FIELD EXPERIMENT MUST Short Term Scientific Mission, COST 732 Efthimiou George 1, Silvia Trini...

FIELD EXPERIMENT MUSTShort 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, Greece2Institute of Atmospheric Sciences and Climate National Research Council, Torino, Italy3SOREQ NRC, Yavne, Israel

Thessaloniki 13-14 May 2008

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 campaign’s data in order to prepare a specific data set to further validate CFD and non-CFD codes for the field experiment conditions.

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.

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.

Meteorological Measurements & Tracer Detection

Concentration MeasurementsUltraviolet Ion Collectors (UVIC).Digital Photoionization Detection (digiPID).

Meteorological MeasurementsDugway Proving Ground (DPG) data.Defense Science and Technology Laboratory

(DSTL) data.

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

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)

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.

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.

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. Thessaloniki 13-14 May 2008

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 Η6 1.15mThessaloniki 13-14 May 2008

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.

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).

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 18:30 and ending at 18:45 on 25 of September 2001.

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.

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.

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.

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.

Mean value -40.55o

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.

Velocities – Temperatures

100-900 seconds (18:30:40 – 18:44:00), values averaged over 800 s

300-500 seconds (18:34:00 – 18:37:20), values averaged over 200 s

30 minutes period (18:15:00 – 18:45:00), time series of data averaged over 1 minute

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).

2( )sigma u u 2( )sigma v v 2( )sigma w w

Turbulence kinetic energy:

Mean temperature: (K)

Covariances: and .

Temperature flux: in ms-1K.

Friction velocity:

2 2 21( )

2k u v w

u w v w

1/ 22 2

*u u w v w

Local free convection velocity scale: where g=9.8 m s-2 and z is the height (m) of the anemometer above the ground surface.

Monin-Obukhov length: where κ = 0.4 von Karman’s constant.

Sensible heat flux: (W m-2) where ρ=1.2 kg m-3 is density of air, and Cpa=1005 J kg-1K-1 is specific heat capacity of dry air at constant pressure.

w w TT

paH C w T

Processing of Velocity and Concentration Text Files

Meteorological variables (200s)

Meteorological variables (15 min)

Meteorological plots

Velocity U, V, W

Direction

Temperature

Turbulence Kinetic Energy

Inflow wind

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:

tke tkeN

2 20.5tke u v

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:

tke tkeN

2 2 20.42w u v

2 2 2 2 22 0.5 0.5 1.16tke u v w u v

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

u u uN

v v vN

var 0.5*(var var )tke u v

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:

var2 0.5*(1.16*(var var ))tke u v

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

tke 2tke vartke var2tke

2 2 20.42w u v

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:

2 2 21( )

2tke u v w

South Tower MUST data

4m 0.983888m 0.9736416m 0.92248

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 = sqrt{{Ubar*Ubar}+{Vbar*Vbar}} ; /* 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 = mean{dU * dU} ; V2 = mean{dV * dV} ; W2 = mean{dW * dW} ; T2 = mean{dT * dT} ; A2 = mean{dA * dA} ;

/* compute standard deviations*/ \ U1 = sqrt{ U2 } ; V1 = sqrt{ V2 } ; W1 = sqrt{ W2 } ; T1 = sqrt{ T2 } ; A1 = sqrt{ A2 } ;

TKE = {0.5}*{sqrt{ U2+{V2+W2} }} ; /* turbulent kinetic energy */ \

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 andu v

Concentrations

100-900 seconds (18:30:40 – 18:44:00) , values averaged over 800 s

300-500 seconds (18:34:00 – 18:37:20) , values averaged over 200 s

17 minutes period (18:29:00 – 18:46:00), time series of data averaged over 1 minute

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:

2( )sigma c c

( ) /i sigma c C

Concentrations (200s)

Concentrations (17 min)

Concentration plots

Mean concentration

Inflow wind

Position of the source

Inflow wind

Position of the source

Inflow windPosition of the source

Inflow wind

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

FIELD EXPERIMENT MUST Short Term Scientific Mission, COST 732 Efthimiou George 1, Silvia Trini...

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