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FIELD EXPERIMENT MUST Short Term Scientific Mission, COST 732 Efthimiou George 1, Silvia Trini Castelli 2, Tamir Reisin 3 31 March - 5 April 2008, Torino,

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Presentation on theme: "FIELD EXPERIMENT MUST Short Term Scientific Mission, COST 732 Efthimiou George 1, Silvia Trini Castelli 2, Tamir Reisin 3 31 March - 5 April 2008, Torino,"— Presentation transcript:

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

2 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. Thessaloniki 13-14 May 2008

3 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. Thessaloniki 13-14 May 2008

4 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. Thessaloniki 13-14 May 2008

5 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. Thessaloniki 13-14 May 2008

6 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. Thessaloniki 13-14 May 2008 Yee, E. and Biltoft, 2004

7 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) Thessaloniki 13-14 May 2008 Yee, E. and Biltoft, 2004

8 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. Thessaloniki 13-14 May 2008

9 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. Thessaloniki 13-14 May 2008 Yee, E. and Biltoft, 2004

10 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

11 DPG wind data  Also there are 4 more positions with measurements inside the domain.  V2In front of the building G5 1.15m  V4Between the buildings G6 and G7 1.15m  V3Between the buildings G6 and H6 1.15m  V12.5m Northwest of the building Η6 1.15m Thessaloniki 13-14 May 2008 Yee, E. and Biltoft, 2004

12 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. Thessaloniki 13-14 May 2008

13 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). Thessaloniki 13-14 May 2008

14 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. Thessaloniki 13-14 May 2008

15 Development of software to handle data Thessaloniki 13-14 May 2008  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.

16 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008  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.

17 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008  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.

18 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008  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.

19 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 Mean value -40.55 o

20 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008  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.

21 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 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

22 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 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 ).

23 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 Velocities – Temperatures  Turbulence kinetic energy:  Mean temperature: (K)  Covariances: and.  Temperature flux: in ms -1 K.  Friction velocity:

24 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 Velocities – Temperatures  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 C pa =1005 J kg -1 K -1 is specific heat capacity of dry air at constant pressure.

25 Processing of Velocity and Concentration Text Files Thessaloniki 13-14 May 2008 Meteorological variables (200s)

26 Processing of Velocity and Concentration Text Files Thessaloniki 13-14 May 2008 Meteorological variables (15 min)

27 Meteorological plots Thessaloniki 13-14 May 2008  Velocity U, V, W  Direction  Temperature  Turbulence Kinetic Energy

28 Inflow wind 0 Thessaloniki 13-14 May 2008

29 Inflow wind 0 Thessaloniki 13-14 May 2008 ? 0

30 Inflow wind 0 Thessaloniki 13-14 May 2008

31 Inflow wind 0 Thessaloniki 13-14 May 2008

32 Inflow wind 0 Thessaloniki 13-14 May 2008

33 Inflow wind 0 Thessaloniki 13-14 May 2008

34 Inflow wind Thessaloniki 13-14 May 2008

35 Inflow wind 0 Thessaloniki 13-14 May 2008

36 Inflow wind 0 Thessaloniki 13-14 May 2008

37 Inflow wind 0 Thessaloniki 13-14 May 2008

38 Calculation of turbulence kinetic energy in upwind mast (consistency check) Thessaloniki 13-14 May 2008  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:

39 Calculation of turbulence kinetic energy in upwind mast Thessaloniki 13-14 May 2008  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:

40 Calculation of turbulence kinetic energy in upwind mast Thessaloniki 13-14 May 2008  In the following process we calculate the mean values of the standard deviations of wind velocity fluctuations and denoted as varu, varv where

41 Calculation of turbulence kinetic energy in upwind mast Thessaloniki 13-14 May 2008  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 4m2.2456301.9358842.2456261.935884 8m2.1990331.8957212.1990361.895720 16m1.9740851.7017991.9740811.701794

42 Calculation of turbulence kinetic energy - mistakes Thessaloniki 13-14 May 2008  From the MUST data we noticed that turbulent kinetic energy in the statistics file is erroneously calculated as follows: South Tower MUST data 4m0.98388 8m0.97364 16m0.92248

43 Calculation of turbulence kinetic energy - mistakes Thessaloniki 13-14 May 2008 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} ;

44 Calculation of turbulence kinetic energy - mistakes Thessaloniki 13-14 May 2008 /* 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*/ \

45 Calculation of mean direction - mistakes Thessaloniki 13-14 May 2008  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

46 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 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

47 Processing of Velocity and Concentration Time series - Statistics Thessaloniki 13-14 May 2008 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:

48 Processing of Velocity and Concentration Text Files Thessaloniki 13-14 May 2008 Concentrations (200s)

49 Processing of Velocity and Concentration Text Files Thessaloniki 13-14 May 2008 Concentrations (17 min)

50 Concentration plots Thessaloniki 13-14 May 2008  Mean concentration

51 Thessaloniki 13-14 May 2008 Inflow wind Position of the source

52 Thessaloniki 13-14 May 2008 Inflow wind Position of the source

53 Thessaloniki 13-14 May 2008 Inflow wind Position of the source

54 Inflow wind Thessaloniki 13-14 May 2008

55 Inflow wind Thessaloniki 13-14 May 2008

56 Further discussion Thessaloniki 13-14 May 2008  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

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