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Istituto di Scienze dell’Atmosfera e del Clima C.N.R. - Torino
D. Anfossi , S. Trini Castelli, G. Belfiore
Modelli LagrangianiModelli Lagrangiani
1. Studio e applicazione delle tecnologie consolidate a scenari reali, corrispondenti a periodi recenti e confronto dei dati modellistici con le misure effettuate dall'Agenzia Regionali per l'Ambiente. (ALPNAP)
2. Studio di nuovi approcci modellistici relativi alla chiusura delle equazioni fondamentali: momenti di ordine elevato nei modelli Euleriano e sperimentazione di nuovi approcci di tipo Lagrangiano (MSS).
3. Analisi dell'influenza dell'input meteorologico dei modelli di dispersione tramite l'interfacciamento di codici meteorologici prognostici (HARMO12).
Model system MSS
MicroSprayMicroSwift
prognostic (mass consistent) wind interpolator over complex terrain accounting for complex terrain
and buildings
LPD model derived from SPRAY; it accounts for the presence of buildings, other obstacles, complex terrain, and
possible occurrence of low wind speed
any kind of source configuration, with emission in any direction and any initial velocity
M S Sallows taking into
account:
negatively, positively or neutral emissions in presence of obstacles
dispersion of dense and/or light gas, accidental releases and possible terrorist attacks in
urban areas.
from: Venetsanos et al. (2003), Journal of Hazardous Materials
Accidental release of hydrogen in Stockholm
500 meters
plume phase
normal dispersionas passive scalar
gravity spreading
gravity spreading
Equations
energy conservation
mass conservationss
a
p uEbudt
d
2
222 bwuNBbudt
dps
a
ps
vertical momentum conservationsps
a
p ubBbwudt
d 22
Y horizontal momentum conservationaspsa
p vuEvbudt
d
2
aspsa
p uuEubudt
d
2
X horizontal momenta conservation
five unknowns p, up, vp, wp, b where:
eubE 2 entrainmentz
gN a
a
2
a
aegB
Uauu se 21 entrainment velocity
222
ppps wvuu
222
aaaa wvuU where
and 1.0 6.0
Plume spread at ground
When a dense plume reaches the ground an horizontal momentum is generated by the weight of the plume
itself that tends to spread the plume
This heavy gas induced outflow velocity depends on the bulk properties of the dense plume, i.e. how
density varies in 3 D and requires all the particles position to be accounted for
Thus the movement of each particle depends on the characteristics of the ‘ensemble’
A hybrid algorithm used
To each particle is assigned an horizontal speed
bulkbulkg HgU 2
with:a
abulkbulk
bulk= ‘bulk’ density of the plume above the particle
H = ’bulk’ height of the plume above the particle
direction of the spread
cosggs UU
singgs UV where is randomly picked from a uniform [0°-360°] distribution
is chosen at emission time and kept by the particle
How to compute Hbulk and bulk ?
1) when a particles ‘reaches’ the ground 2) all the particles pi belonging to a dx dy column are accounted for
np
ipbulk i
znp
H1
1
np
ipbulk inp 1
1
Trial 8 Instantaneous Release – Downrange
Courtesy of Dr. Jim McQuaid
Trial 8 Instantaneous Release – Overhead
Courtesy of Dr. Jim McQuaid
SPRAY
HARMO12-2008
Chlorine accident - Macdona, TX, USA
June 28, 2004 two trains collision
Picture from Railroad Accident Report NTSB/RAR-06/03
Left graph refers to the Cl2 concentration versus distance; right graph plots Cl2 cloud width and height, both to the model-simulated concentration of 20 ppm versus
distance
MSS results compared to the Macdona accident simulations from six widely-used models (Hanna, 2007). Continuous lines indicate present results, vertical bars show
the variability (max, min) of the six models, circles locate their median
50 m
23.3 m26.2 m
47 m
MERCURE - MSS
Building
•Xo = 50 m from release
•H = 47 m, Lx =23.3 m, Ly = 26.2 minitial momentum directed vertically, w = 1.14 m/s
emission height = 10 m
initial density ratio (plume/air) = 2.0
initial emission diameter = 2.17 m
gas emission rate = 10 kgs-1
neutral stratification, logarithmic wind profile
low wind at 10 m = 1.5 m/s
higher wind at 10 m = 5 m/s 2 flow regimes
MSS
MERCURE Wind(z=10m) = 1.5 m/s
Iso-surface 0.01 kg/kg
MERCURE
MSS
Wind(z=10m) = 5.0 m/s
Iso-surface 0.01 kg/kg
MERCURE
MSS
Wind(z=10m) = 5.0 m/s
Vertical cut
1.E+02
1.E+03
1.E+04
1.E+05
1.E+06
1.E+02 1.E+03 1.E+04 1.E+05 1.E+06
Observed Concentration (ppm)
Pre
dic
ted
Co
nc
en
tra
tio
n (
pp
m)
ti6ti7ti8ti9ti12ti13ti18ti19tc45tc47bu2abu2ibu3abu3ibu4abu4ibu5abu5ibu6abu6ibu7abu7ibu8abu8ibu9abu9ico3aco3ico5aco5ico6aco6i
MicroSpray dense-gas model evaluation
(TIsland-Ist + TIsland-Cont + Burro+Coyote)
SCIPUFF dense-gas model evaluation
Paris 16th October 2007
HARMO12-2008
Plot plan of the Kit Fox site
consists of 52 trials
with CO2 gas releases, puffs & continuous plumes
samplers (one reading per second) were installed at four arcs:
25, 50, 100, and 225 m
8 masts with wind speed measurements
HARMO12-2008
Validation of MSS against Kit Fox field data
Statistical evaluation of comparisons between observations and predicted data includes: geometric mean bias (MG),
geometric variance (VG)
factor of 2 (FA2)
Kit Fox experiment
Overall URA Continuous
URAPuff
ERP Continuous
ERPPuff
52 experiments
12 experiments
21 experiments
6 experiments
13 experiments
MG 1.04 1.42 0.95 1.19 0.87
VG 1.20 1.20 1.15 1.25 1.29
FA2 88 % 92 % 99 % 83% 83 %
CONCLUSIONS
Preliminary results suggest that MicroSpray
performs correct and fast simulations of dense gas dispersion in real field situations.
Computation timeType of model
Some hoursCFD model
Few minutesMicroSpray
RAMS-MIRS configuration
Example of a typical configuration for a simulation of the meteo fields using the prognostic code RAMS up to 1 km resolution, 4 nested domains
grid 1: 64 km horizontal resolutiongrid 2: 16 km horizontal resolutiongrid 3: 4 km horizontal resolution grid 4: 1 km horizontal resolution
Vertical grid: vertical stretched layers, 0 –15/20000 m, first layer 50 m depth (first level at ~25 m)
RAMS is initialised with the ECMWF (0.5o lat/lon) analysis fields.
Nudging at the lateral boundaries of the outer grid every 6 hours.
Mesoscale
Regional to local scale
Downscaling from RMS to MINERVE
mass consistent model
Simulation of the meteo fields using the diagnostic code MINERVE up to ~ 100 m resolution, in subdomainstypically 10-20 km x 10-20 km sizeMINERVE gets as input the hourly RAMS 3D gridded dynamical and thermal fields and…
- interpolates the mean input fields on its 3D computational domain-performs and objective analysis: application of mass conservation in every domain cell
Regional scale
Local scale
Advantages of RAMSMINERVE downscaling:
-possibility of including local measurements-possibility of including more detailed topograhy data
An example of how RAMS_MIRS + MINERVE
works for wind field in complex terrain
from ALPNAP Alpine Space Project
RAMS MINERVE
For its nature, MINERVE is not designed to account for the prognostic turbulence fields, and the Lagrangian turbulent variables are thus calculated in SPRAY from parameterisations defined for flat terrain (ex. Hanna, 1982).
In this work we investigate whether a proper interpolation from the coarser-resolution prognostic 3D-gridded turbulence fields, like diffusion coefficients, turbulent kinetic energy and its dissipation, might be used in complex and inhomogeneous terrain.
In this way, the shortcoming of using parameterised turbulent fields might be overcome by coupling MINERVE with a module, which calculates the turbulence fields on the high-resolution diagnostic grid by interpolating from the coarser prognostic grid.
What is this work about
What we compare here
RAMS is run with four nested grids, where the third (G3) and the fourth (G4) grids have respectively 1 km and 250 m resolution.
RAMS fields on G4 at 250 m are considered the ‘truth’ versus which to test other two combinations.
The G3 turbulence fields from the 1-km grid are bilinearly interpolated on the 250-m mesh points, originating the turbulence dataset G3_INTP to be checked as an alternative to flat-terrain parameterisations.
A downscaling of the mean flow to 250 m with MINERVE, using in input the 1-km resolution grid RAMS G3 fields, is done. MINERVE wind fields at 250 m are then used to calculate the surface layer and boundary layer parameters entering the turbulence calculation in the standard configuration, that is applying the Hanna (1982) parameterisation
We consider three different turbulence closure schemes in RAMS……
The MY 2.5 scheme (as in RAMS)
Vertical diffusion coefficients from the TKE equation in boundary layer approximation:
with
502
20
.xhminhorizm S)xC(,KmaxK
Horizontal diffusion coefficients from the deformation scheme as in El-anis…
340750 /Ahmin xK.K with
εPz
EK
zdt
dEE
21
EE E)(lSK 2
21
mm E)(lSK 2 1
2
32E
l
kz+
kzl
1
dzE
dzEzal
are functions depending on the set of empirical constants = (0.92, 16.6, 0.74,
10.1, 0.08) and on the shear and buoyancy terms (ref. to Mellor and Yamada (1974,1982)).
Em S,S C,B,A,B,A 2211
Closure length scales: lB,A,B,A,l,,l 22112211
The turbulence closures used in RAMS_MIRS
The EL_(iso)anis scheme
Vertical diffusion coefficients from the 3D TKE (E) equation:
εPjx
EK
jxdt
dEE
lEcK /m
21
d
/
l
Ec 23
c c E empirical coefficients
mEE KK with
502
20
.xhminhorizm S)xC(,KmaxK
Horizontal diffusion coefficients from a deformation scheme
340750 /Ahmin xK.K with
ρ0 air density, Cx dimensionless coefficient, Δx grid spacing
S2 horizontal strain rate, KA user-specified coefficient of order 1.
lld
l
kz+
kz
1
dzE
dzEzal
The turbulence closures used in RAMS_MIRS
The case considered
Susa
Point at 970 m
Grid 4
Grid 3
Torino
North-West Italian Alpine region around Torino
Altitudes……
G4 970 m
G3, 4 points:
NW 772 mNE 598 m SE 780 mSW 939 m
Dashed blue: values interpolated from Grid 3Solid orange: values calculated on Grid 4
Distributions of TKE for G3_INTP and G4 values (h < 1450 m)
MY2.5 EL_iso EL_anis
Red: RAMS G3_intpBlue: RAMS G4Green: (RAMS G3 mean flow ) MINERVE+Hanna
A critical case in complex terrain, TKE
MY 2.5 EL-anis15 GMT
Conclusions on the turbulence closure analisys
Interpolated values of TKE from 1 km resolution grid (G3_INTP) result to be overall representative of the TKE
values simulated on a 250 m grid (G4).
The spread between the two sets of TKE values, G3_INTP and G4 are probably mainly due to the fact
that the G3 points, on which the interpolation procedure is applied, may be characterized by even significantly
different altitudes
The methodology seems to be feasible, also in complex terrain and in critical locations and asks for further
investigation.