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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
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