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FIELD EXPERIMENT MUST Short Term Scientific
Mission, COST 732
Efthimiou George1, Silvia Trini Castelli2, Tamir
Reisin3 31 March - 5 April 2008, Torino,
Italy 1Department of Engineering and Management
of Energy Resources, University of West
Macedonia, Kozani, Greece 2Institute of
Atmospheric Sciences and Climate National
Research Council, Torino, Italy 3SOREQ NRC,
Yavne, Israel
Thessaloniki 13-14 May 2008
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Purpose of this STSM

• Mock Urban Setting Test (MUST) is one of the most
  successful field experiment containing a rich and
  comprehensive dataset. Largely used by the
  scientific community, it includes detailed
  information about tracer concentrations and
  turbulence.
• COST 732 WGs used mainly Wind Tunnel data
  (Bezpalcova, 2005).
• The purpose of this STSM was to process the field
  campaigns data in order to prepare a specific
  data set to further validate CFD and non-CFD
  codes for the field experiment conditions.

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Description of the work carried out during the
visit

• General description of the MUST field experiment
  (buildings, equipment).
• Examination of existing meteorological and
  concentration data sets.
• Development of software to handle data.
• Processing of Velocity and Concentration Time
  Series Statistics.

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General description of the MUST field experiment
(buildings, equipment)

• The geometry and the coordinates of the Wind
  Tunnel experiment is supposed to be the same as
  used in the Field experiment 0 degree case.
  Accordingly
• The Shipping Containers.
• The VIP van for the collection of wind and
  concentration data.
• The 32-m tower near the centre of the container
  array.
• The 6-m towers in each of the four quadrants.
• The measurements of concentrations in the four
  sampling lines.

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Meteorological Measurements Tracer Detection

• Concentration Measurements
• Ultraviolet Ion Collectors (UVIC).
• Digital Photoionization Detection (digiPID).
• Meteorological Measurements
• Dugway Proving Ground (DPG) data.
• Defense Science and Technology Laboratory (DSTL)
  data.

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Ultraviolet Ion Collectors (UVIC)

• These files include time series of concentration
  in ppm with time interval 0.01s.
• There are 24 UVICS mounted
• on the four 6-m towers
• A, B, C, D.
• On each of these 6-m towers,
• 6 UVICs were deployed at the
• following levels
• 1, 2, 3, 4, 5 and 5.9 m.

Yee, E. and Biltoft, 2004
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Ultraviolet Ion Collectors (UVIC)

• There are 2 other files
• Tip.dat (2 m above the
• ground on the 32 m
• tower)
• Wake.dat (2 m above the
• ground, 1m behind the
• center of the building H4)

Yee, E. and Biltoft, 2004
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Digital Photoionization Detection (digiPID)

• These files include time series of concentration
  in ppm with time interval 0.02s.
• There are 48 ascii files which correspond to
  horizontal and vertical profiles of
  concentration.
• Horizontal profiles of concentration fluctuations
  were measured using 40 dPIDs which were arrayed
  along the four horizontal sampling lines that
  were parallel to and centred in the street
  canyons.
• The concentration detectors along the four
  horizontal sampling lines were placed at a height
  of 1.6 m.

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Digital Photoionization Detection (digiPID)

• Vertical profiles of concentration statistics
  were characterized by 8 dPIDs deployed on the
  32-m lattice tower near the centre of the
  obstacle array at heights of 1m, 2m, 4m, 6m, 8m,
  10m, 12m, and 16m.

Yee, E. and Biltoft, 2004
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DPG wind data

• These files include time series of velocities and
  temperature with time interval 0.1s.
• Measurements of the vertical profiles of the mean
  horizontal wind velocity and temperature in the
  upwind flow obtained from a 16-m telescoping
  pneumatic mast.
• Similar 2-D sonic anemometer/thermometers were
  mounted at the 4-, 8- and 16-m levels of a 16-m
  pneumatic mast downwind of the back of the
  obstacle array.
• Vertical profiles of mean wind speed and
  temperature were obtained from the 32-m lattice
  tower located near the center of the obstacle
  array.

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DPG wind data

• Also there are 4 more positions with measurements
  inside the domain.
• V2 In front of the
• building G5 1.15m
• V4 Between the buildings
• G6 and G7 1.15m
• V3 Between the buildings
• G6 and H6 1.15m
• V1 2.5m Northwest of the
• building ?6 1.15m

Yee, E. and Biltoft, 2004
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DSTL wind data

• These files include time series of velocities and
  temperature with time interval 0.05s.
• There are 8 ascii files which correspond to
  velocities and temperatures at heights 2 and 6 m.
• These data belongs to the four towers A, B, C, D.

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Examination of existing meteorological and
concentration data sets

• There are two main sets of data acquired during
  the trials, namely
• The dispersion data which were obtained using 74
  high-speed photoionization detectors (48 DPIDs
  and 26 UVICs).
• The meteorological dataset (i.e. the wind
  velocity and sonic temperature), which was
  obtained using 22 sonic anemometers (14 DPG and 8
  DSTL).

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Examination of existing meteorological and
concentration data sets

• We selected a first sub-set of data, collected
  during two days (25 and 26 September 2001) and
  corresponding to a neutrally stratified
  atmospheric surface layer (ASL) according to
  Monin Obukhov Length.
• We chose the experiment that corresponds to the
  release starting at 1830 and ending at 1845 on
  25 of September 2001.

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Development of software to handle data

• A tailored Fortran code was written as a flexible
  tool that allows reading the time series of
  velocities, concentration and temperature, thus
  calculating mean values and variances for any
  averaging time, chosen by the user.
• The output files, as time series and averaged
  fields can be used by the COST WGs, CFD and
  non-CFD, for numerical model simulation.

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Processing of Velocity and Concentration Time
series - Statistics

• The concentration time series were acquired over
  sampling times of 15 minutes for most of the
  continuous release experiments.
• The MUST dataset authors made the following
  processing of the data
• Because background meteorological conditions may
  change over the 15-minute sampling time duration,
  it was necessary to apply conditional sampling to
  the concentration time series.

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Processing of Velocity and Concentration Time
series - Statistics

• For this reason they extracted 3- to 5- minute
  period from each record of 15-minute duration
  with a minimal variation of mean wind direction.
• Finally they used this 3- to 5- minute period as
  the standard sampling period for computation of
  the plume concentration statistics.

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Processing of Velocity and Concentration Time
series - Statistics

• According to the above, two periods from trial 25
  September 2001 were chosen for analysis.
• These two periods (100-900 seconds and 300-500
  seconds) were the same both for velocities and
  concentrations and primarily based on the
  stationarity (i.e., speed and direction) of the
  wind over the period.

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Processing of Velocity and Concentration Time
series - Statistics
Mean value -40.55o
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Processing of Velocity and Concentration Time
series - Statistics

• We performed the same analysis on the original
  data as carried out by the MUST data referees,
  checked and compared our results with their
  averaged data.
• For velocities and temperatures we chose also to
  analyze a 30 minutes period and we calculated the
  statistics producing a time series of data
  averaged over one minute. For concentrations we
  performed an analogous analysis but for a period
  of 17 minutes.

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Processing of Velocity and Concentration Time
series - Statistics
Velocities Temperatures 100-900 seconds
(183040 184400), values averaged over 800
s 300-500 seconds (183400 183720), values
averaged over 200 s 30 minutes period (181500
184500), time series of data averaged over 1
minute
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Processing of Velocity and Concentration Time
series - Statistics

• Velocities Temperatures
• For each data record from each sonic anemometer,
  we computed the following quantities
• Mean velocity in each direction , , and
  (m s-1). Note that W is not available for the
  two-axis sonic anemometers mounted on the
  pneumatic masts just upstream and downstream of
  the MUST array.
• Mean direction
• Velocity standard deviations of the velocity
  fluctuations in the x, y, z directions
  , , and
  
• (m s-1).

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Processing of Velocity and Concentration Time
series - Statistics

• Velocities Temperatures
• Turbulence kinetic energy
• Mean temperature (K)
• Covariances and .
• Temperature flux in ms-1K.
• Friction velocity

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Processing of Velocity and Concentration Time
series - Statistics

• Velocities Temperatures
• Local free convection velocity scale
  where g9.8 m s-2 and z is the
  height (m) of the anemometer above the ground
  surface.
• Monin-Obukhov length where ? 0.4 von
  Karmans constant.
• Sensible heat flux (W
  m-2) where ?1.2 kg m-3 is density of air, and
  Cpa1005 J kg-1K-1 is specific heat capacity of
  dry air at constant pressure.

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Processing of Velocity and Concentration Text
Files
Meteorological variables (200s)
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Processing of Velocity and Concentration Text
Files
Meteorological variables (15 min)
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Meteorological plots

• Velocity U, V, W
• Direction
• Temperature
• Turbulence Kinetic Energy

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0
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Calculation of turbulence kinetic energy in
upwind mast (consistency check)

• Because the upwind mast consists only from 2-D
  sonic anemometer-thermometer we did not have the
  3rd component of velocity (w) and we calculate
  Turbulent Kinetic Energy in four ways
• We calculate first the time series of the
  variances of the velocities fluctuations ,
  . Then we calculate the time series of
  turbulent kinetic energy according to the
  relation
• and finally

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Calculation of turbulence kinetic energy in
upwind mast

• Like in the first way but this time we account
  also for the prime of velocity w?w? according to
  the relation
• (Yee and Biltoft, 2004).
• The time series of turbulent kinetic energy
  becomes

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Calculation of turbulence kinetic energy in
upwind mast

• In the following process we calculate the mean
  values of the standard deviations of wind
  velocity fluctuations and denoted as varu,
  varv where

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Calculation of turbulence kinetic energy in
upwind mast

• Like in the third way but at this time we account
  also the mean value of the prime w?w? according
  to the relation
• (Yee and Biltoft, 2004) and the mean value
  of turbulent kinetic energy becomes

South Tower Numerical Simulation South Tower Numerical Simulation South Tower Numerical Simulation South Tower Numerical Simulation South Tower Numerical Simulation

4m 2.245630 1.935884 2.245626 1.935884
8m 2.199033 1.895721 2.199036 1.895720
16m 1.974085 1.701799 1.974081 1.701794
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Calculation of turbulence kinetic energy -
mistakes

• From the MUST data we noticed that turbulent
  kinetic energy in the statistics file is
  erroneously calculated as follows

South Tower MUST data South Tower MUST data

4m 0.98388
8m 0.97364
16m 0.92248
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Calculation of turbulence kinetic energy -
mistakes
The part of the script DPGSONIC.MAT which refers
to TKE   Ubar mean U / mean x
component/ \ Vbar mean V / mean y
component/ \ Wbar mean W / mean z
component/ \ Tbar mean T / mean
temperature/ \ Abar RADTOD atan Ubar
Vbar / mean bearing (deg)/ \ Sbar
sqrtUbarUbarVbarVbar / mean wind
speed/ \ / compute deviations from mean/ \
dU U - Ubar dV V - Vbar dW
W - Wbar dT T - Tbar dA A
- Abar / compute variances/ \ U2
meandU dU V2 meandV dV W2
meandW dW T2 meandT dT
A2 meandA dA
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Calculation of turbulence kinetic energy -
mistakes
/ compute standard deviations/ \ U1
sqrt U2 V1 sqrt V2 W1
sqrt W2 T1 sqrt T2 A1
sqrt A2 TKE 0.5sqrt
U2V2W2 / turbulent kinetic energy / \
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Calculation of mean direction - mistakes

• In the file explaining how to calculate the
  direction they suggest first to calculate the
  instantaneous direction and then to average these
  time series to obtain the mean value.
• The procedure described does not output the
  values as in the file of statistics M2681829.
• We apply the correct way to average the wind
  direction for every averaging period, we
  calculated the mean values of wind components and
  after calculate the corresponding wind direction
  on the averaged and

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Processing of Velocity and Concentration Time
series - Statistics
Concentrations 100-900 seconds (183040
184400) , values averaged over 800 s 300-500
seconds (183400 183720) , values averaged
over 200 s 17 minutes period (182900
184600), time series of data averaged over 1
minute
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Processing of Velocity and Concentration Time
series - Statistics

• Concentrations
• After the conditional sampling of concentration,
  we computed the following concentration
  statistics
• Mean concentration (ppm).
• Concentration standard deviation of the
  concentration fluctuation
• Concentration fluctuation intensity

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Processing of Velocity and Concentration Text
Files
Concentrations (200s)
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Processing of Velocity and Concentration Text
Files
Concentrations (17 min)
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Concentration plots

• Mean concentration

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Inflow wind
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Inflow wind
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Inflow wind
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Further discussion

• Except from the known measurements points in COST
  there are others for which we have the data but
  we do not know their exact positions inside the
  domain.

Milliez and Carissimo, 2008