max

0 5 10 15 20 25 30-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

PS3.A

PS3.B

WindHubAvg

*VD

iff*

Avg0.3

sA

vg

*VDif f *Av g0.3sAv g

*VDif f *Av g0.3sMin

*VDif f *Av g0.3sMax

0 5 10 15 20 25 30-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

PS3.A

PS3.B

WindHubAvg

*VD

iff*

Avg0.3

sC

D1%

*VDif f *Av g0.3sCD1%

0 5 10 15 20 25 30-200

-100

0

100

200

300

400

500

600

700

800

900

1000

WindHubAvg

APM

GridA

vg

APMGridAv g

APMGridMax

APMGridMin

0 5 10 15 20 25 30-3

-2.5-2

-1.5-1

-0.50

0.51

1.52

2.53

3.54

4.55

WindHubAvg

Sys65A

vg

Sy s65Av g

Sy s65Max

Sy s65Min

Sy s65Std

0 5 10 15 20 25 30-4

-3.5-3

-2.5-2

-1.5-1

-0.50

0.51

1.52

2.53

3.54

WindHubAvg

Sys66A

vg

Sy s66Av g

Sy s66Max

Sy s66Min

Sy s66Std

02051603.MAT:

28204.32-28804.31 sec.

02051603.MAT:

27604.23-28204.22 sec.

02051405.MAT:

5400.84-6000.83 sec.

WTG name sector330 sector000

WTG15 0 61.8

WTG16 0 3.261

WTG17 0 0.225

Results for sector 000 for WTG15 ( #6134 ) show 61.8 % negative wind shear outside IEC norm which is in line with the Negative wind shear report.

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Validation case 3


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Validation case


07

07

06

06

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70 deg Sector DES


WTG07

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High-fidelity CFD simulation of forest – benefits • A more thorough understanding of underlying flow physics including turbulence structure

evolution:

(Brown & Roshko 1974)

(Finnigan 2000)

(Large-eddy simulation by Finnigan et al. 2009)


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Sanz (2003) forest model for k-ε RANS simulation

•

•

•

•

•

•

𝐷𝑘

𝐷𝑡= 𝑃 − 𝜀 + 𝑃𝐶 − 𝐷𝐶 ⋯

𝐷𝜀

𝐷𝑡=𝐶1𝑃 − 𝐶2𝜀

𝑇+ 𝐶3

𝑃𝑐 − 𝐷𝑐𝑇

⋯

𝑃𝐶 = LAD × 𝐶𝐷𝛽𝑃𝑈3

𝐷𝐶 = LAD × 𝐶𝐷𝛽𝐷𝑈𝑘

𝛽𝐷 = 𝐶𝜇2

𝑎

23

𝛽𝑃 +3

𝜎𝑘

𝐶3 = 𝜎𝑘2

𝜎𝜀−

𝐶𝜇

6

2

𝑎

23

𝐶2 − 𝐶1

(𝑎 = 0.05)


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CFD forest model for RANS simulation • Source terms added within the forest:

𝐷𝒖

𝐷𝑡= ⋯− LAD × 𝐶𝐷 𝒖 𝒖

FLOW


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Validation case- DES with forest

• Data for alarms in sector from -005° to +005° for 7 years. • Only the 000° sector was simulated with DES. • Results showing reasonable improvement with the forest model activated are

marked in blue. • Alarms can be triggered by non-flow related (mechanical) reasons as well

18

WTG % of Max # alarms DES with forest: negative shear

DES without forest: negative shear

00 16.75% 14.59% 13.21%

01 36.93% 37.82% 32.10%

02 18.25% 35.01% 25.30%

03 17.37% 1.07% 0.11%

04 12.37% 12.45% 1.31%

05 14.30% 4.76% 0.35%

06 15.09% 11.97% 2.24%

07 100.00% 97.20% 29.20%

08 43.77% 2.45% 0.00%

09 8.77% 0.00% 0.00%

10 8.42% 7.59% 1.50%

11 23.25% 0.00% 0.00%

12 17.98% 0.00% 0.00%

13 15.44% 2.19% 2.90%

14 11.93% 12.16% 2.69%

15 23.07% 3.20% 2.32%

16 16.40% 0.00% 0.00%

17 9.12% 0.00% 0.00%

18 13.42% 11.81% 4.41%

19 18.86% 0.00% 0.00%

20 10.96% 9.38% 3.69%

21 14.56% 61.09% 2.30%

22 6.32% 20.13% 20.90%

Vindkraftnet, June 01, 2017

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Discussion

1. Mesh resolution ( horizontal, vertical)

2. Turbulent inflow conditions to trigger switch from RANS to LES

3. Tuning F1 and F2 blending functions in the DES formulation for ABL applications

Gritskevich et al, Flow Turbulence Combust (2012) 88:431–449,DOI 10.1007/s10494-011-9378-4

4. Forest with DES- further validation


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Design of Risk Mitigation Strategies for Wind Farms Using … · 2020-02-05 · Design of Risk Mitigation Strategies for Wind Farms Using Detached-Eddy Simulation with Turbulent Inflow