Present 14 08-2012

CFD EVALUATION OF SUITABLE SITE FOR WIND TURBINE OF THE

FLOW OVER TERRAIN

THITIPONG UNCHAI Advisor

ASST.PROF.DR.ADUN JANYALERTADUN

∗ Introductions ∗ Objective ∗ Scope of works ∗ Research Procedure ∗ Theory, Literature reviews and Results of

∗ Potential Energy Assessments ∗ CFD simulations

∗ Pha Taem Hill ∗ Comparison of terrain and geometry shape

∗ Chart generation ∗ Discussions

Topics

∗ Investigate wind energy potential of Ubonratchathani region.

∗ Find suitable site of wind turbine using CFD simulations.

∗ Generate suitable site chart for trapezoid hill shape.

Objective

∗ Wind data collection at 10, 30 40 m height. ∗ Wind energy potential assessment using Weibull

distribution. ∗ CFD simulation of Pha Taem hill. ∗ CFD simulation compare of terrain and geometry

shape. ∗ Generate wind turbine suitable site chart using

extrapolation technique.

Scope of the work

∗ Wind energy

Introduction

Source : http://www.jewishpolicycenter.org/1415/investment-opportunity-of-the-21st-century

Wind Power Class

At a height of 10 m Height of 50 m

Wind Power

Density (W/m2)

Speed (m/s)

Wind Power Density (W/m2)

Speed (m/s)

1 0 – 100 0 – 4.4 0 – 200 0 – 5.6

2 100 – 150 4.4 – 5.1 200 – 300 5.6 – 6.4

3 150 – 200 5.1 – 5.6 300 – 400 6.4 – 7.0

4 200 – 250 5.6 – 6.0 400 – 500 7.0 – 7.5

5 250 – 300 6.0 – 6.4 500 – 600 7.5 – 8.0

6 300 – 400 6.4 – 7.0 600 – 800 8.0 – 8.8

7 400 – 1000 7.0 – 9.4 800 – 2000 8.8 – 11.9

Introduction

Source : The U.S. Dept. of Energy defined a wind power scale in the Wind Energy Resource Atlas of the United States, published in 1986.

∗ Wind turbine site

Introduction

∗ Wind potential site in Thailand

Introduction

Region Province Power Class Wind speed

(50 m) Wind power

Tai Rom Yen National Park Nakhon Si Thammarat 6 - 7 8.00 – 11.90 600 – 2,000

Khao Luang National Park Nakhon Si Thammarat 6 – 7 8.00 – 11.90 600 – 2,000

Khao Pu - Khao Ya National Park Phatthalung 6 – 7 8.00 – 11.90 600 – 2,000

Wong – Jao National Park Tak 6 8.00 – 8.80 600 – 800

Doi Inthanon Chiang Mai 4 7.00 – 7.50 400 – 500

Kaeng Krung National Park Surat Thani 4 – 5 7.00 – 8.00 400 – 600

Pranom-Benja National Park Krabi 6 8.00 – 8.80 600 – 800

Ranot Songkhla 4 7.00 – 7.50 400 – 500

Songkhla Lake Songkhla 5 – 6 7.50 – 8.00 500 – 700

Gulf of Pattani Pattani 4 7.00 – 7.50 400 – 500

Hua Sai Nakhon Si Thammarat 3 6.40 – 7.00 300 – 400

∗ Alternative Energy Development Plan: 2012-2021

Introduction


Introduction

Source : http://www.acusim.com/html/apps/windTurbSiting.html


Introduction

Source : http://www.lec.ethz.ch/research/wind_energy/cfd


Introduction

Source : Paul Stangroom. CFD Modelling of Wind Flow Over Terrain. Ph.D. thesis of University of Nottingham. 2004


Introduction

Source : Keith W. Ayotte. Computational modelling for wind energy assessment. Journal of Wind Engineering and Industrial Aerodynamics. 96: 2008, 1571–1590

∗ Wind measurement and potential energy assessment. ∗ Simulation of Pha Taem hill. ∗ Comparison of terrain and geometry shape. ∗ Generate suitable position site chart.

Research Procedure

∗ Literature reviews

* Extrapolation from the data using the Power-Law are presented.

Wind measurement

Researchers Heights Periods

A. Keyhani et al. 10 m 11 years

Ramazan Kose 10, 30 m 20 months

Meishen Li 10 m 5 years

Murat Gokcek et al. 10 m 5 months

Murat Gokcek et al. 6 – 12 m (11 stations) 3 years

W. Al-Nassar et al. 10, 30, 60 m* 46 years

Parameters Details

Location Khong Chiam, UBN

Height from sea level 123 m

Sampling Period 1 hr

Data collected January 1, 2008 to December 31, 2010

Wind measurement

Anemometer

Anemometer

Wind Vane

Data Logger

Anemometer

Thermometer &

Barometer

∗ Wind speed

Wind measurement

0.0

1.0

2.0

3.0

4.0

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Win

d Sp

eed

(m/s

)

Month

2008 2009 2010 Average

Wind measurement

0.0

1.0

2.0

3.0

4.0

0:00

2:00

4:00

6:00

8:00

10:0

0

12:0

0

14:0

0

16:0

0

18:0

0

20:0

0

22:0

0

Win

d Sp

eed

(m/s

)

Hour of Day

10 m 30 m 40 m

∗ Wind direction

Wind measurement

0%

5%

10%

15% N

NNE NE

ENE

E

ESE

SE SSE

S SSW

SW

W

NW

∗ Power of the wind

∗ Wind power density developed by Weibull distribution

where k is shape factor and c is scalar factor

Potential energy assessment

3

21 AVP ρ=

∫∞

+

Γ==0

33 321)(

21

kkcdVVfV

AP ρρ

∗ Weibull distribution ∗ Graphical method

∗ Approximated method


086.1−

=

mVk σ

( )kVc m

/11+Γ=

−= kb

ec

( )( )[ ] ( ) ( )00 lnln1lnln VkckVVF +−=≤−−

Month Vm

Meteorological Weibull approximated Weibull graphical

P/A (W/m2)

E/A (kWh/m2)

P/A (W/m2)

E/A (kWh/m2)

P/A (W/m2)

E/A (kWh/m2)

January 3.91 36.61 27.24 85.97 63.96 82.23 61.18 February 3.72 31.53 21.19 62.39 41.92 59.79 40.18 March 3.64 29.54 21.98 64.05 47.65 61.31 45.61 April 3.31 22.21 15.99 103.17 74.28 93.59 67.39 May 3.23 20.64 15.36 78.47 58.38 88.11 65.55 June 3.29 21.81 15.70 111.25 80.10 97.45 70.17 July 3.50 26.26 19.54 171.73 127.77 122.22 90.93 August 3.53 26.94 20.04 136.13 101.28 123.13 91.61 September 2.83 13.88 10.00 97.52 70.21 91.76 66.07 October 3.46 25.37 18.88 100.86 75.04 94.96 70.65 November 4.19 45.06 32.44 113.08 81.42 80.58 58.01 December 3.73 31.79 23.65 104.42 77.69 107.07 79.66

Annual 3.53 331.65 242.00 1,229.03 899.71 1,102.20 807.01


Note : Details of wind power density calculations are indicated in Appendix B

Pha Taem hill

Geographic Information System

∗ Geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data.



Source : Atsushi Yamaguchi, Takeshi Ishihar and Yozo Fujino. Experimental study of the wind flow in a coastal region of Japan. Journal of Wind Engineering and Industrial Aerodynamics 91 (2003) 247–264



Source : Paul Stangroom. CFD Modelling of Wind Flow Over Terrain. Ph.D. thesis of University of Nottingham. 2004

Contour Terrain topography

Pha Taem hill

Parameters Details

Resolution 1 : 50,000

height 240 m

Front slope 20.06°

Rear slope 1.66°

Ground Rocky flat plate

Applying the fundamental laws of mechanics to a fluid gives the governing equations for a fluid. The conservation of mass equation

𝜕𝜕𝜕𝑡

+ 𝛻 ∙ 𝜕𝜌 = 0

and the conservation of momentum equation

𝜕𝜕𝜌𝜕𝑡

+ 𝜕 𝜌 ∙ 𝛻 𝜌 = −𝛻𝛻 + 𝜕�⃗� + 𝛻 ∙ 𝜏𝑖𝑖

Computational Fluid Dynamics

The term 𝜏𝑖𝑖 in the equation is the Reynolds stress for Reynolds Averaged Navier-Stokes equations

𝜏𝑖𝑖 = −𝑢𝑖′𝑢𝑖′ and the subgrid-scale stress for Large Eddy Simulation

𝜏𝑖𝑖 = 𝑢�𝑖𝑢�𝑖 − 𝑢𝑖′𝑢𝑖′

Computational Fluid Dynamics

Researcher Article Topics Knowledge

Ove Undheim

Comparison of turbulence models for wind evaluation in complex terrain

• Use k-l and k-ε model

• k-l model indicated more inaccuracy

Pual Carpenter and Nicholas Locke

Investigation of wind speeds over multiple 2D hills

• Use k-ω, k-ε model • Imported model

from GIS data

• k-ω model improved k-ε

Keith W. Ayotte

Computational modelling for wind energy assessment

• Comparison LES simulation with European Wind Atlas

• Wind Atlas using extrapolation method to predict speed in vertical and horizontal

D.D. Apsley and M.A. Leschziner

A new low-Reynolds-number nonlinear two-equation turbulence model for complex flows

• low-Reynolds-number flow

• Use RSM , k-ε model

• In low-Reynolds-number flow, RSM is better than k-ε

Turbulence Models dependence

• Literature reviews

∗ Standard k-ε model ∗ The two-equation k-ε turbulence model and its variants

are most commonly used in wind energy researches.

𝜇𝑡 = 𝐶𝜇𝜕𝑘2

𝜀

where k is turbulence kinetic energy ε is dissipation rate

Turbulence Models

∗ Standard k-ω model ∗ One of the advantages of the k-ω formulation is the near

wall treatment for low-Reynolds number computations.

𝜇𝑡 = 𝜕𝑘𝜔

where k is turbulence kinetic energy ω is specific dissipation rate

Turbulence Models

∗ Reynolds Stress Model ∗ Using differential transport equations for calculation of

the individual Reynolds stresses, 𝑢𝑖′𝑢𝑖′. The individual Reynolds stresses are then used to obtain closure of the Reynolds-averaged momentum equation.

Turbulence Models

Properties Coarse Medium Fine

Dimension 2,000×1,000 m 2,000×1,000 m 2,000×1,000 m

First grid cell size 0.25493 m 0.04181 m 0.00709 m

Increasing rate 8% 10% 12%

Largest cell size 70.20 m 85.64 m 96.59 m

Grid dependence

Coarse Medium Fine

∗ Grid resolution sensitivity

Grid dependence

0 1 2 3 4

x/H = 3

0 1 2 3 4

Wind Speed

x/H = 1

1.00

1.02

1.04

1.06

1.08

1.10

0 1 2 3 4

Hei

ght (

Z/H

)

x/H = 0 Coarse

Medium

Fine

Measure


Initial and boundary conditions

Researcher Article Topics

T. Takahashi et al., Turbulence characteristics of wind over a hill with a rough surface

• Increased porous fences from 0% to 100%.

Bert Blocken

CFD simulation of the atmospheric boundary layer : wall function problems

• Simulated problem of wall function.

• Irrespective of the wall functions and near wall grid resolution.

D.M. Hargreaves and N.G. Wright

On the use of the k–ε model in commercial CFD software to model the neutral atmospheric boundary layer

• Shear stress applied to the top boundary of the domain.

O. Undheim 2D simulations of terrain effects on atmospheric flow

• The influence of a roughness change spreads upwards in the boundary layer downstream from the roughness change.

Parameters Type Solver Segregated Formulation Implicit Space 3D Time Unsteady Velocity Formulation Absolute Unsteady Formulation 1st Order Implicit Turbulent Model - standard k-ε model

- standard k-ω model - Reynolds Stress Model

Near Wall Treatment Enhance Wall Treatment Model Constant Cμ , Cε1 , Cε2 , σk , σε Air Density 1.225 kg/m3 Time Step 0.1 second Max Iteration / Time Step 20,000 Inflow boundary Velocity inlet Outflow boundary Pressure outlet Top boundary Zero-gradient Ground boundary Wall

Initial and boundary conditions

∗ Wind velocity profile at reference station

Results

0

0.05

0.1

0.15

0.2

0 0.5 1 1.5 2 2.5 3 3.5 4

Hei

ght (

Z/H

)

Wind speed (m/s)

k-epsilon k-omega Reynolds stress measure

∗ Comparison of increasing speed at hill top

Results

0.00

0.05

0.10

0.15

0.20

-20% -10% 0% 10% 20%

Hei

ght (

Z/H

)

Wind speed increase (%)

k-epsilon k-omega Reynolds stress measure

∗ Comparison of velocity in longitudinal direction

Results

0

100

200

300

400

500

0 1 2 3 4

Hei

ght (

Z/H

)

x/H = -1.0

0 1 2 3 4

x/H = 0

0 1 2 3 4

x/H = 1.0

0 1 2 3 4

x/H = 2.0

∗ Comparison of turbulence kinetic energy in longitudinal direction

Results

0

100

200

300

400

500

0.00 0.05 0.10 0.15 0.20

Hei

ght (

m)

TKE (m2/s2)

x/H = -1.0

0 0.05 0.1 0.15 0.2TKE (m2/s2)

x/H = 0

0.00 0.05 0.10 0.15 0.20TKE (m2/s2)

x/H = 1.0

0.00 0.05 0.10 0.15 0.20TKE (m2/s2)

x/H = 2.0

Comparison of Pha Taem geography and geometry shape

0

0.5

0 1 2 3

Hei

ght

Distance

Geometry Geography hill

y = 0.3653x - 0.0352 R² = 0.9549

0.0

0.2

0.4

0.0 0.5 1.0

Hei

ght

Distance

y = -0.029x + 0.3994 R² = 0.9543

0

0.2

0.4

1.0 2.0 3.0 4.0Distance

∗ Comparison of increasing speed

Results

1.0

1.1

1.2

-20%-10% 0% 10% 20%

Ver

tical

hei

ght (

Z/H

)

k-epsilon model

1.0

1.1

1.2

-20%-10% 0% 10% 20%k-omega model

1.0

1.1

1.2

-20%-10% 0% 10% 20%Reynolds Stress model

∗ Comparison of increasing turbulence kinetic energy

Results

1.0

1.1

1.2

0% 10% 20% 30%

Ver

tical

hei

ght (

Z/H

)

k-epsilon model

1.0

1.1

1.2

0% 10% 20% 30%k-omega model

1.0

1.1

1.2

0% 10% 20% 30%Reynolds Stress model

Parameters Details

Hill angle 5°, 10°, 15°, 20°, 25°, 30°, 35°, 40°, 45°

Wind speed 2, 4, 6, 8, 10 m/s

Roughness height 0.0001, 0.001, 0.01, 0.1 m

Wind turbine suitable site chart Land Cover Types

Typical Roughness Length (m)

Farmland and grassy plains Many trees and hedges, a few buildings Scattered trees and hedges Many hedges Few trees (summer) Crops and tall grass Isolated trees Few trees (winter) Snow-covered cultivated farmland Large expanses of water Flat desert Snow-covered flat ground Mud flats and ice

0.002-0.30 0.30 0.15 0.085 0.055 0.050 0.025 0.010 0.002 0.0001-0.001 0.0001-0.001 0.0001 0.00001-0.00003

Wind turbine suitable site chart

0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3 0.4 0.5

heig

ht (

Tim

e of

hill

hei

ght)

length ( Time of hill height)

10°

20°

30°

40°

10

20

30

40

Suitable site chart for trapezoid terrain (Roughness 0.0001)


0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3 0.4 0.5

heig

ht

length

10°

20°

30°

40°

10

20

30

40



0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3 0.4 0.5

heig

ht (

Tim

e of

hill

hei

ght)

length ( Time of hill height)

10°

20°

30°

40°

10

20

30

40



0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3 0.4 0.5

heig

ht

length


10°

20°

30°

40°

10

20

30

40

∗ Wind energy potential of UBN regions is about 1,102 – 1,229 W/m2

∗ The simulation results of Pha Taem hill indicated that RSM method is most similar with measurements.

∗ Comparison of hill and geometry are 95.46% matching terrain with 9.72% of difference results.

∗ Extrapolation technique have been used to generated wind turbine suitable site charts.

Concluding Remarks

∗ More measure station with more frequency and more sensitive instruments should be provided to collect most accurate wind data.

∗ The measurement data from the station in single row do not indicated that same particle of the air.

∗ The 1:50,000 scales of data include much error into the simulations because GIS data non-included any obstacles.

Suggestion

Thank you

Business

Present 14 08-2012