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EWEA 2011 – Brussels – 16 March 2011. Feasibility of Micro Siting in Mountainous Terrain by Wind Tunnel Physical Modeling. B. Conan 1,2 , S. Buckingham 3 , J. van Beeck 1 , S. Aubrun 2 , J. Sanz Rodrigo 4. von Karman Insitute for Fluid Dynamics, Rhode-Saint-Genèse, Belgium

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Ewea 2011 brussels 16 march 2011
EWEA 2011 – Brussels – 16 March 2011

Feasibility of Micro Siting in Mountainous Terrain by Wind Tunnel Physical Modeling

B. Conan1,2,

S. Buckingham3, J. van Beeck1, S. Aubrun2, J. Sanz Rodrigo4

  • von Karman Insitute for Fluid Dynamics, Rhode-Saint-Genèse, Belgium

  • Institut PRISME, Université d’Orléans, France

  • CENAERO, Belgium

  • CENER, National Renewable Energy Center, Wind Energy department, Spain


The waudit project
The WAUDIT project

  • Wind Resource Assessment Audit and Standardization

    European Commission Marie-Curie Initial Training Networks

    Pool of 18 PhD FP7-PEOPLE-ITN-2008


Physical modeling of atmospheric flows
Physical modeling of atmospheric flows

Pedestrian comfort: European parliament

Aerodynamic design of the Belgium polar station

Wind loading on buildings

Pollution dispersion


Physical modeling assumptions
Physical modeling assumptions

  • Modeling requirements (N-S equations):

    • undistorted scaling geometry

    • equal dimensionless inflow conditions

    • Ri (Richardson)

    • Ec (Eckert)

    • Pr (Prandtl): same fluid

    • Ro (Rossby): Coriolis effect neglected in the near surface

    • Re (Reynolds): cannot be conserved

      • Ensure fully turbulent state: Re > 10 000

      • Minimum roughness Reynolds number:

      • Reynolds number dependency study if possible

cst. temp. wind tunnel neutral stratification


Modeling assumptions
Modeling assumptions

  • Modeling questions common to CFD and physical modelers:

    • reproduction of atmospheric inflow conditions

    • choice of the modeling area

    • choice of surface roughness / wall function

    • integration of the model in the domain

  • Specific topics for physical modeling:

    • Re number dependency

    • high scaling factors

    • choice of the measurement techniques


Cener test case
CENER - Test Case

  • Alaiz mountain, Pamplona, Spain:

    • 1 130 m high (600 m)

    • very complex terrain

    • steep slopes

    • dominant North wind

    • upstream ridge > 200m


Cener test case1
CENER - Test Case

  • Mock-up in the wind tunnel:

    • 1 / 5000 scale (2.8 m wide x 3 m long)

    • one direction tested

  • Model realized by UPM (Universidad politechnica de Madrid, Spain)


Modeling inlet conditions
Modeling inlet conditions

  • Parameters to reproduce:

    • velocity profile

    • roughness length ( )

    • friction velocity ( )


Modeling inlet conditions1
Modeling inlet conditions

  • ABL generation:

    • grid

    • fence

    • adaptative roughness elements


Modeling inlet conditions2
Modeling inlet conditions

  • Parameters to reproduce:

    • velocity profile

    • roughness length ( )

    • friction velocity ( )

    • turbulent profiles (3 components)

    • turbulent spectra

  •  Need for 3 components time resolved measurements


Modeling inlet conditions3
Modeling inlet conditions

  • Triple hot-wire probe

    • punctual measurement

    • U, V, W, u, v, w, u’, v’, w’

    • Iu, Iv, Iw

    • TKE

    • shear stress:


Modeling inlet conditions4
Modeling inlet conditions

  • Alaiz inlet conditions:

Wind tunnel can model different roughness length and scales Inflow reproduction challenging at very high scaling factor


Particle image velocimetry piv
Particle Image Velocimetry (PIV)

  • 2D instantaneous velocity field (U, W)

  • high spacial resolution: 150mm x 150mm with 2mm = 10m resolution

  • 500 images to perform averaging


Particle image velocimetry piv1
Particle Image Velocimetry (PIV)

  • Averaged fields:

    • velocity field

    • turbulence intensity

    • velocity vector

  • Instantaneous fields:

    • velocity field

    • shear stress

    • vorticity

    • vortex detection


Complementary measurement techniques
Complementary measurement techniques

Measurement error:

Measurement error : PIV: 1.5%

HW: 2%

Statistical error:

Averaged quantities: PIV: 1.5% (95% c.l.)

HW: 1% (95% c.l.)

Fluctuating quantities: PIV: 8% (95% c.l.)

HW: 6% (95% c.l.)

  • PIV:

    • space resolution

    • 2 components

  • Hot-wire:

    • time resolution

    • 3 components

High wind potential areas detection by PIV Fine characterization of the wind profile with triple hot-wire


Flow around the mountain
Flow around the mountain

  • Velocity field:

    • speed-up at the top of the mountain

  • Turbulence intensity:

    • inlet perturbation

    • influence at mountain’s top

  • Velocity vector field:

    • perturbation of the inlet velocity profile

    • speed-up at mountain’s top


Flow around the mountain1
Flow around the mountain

  • Fractional Speed-up Ratio at 90m (FSR):

Reference velocity

Max speed up

Recovery from the influence of the ridge

Speed-down due to the mountain


Flow around the mountain2
Flow around the mountain

  • Comparison 2D CFD simulation:

From: D. Munoz-Esparza et al. EWEA 2011, PO. 218

50% speed-up at the mountain’s top High influence of the front ridge


Conclusions and future investigations
Conclusions and future investigations

  • Inflow conditions modeling:

    • the wind tunnel can model different ABL, it is more challenging at very high scaling factors.

    • characterization of all the inlet conditions possible to model in the wind tunnel.

  • Choice of the modeling area:

    • a ridge of 1/3 of the main mountain height and situated 4km upstream influences a lot the FSR.

    • parametric study with simplified geometries


Conclusions and future investigations1
Conclusions and future investigations

  • Measurement techniques:

    • combination of PIV and triple hot-wire very powerful.

    • implementation of Stereoscopic PIV (2D-3C) on Bolund

  • Remaining questions:

    • model roughness implementation  Alaiz

    • comparison with field data  Alaiz

    • Reynolds number dependency

  • Quantification of the influence of each parameter


Thank you for your attention
Thank you for your attention

References:

[1]. Cermak, J.E. “Laboratory simulation of the Atmospheric Boundary Layer” AIAA Journal vol. 9 num. 9 pp1746-1754 (1971).

[2]. Sanz Rodrigo, J., Van Beeck, J. and Dezsö Weidinger, G. “Wind tunnel simulation of 1the wind conditions inside bidimensional forest clear-cuts. Application to wind turbine siting” J. Wind Eng. Ind. Aerodyn. 95(7)609-634, 2007.

[3]. Siddiqui, K., Hangan, H., Rasouli, A. “PIV technique implementation for wind mapping in complex topographies” Meas. Sci. Technol. 19 (2008) 065403 doi:10.1088/0957-0233/19/6/065403.

[4]. ESDU Engineering Science Data Unit. Characteristics of atmospheric turbulence near the ground. 1985.

[5]. VDI-guidelines 3783/12, 2000. Physical modelling of flow and dispersion processes in the atmospheric boundary layer – application of wind tunnels. Beuth Verlag, Berlin

[6]. Raffel, M., Willert, C., Wereley, S. “Particle Image Velocimetry: a practical guide” Springer Verlag, 2007

[7]. Bruun, H.H., ”Hot-wire anemometry” Oxford University Press Oxford (2002) ISBN: 0198563426


The waudit project1
The WAUDIT project

  • Wind Resource Assessment Audit and Standardization

    EU program: Marie-Curie Initial Training Networks Action

    FP7-PEOPLE-ITN-2008

    • Scientific/Technical:

      Advance the state-of-the-art on wind assessment

    • Academic:

      Provide a multidisciplinary education around wind energy with specialization on wind resource assessment (18 PhDs)


Back up
Back-up

  • Modeling requirements (N-S equations):


Back up1
Back-up

  • Pressure gradient

  • Fully developed flow

    • Two profiles at X and X+2m



Back up3
Back-up

Calibration with a 4th polynomial:

Velocity components in the frame of the wires:

Velocity in wind tunnel frame:


Modelling boundary conditions
Modelling boundary conditions

Velocity in wind tunnel frame:





Back up7
Back-up

Measurement error:

Measurement error : PIV: 1.5%

HW: 2%

Statistical error:

Averaged quantities: PIV: 1.5% (95% c.l.)

HW: 1% (95% c.l.)

Fluctuating quantities: PIV: 8% (95% c.l.)

HW: 6% (95% c.l.)

  • PIV:

    • space resolution

    • 2 components

  • Hot-wire:

    • time resolution

    • 3 components

High wind potential areas detection by PIV Fine characterization of the wind profile with triple hot-wire


Back up8
Back-up

Assessment of wind potential in complex terrain with high accuracy

  • field measurements: reality, but long, expensive and low resolution

  • linear models: limited to slopes < 30%

  • numerical simulation: (main area of research) high resolution, controlled boundary conditions, modeling all scales but also high level of modeling: need for precise validation

    Wind tunnel modeling:

  • constant conditions

  • space and temporal resolution

  • moderated level of modeling

  • Example on a mountainous terrain