Observation and simulation of flow in vegetation canopies

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Observation and simulation of flow in vegetation
canopies Roger H. Shaw University of California,
Davis
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Canopy turbulence

• Kinetic energy spectral densities that are
  strongly peaked
• Strong correlations between streamwise and
  vertical velocities
• Large velocity skewness (Skugt0 Skwlt0)
• Transport dominated by organized structures
• Larger contributions from sweep motions than
  ejections

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We will understand the movement of the stars
long before we understand canopy
turbulence Galileo Galilei
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Time traces of velocity components
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Z2.4h
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Z0.9h
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Scalar ramps correlated through the depth of
the canopy show wholesale flushing of the
canopy airspace by large scale gusts.
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Scalar
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Vertical velocity
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Streamwise velocity
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Turbulent kinetic energy budget determined from
LES
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Large-eddy simulation of surface and canopy layers

• Based on NCAR code developed by Moeng (1984)
• Modified to include drag effects on both the
  resolved-scale
• flow and SGS motions
• An experimental tool and framework for
  investigation
• of observed phenomena

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Resolved- and subgrid-scales in large-eddy
simulation (LES)
2?
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LES resolved- and subgrid-scales
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• periodic horizontal boundary conditions
• frictionless lid at upper boundary (no flux)
• uniform force to drive the flow
• scalar source through depth of canopy

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• Canopy specification
• Represented at each grid point by element
• area density a (m2/m3)
• Area density horizontally uniform but a(z)
• Canopy elements rigid
• Volume occupied by solid elements is
• considered to be negligible

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Static pressure perturbation
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Resolved- subgrid- and wake-scales
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Internal energy
Mean flow KE
Resolved-scale TKE
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Subgrid-scale TKE
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2
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Viscous drag
Internal energy
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Mean flow KE
Resolved-scale TKE
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Subgrid-scale TKE
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2
Wake-scale TKE
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Form drag
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Drag parameterization
Blasius solution for flow parallel to a flat
plate
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inertial cascade
form drag
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SGS energy pool
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inertial cascade
form drag
?sgs
?w
SGS energy
wake energy
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Subgrid-scale energy equation
where
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Wake-scale energy equation
where
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• Additional variable ew to represent kinetic
  energy
• associated with wake motions
• Dissipation of ew controlled by dimension of
• canopy elements

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• Additional variable ew to represent kinetic
  energy
• associated with wake motions
• Dissipation of ew controlled by dimension of
• canopy elements
• Rate of conversion of kinetic energy from
• resolved scales to wake scales is large
• Effective diffusivity of wake-scale turbulence
• can be ignored

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• Additional variable ew to represent kinetic
  energy
• associated with wake motions
• Dissipation of ew controlled by dimension of
• canopy elements
• Rate of conversion of kinetic energy from
• resolved scales to wake scales is large
• Effective diffusivity of wake-scale turbulence
• can be ignored
• Important to include the conversion of resolved
• and SGS energy to wake-scale kinetic energy

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• Additional variable ew to represent kinetic
  energy
• associated with wake motions
• Dissipation of ew controlled by dimension of
• canopy elements
• Rate of conversion of kinetic energy from
• resolved scales to wake scales is large
• Effective diffusivity of wake-scale turbulence
• can be ignored
• Important to include the conversion of resolved
• and SGS energy to wake-scale kinetic energy
• Viscous drag and direct dissipation in viscous
• boundary layers of leaves is of little
  consequence

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Conditional sampling of LES output and composite
averaging of flow structures

1. Pressure signal at z/h1 used as detection
   function
2. Structures aligned according to peak in pressure
   signal
3. Composite averages of various elements of the
   structures

Approximately 1,600 events extracted from one
30-minute time series (but not all independent)
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270 seconds (17 frames)
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The structure of the large-eddy motion as a
solution to the eigenvalue problem
Where Fij is the spectral density tensor fi is
the eigenvector l is the associated eigenvalue
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