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FLACS SIMULATIONS OF SMEDIS

SCENARIOS

Client

FLUG members

Author:

Thor Gjesdal

Hans-Christen Salvesen

Benoit Buffet

Bergen, December 1999

Ref.nr.: CMR-99-F30063

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Copy no.: .................

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SUMMARY

In this report we describe FLACS predictions of dense gas dispersion experiments con-tributed to the SMEDIS (Scientific Model Evaluation of dense gas DISpersion mod-

els) project. The predictions agree well with experimental measurements and give

confidence in the capabilities of FLACS to predict dense gas release and dispersion

scenarios.

i

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FLACS simulations of SMEDIS scenarios 1 of 53

CONTENTS

List of Figures 3

1 Introduction 5

2 SMEDIS validation strategy and comparison parameters 7

2.1 Validation strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2 Physical comparison parameters . . . . . . . . . . . . . . . . . . . . 7

2.3 Statistical performance parameters . . . . . . . . . . . . . . . . . . . 9

3 EEC550 and EEC551 experiments 11

3.1 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Computational grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.3 Source definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4 DAT638 experiment 19


4.2 Scaling and computational grid . . . . . . . . . . . . . . . . . . . . . 19

4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

5 Thorney Island test 21 23



5.3 Gas composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

6 EMU chlorine release experiment 29


6.2 Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.3 Gas composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31




7.3 Source definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

7.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

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2 of 53 FLACS simulations of SMEDIS scenarios



8.2 Computational grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 438.3 Source definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

8.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

9 Summary and conclusions 51

References 53

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LIST OF FIGURES

3.1 EEC551. Plan of the experimental site . . . . . . . . . . . . . . . . . 12

3.2 EEC551. Grid, monitor, and obstacle layout . . . . . . . . . . . . . . 13

3.3 EEC550. Concentration contours at ground level . . . . . . . . . . . 14


3.5 EEC550 simulated vs. experimental point-wise comparison . . . . . . 16


4.1 DAT638 simulated gas cloud . . . . . . . . . . . . . . . . . . . . . . 20

4.2 DAT638 simulated vs. experimental point-wise comparison . . . . . . 21

4.3 DAT638 simulated vs. experimental arc-wise comparison . . . . . . . 22

5.1 Sensor locations for the Thorney Island experiment . . . . . . . . . . 24

5.2 TI21 interaction of the gas cloud with the fence . . . . . . . . . . . . 25

5.3 TI21 simulated vs. experimental point-wise comparison . . . . . . . . 27

5.4 TI21 arc-wise dose comparison . . . . . . . . . . . . . . . . . . . . . 28

6.1 EMU DJ. Overview of the source area . . . . . . . . . . . . . . . . . 30

6.2 EMU DJ. 2D plot of concentration in the release plane . . . . . . . . 32

6.3 EMU DJ. Simulated and experimental point-wise comparison . . . . . 33


7.2 EEC171. Grid and obstacle layout . . . . . . . . . . . . . . . . . . . 377.3 EEC170. Concentration contours close to the ground . . . . . . . . . 38

7.4 EEC171. Concentration contours close to the ground . . . . . . . . . 39

7.5 EEC170. Simulated vs. experimental point-wise comparison . . . . . 40

7.6 EEC171. Simulated vs. experimental point-wise comparison . . . . . 41


8.2 EEC561. Grid, monitor, and obstacle layout . . . . . . . . . . . . . . 45





9.1 Geometric Mean Bias vs. Geometric Mean Variance for all test cases . 52


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1. INTRODUCTION

SMEDIS (Scientific Model Evaluation of dense gas DISpersion models) is a jointEuropean project conducted under the CECs Environment and Climate Programme

1996-1999. The objective of the project is:

To develop and test a protocol for a Scientific Model Evaluation (SME) of Dense

Gas Dispersion (DGD) Models.

To use this protocol to carry out an SME of DGD models in current use.

SMEDIS partner DNV invited CMR to take part in the evaluation exercise, and CMR

has contributed FLACS documentation and predictions for selected test cases to the

project. This report documents the FLACS predictions and comparison with experi-mental measurements for the SMEDIS test cases.

Although predictions of release and dispersion of dense gas is within the capa-

bilities of FLACS, it has not been extensively used in this field. Salvesen [1] has

developed a model to calculate source definitions for flashing liquid release that can

be used as input to FLACS. Salvesen and Asheim [2] performed predictions of some

DGD scenarios.

Several of the SMEDIS test cases are concerned with flashing release of liquid

propane. These cases can therefore be used to validate both the flashing model within

its range of applicability as well as the capabilities of FLACS to predict dense gas

dispersion.The SMEDIS test cases described in this report were generated during the follow-

ing projects:

The BA-Propane field tests conducted near Lathen, Germany.

A laboratory experiment performed at the University of Hamburg.

The well-known Thorney Island field tests.

A field experiment concerning release of chlorine at an industrial facility in Aml-

wch, Wales.

The test cases were organized as three batches of data sets, and in this report we doc-

ument the cases in the order they were performed.

The results show that FLACS is quite capable of performing reliable predictions

of dense gas dispersion scenarios. While we do not have enough results to perform

a full validation and to estimate the uncertainty in the computed results, the good

performance of the FLACS predictions across the board gives us confidence in the

capabilities of FLACS as a dense gas dispersion model.


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2. SMEDIS VALIDATION STRATEGY AND COMPARISON

PARAMETERS

2.1 Validation strategy

Two strategies were employed for comparison of model predictions and observations

in SMEDIS. These are commonly referred to as:

Concentration paired in space and time (CPST) in which primary target data

(concentration) are compared to primary measurements paired in space and time

The maximum arc-wise concentration (MAC) approach in which secondary or

derived predictions are compared to manipulated experimental data, notablymaximum concentration and plume width at specified downwind distances.

In SMEDIS the CPST approach were modified by the use of time averaging to

produce best estimates of ensemble average values for comparison with the output

from the generally ensemble averaged models. Thus the data are paired in space only

(CPS) since the time element is removed by the use of time averaging.

The MAC approach seeks to compare the downwind extent of the cloud and the

variation of maximum concentration with downwind distance irrespective of the wind

direction and its fluctuations. It cannot, however, be used to assess the degree to which

a model predicts the spatial extent of the regions of high concentration within the cloud.

It is therefore less appropriate for the assessment of models, such as CFD models, that

have a capability to predict internal cloud structure.

2.2 Physical comparison parameters

For a given validation case the physical parameters to be compared depend on the type

of the release. Two types of parameter are defined for each type of release :

point-wise comparison parameters (paired data) - values at each sensor

arc-wise comparison parameters (derived data) - values at each arc

2.2.1 Concentration time series

At each sensor the concentration of released material is measured and recorded in the

form of a time series that we denote by

, where

is the position of sensor

number

The concentration measurements are recorded for a finite time, i.e. the time series

is defined for

. To calculate the time integrals of concentration, we may

restrict the time range to

.


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2.2.2 Time-averaged concentration

For each sensor location where a concentration time series is available we define the

time-averaged concentration over the period

by

2.2.3 Dose and related quantities

We define the dose for each concentration sensor by integration of the original time

series over the period

:

The time of arrival is the time at which a given fraction (

) of the dose has

occurred at that point

and, similarly, for the time of departure

2.2.4 Maximum concentration at a point

We define the maximum concentration at a point

by dividing the period

of the time series into equal segments.

is taken to be the

maximum average value over these time sub-intervals.

where

.

If the time segment that contains the maximum concentration is indexed by ,then the time of maximum concentration is defined to be in the middle of this time

segment

2.2.5 Sensor arc quantities

The concentration sensors are usually arranged in some ordered pattern over the test

site. In plan view, this ordered pattern may be arranged on a polar or Cartesian grid.

For each test, sensor arcs can be defined that are at the same downwind distance from

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the source either exactly for a polar arrangement or approximately for a Cartesian

arrangement.

For each sensor arc, the arc-wise maximum of a quantity is simply the arithmeticmaximum of the values of the quantity at the sensors in the arc.

The cloud width on a given arc and at a given time

,

, is derived from

the arc-wise concentration distribution. If is the concentration distribution

across the arc or a quantity derived from it, then the width is defined as a multiple,

of the standard deviation of

was used in the SMEDIS project. The standard deviation is given by

-

-

-

-

and the

th moment of

is

-

The choice of which physical quantity to use in the calculation of the moments

depends on the release type:

For continuous releases the time-averaged concentrations at each sensor on thearc are used to define .

For instantaneous releases the dose at each sensor on the arc is used to define

2.3 Statistical performance parameters

For cases in which few data points are involved it is often possible to evaluate a

model/data comparison by eye. In SMEDIS, however, due to the potentially large

number of data points, this comparison was assessed using statistical performance

measures (SPMs) as quantitative tools.The SMEDIS Project has considered a large number of possible measures to com-

pare the performance of predictive models against experimental data [3].

The main objective of SMEDIS is not to rank models and no attempt has therefore

been made to combine measures to produce a single quantitative performance param-

eter. The project has performed a review of a wide range of the available performance

measures for model assessment in order to investigate model performance for complex

situations and to test out a variety of validation approaches.

The following criteria were used in SMEDIS to evaluate the different performance

measures:


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Measures must be applicable at both low and high concentration levels to be

able to handle on-axis data and LFL concentrations and plume edges and toxic

concentrations. Thus they must weigh all pairs of observations and predictionsequally, independent of absolute concentration.

Measures must be capable of distinguishing between model performance, and

indicate if a model, in general, over- or under-predicts. They must also indi-

cate the level of scatter or random deviation from this average under- or over-

prediction.

If spatially-paired data are to be used, is that they must be capable of accepting

zero predicted or measured concentrations.

Based on the above criteria, a number of statistical performance measures were

chosen to be used in the SMEDIS evaluation. We give the definition of each of thesebelow.

We consider a set of observed and predicted quantities that we denote by

and

, respectively. To obtain a measure for the comparison of observed and pre-

dicted values, we let

denote the average over the

pairs of data and we define the

parameters

Mean Relative Bias (MRB):

Mean Relative Square Error (MRSE):

Factor of

(FA

):

Geometric Mean Bias (MG):

Geometric Mean Variance (VG):

According to SMEDIS [3], these parameters seem to perform well and to provide

consistent results. In particular the MRB, MRSE and FA2 seem to fulfill most of the

criteria.

Although MG and VG are somewhat similar to MRB and MRSE, both pairs of

measures were adopted for the project to allow comparison with previous work. MG

and VG are not applicable for zero concentrations, and to overcome this a sensor

threshold value of was applied to the data.

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3. EEC550 AND EEC551 EXPERIMENTS

Test 55 of the BA-Propane experiments consisted of a continuous jet release of liquidpropane. The jet was obstructed by a 2m high linear fence downstream of the release

point. The obstacle was removed in the middle of the experiment such that two data

sets were generated. It is thus possible to compare the dispersion with and without the

obstacle. The two data sets are denoted by EEC550 and EEC551 for the non-obstructed

and obstructed case respectively.

3.1 Experimental setup

Liquid propane was released through nozzle to form a liquid jet. The

propane was released at a rate of . The duration of the release was , the

average wind speed during the test was , and the wind direction formed

an angle with the release direction. The fence was located from the

source. Fig. 3.1 shows a plan of the experimental site with the fence and the sensor

locations.

3.2 Computational grid

To facilitate grid refinement by stretching towards the jet axis, the coordinate system

was rotated to align the jet direction with the -direction. The computational domain

covers an area of in the horizontal plane and a height of , and is

covered by a grid comprising

cells. Fig. 3.2 shows the horizontal grid,

and the layout of the obstacle and the monitor points in the source region. The vertical

grid is uniform close to the ground to resolve the height of the fence with five control

volumes. Above the height of the fence the grid is stretched towards the top of the

domain.

3.3 Source definition

Because of the sudden de-pressurization during the release, the propane will undergoa flash vaporization and turn into a two-phase aerosol jet. FLACS can only handle

gaseous releases, and we therefore used the FLASH utility program [1] to define an

equivalent source at a point where all liquid in the jet has evaporated. For the release

considered in the EEC55 test, the virtual source is located downstream of the

release point and is characterized by


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0

20

40

60

80

100

-20 0 20 40 60 800

20

40

60

80

100

-20 0 20 40 60 800

20

40

60

80

100

-20 0 20 40 60 80

Figure 3.1: Plan of the release point and direction, fence, and sensor locations for the

EEC551 test. The release was located in the origin, the two other arrows indicate the

wind direction.

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X (m)

-0 10 20 30 40 50 60 70 80

Y (m)

-30

-20

-10

0

10

20

30

40

Job=491003.

Time= 160.010 (s). IJ plane, K=1

M1

M2

M3

M4M5

M6

M7

M8

M9

M10

M11

M12

M13

M14

M15

M16

M17

M18

M19

M20

M21

M31 M32 M33 M34M43 M48

X (m)

-0 10 20 30 40 50 60 70 80

Y (m)

-30

-20

-10

0

10

20

30

40

Job=491003.


Figure 3.2: Grid, monitor, and obstacle layout for the simulation of the EEC550 and

EEC551 tests.


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X (m)

-0 10 20 30 40 50 60 70 80

Y (m)

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

40

45

Job=490903. Var.=LOG10(FUE L (-)).


Figure 3.3: Field plot of propane mass fraction at ground level for the simulation of the

EEC550 test. The scale is logarithmic and the range is from

to

by volume

Air has been entrained into the jet during the evaporation, and the propane in the jet

has therefore been diluted. See [1] for a description of the modeling of the evaporation

region.

3.4 Results

We show contour plots of the steady-state concentration field at ground level for thenon-obstructed and obstructed case in Fig. 3.3 and Fig. 3.4, respectively. We see that

there is a significant deflection of the jet by the fence. As a consequence is the jet

downstream of the fence much wider than in the un-obstructed case.

Time histories of the mass fraction of the released gas were recorded for all the

sensor locations.

Volume fractions were extracted from the recorded mass fraction to compute the

mean concentrations for all the sensor locations. The average was performed over a

period of 60s during steady state conditions, such that the initial build-up of the jet was

not taken into account for the calculation of the mean values.

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X (m)

-0 10 20 30 40 50 60 70 80

Y (m)

-35

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

35

40

45

Job=491003. Var.=LOG10(FUE L (-)).




to

We show the point-wise comparison of simulated and experimental values in Fig. 3.5

and Fig. 3.6. There is good agreement between the experimental and predicted valuesfor the sensors located at ground level (120) in particular for the un-obstructed case.

In the obstructed case there are greater variation in the results, the significant under-

predictions that we see for some sensors are for locations immediately downstream of

the fence or at the edge of the cloud. For sensor positions above ground level (14m

height) are the differences between the measured and predicted values larger.

We present a quantitative comparison of measured and predicted values in terms

of the statistical performance measures in Table 3.1 and Table 3.2. These quantities

confirm that the FLACS predictions are in good agreement with the experimental mea-

surements.


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0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

0.01

0.1

1

10

100

EEC550, Mean conc.

Sensor no.

SIM/EXP

Figure 3.5: Comparison of simulated and experimental mean concentration for all

sensors of the EEC550 test.

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

0.01

0.1

1

10

100

EEC551, Mean conc.

Sensor no.

SIM/EXP



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Table 3.1: EEC550. Statistical performance measures for the comparison of predicted

and measured values.

Parameter Ideal value All sensors

Width

MRB 0

MRSE 0

MG 1

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1

Table 3.2: EEC551. Statistical performance measures for the comparison of predicted



Width

MRB 0

MRSE 0

MG 1

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1


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4. DAT638 EXPERIMENT

Experiment DAT638 consisted of a test of instantaneous release of heavy gas at labo-ratory scale. A cylindrical column of gas

with mole weight 146.1 was released

on a flat, sloping plane.


The initial gas column had a diameter

and height

. The data

in the SMEDIS data set was scaled up to field scale. There were no external wind

field and no obstructions, the motion of the gas was only driven by gravity effects on

the sloping surface. The surface slope is given by =11.6% which means that the

angle between the sloping plane and the horizontal plane was .

4.2 Scaling and computational grid

The laboratory experiment had a characteristic length scale

, and time

scale

. At full scale the corresponding scales were

and

, which gives up-scaling factors of

and

The grid is oriented with the -axis in the direction of steepest descent of the slop-

ing plane, and the -axis normal to the sloping plane. The computational domain

covers the region

,

, and

. Theinitial gas column was located in the origin. We have taken advantage of the symmetry

of the situation by using a symmetry plane located at . The grid is finest close to

the origin with a spacing of

, with stretching towards the bound-

aries. For the major part of the domain is however the grid spacing less than 4m. In

the vertical direction, the grid is stretched from the sloping plane towards the top of

the domain. The height of the initial gas column, , is covered by 8 control

volumes.

4.3 Results

We show a three-dimensional representation of the gas cloud in Fig. 4.1. We can see

clearly the effect of gravity in this plot. The cloud is not symmetrical with respect to

radial distance from the origin for a fixed height. The front of the cloud is tallest in the

direction of steepest descent for the sloping plane. The front has also reached farther

in the downhill than in the uphill direction, as expected.


sensor locations.


dose and related quantities, and maximum concentrations for all the sensor locations.


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Job=491100. Var.=FUEL (-).

Time= 30.164 (s). I=6-80, J=1-34, K=1-15.

X (m)

-50

0

50

100

150

Y (m)

0

50

100

150

Z (m)

0

50

Figure 4.1: Simulated gas cloud at

for the DAT638 test. Mass fractions

3%

of released gas are shown.

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1 2 3 4 5 6 7 8

0.1

1

10

DAT638, Dose

Sensor no.

SIM/EX

P

1 2 3 4 5 6 7 8

0.1

1

10

DAT 638, Arrival time

Sensor no.

SIM/EXP

1 2 3 4 5 6 7 8

0.1

1

10

DAT 638, Departure time

Sensor no.

SIM/EXP

1 2 3 4 5 6 7 8

0.1

1

10

DAT638 Max. conc.

Sensor no.

SIM/EX

P

Figure 4.2: Comparison of simulated and experimental quantities for all sensors of the

DAT638 test.

We show the point-wise comparison of simulated and experimental values in Fig. 4.2.

We see that there is very good agreement between the measurements and predictions

for all the quantities. Note that there is a consistent over-prediction for the maximum

concentration at all the sensor locations.

We show the dose and the maximum concentrations across two sensor arcs, located

at and in Fig. 4.3. Also here we note the good correspondence

between the measured and the predicted quantities, that the prediction of the width of

the cloud is reasonable, and that the maximum concentrations are over-predicted by a

factor 1.52.

We present a quantitative comparison of measured and predicted values in terms of

the statistical performance measures in Table 4.1. These quantities confirm the good

agreement with the experimental data that we observed in the figures above.


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0 10 20 30 40 50 60 70 80 90 10

0

0

250

500

750

1000

Arc at x=101m

Simulation

Experiment

Distance across (m)

Dose

(%*s

)

0 10 20 30 40 50 60 70 80 90 100

0

2.5

5

7.5

10

Arc at x=101m

Distance across (m)

Max.

conc.

(%)

0 10 20 30 40 50 60 70 80 90 100

0

250

500

750

1000

Arc at x =201m

Distance across(m)

Dose

(%*s

)

0 10 20 30 40 50 60 70 80 90 100

0

2.5

5

7.5

10

Arc at x=201m

Distance across(m)

Max.

conc.

(%)

Figure 4.3: Simulated and experimental dose and maximum concentration for the sen-

sor arcs in the DAT638 test.

Table 4.1: DAT638. Statistical performance measures for the comparison of predicted



MRB 0

MRSE 0

MG 1

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1

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5. THORNEY ISLAND TEST 21

Test 21 of the Thorney Island (TI21) experiments was an instantaneous release of acylindrical column of dense gas. The initial gas column collapses under the effect

of gravity, and interacts with a semi-circular obstruction down-wind of the release

point [4]. The experiment has previously been simulated by FLACS [2]. These simu-

lations were performed with a symmetry plane along the axis of the experimental site

to save computational resources. The simulated results were in fair qualitative agree-

ment with the experimental data and other simulations of the experiment found in the

literature.

A new simulation of the TI21 test was performed for the SMEDIS evaluation.

Because the wind during the test was not parallel to the axis of the experimental site,

we have abandoned the assumption of symmetry used in the previous simulations.


The gas mixture was initially confined within a cylindrical column that was 13m tall

and had a diameter of 14m, and was located at the centre of a semi-circular fence that

had a height of 5m and a diameter of 50m.

The reference wind speed during the experiment was

, and the wind

direction was

relative to the main axis of the experiment (

-axis in the simula-

tions).

Time histories of the concentration of released gas were recorded by 144 sensorslocated at different positions and at four different heights (0.4m, 2.4m, 4.4m, and

6.4m). The locations of the sensors in the horizontal plane are shown in Fig. 5.1.

Note that some of the sensors are organized in a semi-circular pattern that define arcs

of sensors that are equidistant from the source.


The computational grid comprised approximately 150000 cells and covers a domain

,

. The source is located at

m.The horizontal resolution of the grid is m in the region that contains the

source and the semi-circular fence. Outside this region the grid is stretched towards

the boundaries. The maximum stretching ratio is . The vertical resolution close

to the ground is approximately 0.3m. The maximum vertical stretching factor is also

. The minimum and maximum control volume sizes in the simulations were


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200

250

300

350

400

450

500

550

600

650

700

100 200 300 400 500 600 700

Sensor locations

75m arc

100m arc

Figure 5.1: Sensor locations for the Thorney Island experiment. Sensor arcs at 75m

and at 100m distance from the source.

5.3 Gas composition

The TI21 experiment was performed with a mixture of the refrigerant R12 and Nitro-

gen as the model gas. The concentration of R12 in the mixture was

by

volume or

by mass. The molecular weight of R12 is

and

that of nitrogen is

. The effective molecular weight of the mixture is then

(Note that in the data set the effective molecular weight is given as which

corresponds to a mixture of R12 and air.) We performed FLACS simulations with

Butane as the model gas. Butane was chosen as the model gas because the molecular

weight of Butane (

) is close to the effective molecular weight for themixture, and the properties of Butane are already implemented in FLACS.

5.4 Results

Initially the gas column collapses almost radially before the released gas is swept

downstream towards the fence by the wind. In Fig. 5.2 we show the interaction of

the gas cloud with the semi-circular fence at different times.

The experimental data set comprises the integrated dose

, cloud time of ar-

rival

, and cloud time of departure,

for 83 sensor locations. In addi-

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Figure 5.2: Thorney Island Test 21; interaction of the gas cloud and the semi-circular

fence.


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Table 5.1: Thorney Island Test 21. Statistical performance measures for the compari-

son of predicted and measured values.

Parameter Ideal value Dose

MRB 0

MRSE 0

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1

tion, quantities such as maximum dose and concentration, and the cloud width were

recorded for six sensor arcs placed at different heights 75m and 100m from of the

initial puff. Each arc comprises five sensors placed symmetrically with respect to the

main axis of the experiment.

Time histories of the mass fraction of the released gas were recorded for all the sen-

sor locations. The recorded mass fractions were the converted into volume fractions,

and the dose, arrival time, and departure time calculated.

In Fig. 5.3 we compare the measured and simulated values for

,

, and

for

the sensors for which measurements are available. For all three quantities we find that

there is an overall good agreement between the measured and the simulated results

For a detailed comparison of the results, we consider the six sensor arcs placed ina semi-circular pattern at distance of and from the source. In

Fig. 5.4 we see that there is good agreement between the computed and experimental

results for the integrated dose across each of the six arcs.


the statistical performance measures in Table 5.1. These quantities confirm the good

agreement with the experimental data that we observed in the figures above.

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0

1

0

2

0

3

0

4

0

5

0

6

0

7

0

8

0

9

0

10

0

11

0

12

0

13

0

14

0

15

0

0.01

0.1

1

10

100

TI21 Dose

Monitor

SIM/EXP

010

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0.01

0.1

1

10

100

TI21 Arrival time

Monitor

SIM/EXP

010

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0.01

0.1

1

10

100

TI21 Departure Time

Monitor

SIM/EXP

Figure 5.3: Comparison of simulated and experimental quantities for all sensors of the

Thorney Island Test 21.


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r=75m r=100m

z=0.4m

z=2.4m

z=4.4m

1 2 3 4 5

0

100

200

300

TI21 sensor arc #1

Experiment

Prediction

Sensor #

Dose(%*s)

1 2 3 4 5

0

100

200

300

TI21 sensor arc #2

Sensor #

Dose(%*s)

1 2 3 4 5

0

100

200

300

TI21 sensor arc #4

Sensor #

Dose(%*s)

1 2 3 4 5

0

100

200

300

TI21 sensor arc #5

Sensor #

Dose(%*s)

1 2 3 4 5

0

100

200

300

TI21 sensor arc #3

Sensor #

Dose(%*s)

1 2 3 4 5

0

100

200

300

TI21 sensor arc #6

Sensor #

Dose(%*s)

Figure 5.4: Comparison of simulated and experimental values for the integrated dose

across six sensor arcs in the Thorney Island Test 21.

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6. EMU CHLORINE RELEASE EXPERIMENT

The EMU Dense Jet (EMUDJ) experiment consisted of a steady jet release of Chlorine

(

) at an industrial compound located in Amlwch, Wales. The experimental site is

characterized by a hilly topography, with the sea to the north and hills of up to approx.

30m height.


We show a schematic layout of the experiment in Fig. 6.1. The experiment was

performed with a mixture of 10%

(by volume) that was released at a rate of

through a nozzle with diameter 1.74m.During the experiment the wind came from the direction of the sea (north). The

average wind speed during the experiment was .

The release duration was 900s, and gas concentrations were recorded in a total of

125 sensors located at several heights at six different distances downwind of the release

point.

6.2 Grid

Because of limited computational resources we restricted the simulation domain tocover the source region. The grid comprised approximately 250000 cells. To align the

grid with the buildings, we rotated the domain (i.e. geometry & topography) by an

angle of 32 degrees in the positive direction. The extent of the simulation domain is

m in the

,and

-directions, respectively. In a

m close

to the release, the grid size is 2m in all directions. Outside of this region, the grid has

been stretched towards the boundaries. The stretching factor is less than in all

directions.

6.3 Gas composition

The molecular weight of chlorine is

. Chlorine is not one of the standard

gases in FLACS, and for this simulation we prepared a special version of the code that

includes the gas properties of chlorine.

The molecular weight of the chlorine/air mixture is

. We also per-

formed an additional simulation in which the released gas was modeled by a mixture

of 78.786% ethane and 21.214% propane. The results of the two simulations agree

both qualitatively and quantitatively. The reported results are from the simulation of

the Chlorine/Air mixture.


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Figure 6.1: EMU Dense Jet experiment. Overview of the experimental site and the

release.

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Table 6.1: EMU Dense Jet experiment. Statistical performance measures for the com-

parison of predicted and measured values.

Parameter Ideal value Mean

MRB 0

MRSE 0

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1

6.4 Results

We show a horizontal two-dimensional field plot of the gas concentration in the plane

of the release in Fig. 6.2. We note that the jet is deflected somewhat by the buildings

in the near-field as it is swept downwind.


sensor locations that fall within the restricted simulation domain. The sensors located

at m and m were thus discarded.



period of 700s such that the initial build-up of the jet was not taken into account forthe calculation of the mean values. In Fig. 6.3 we compare the mean concentration for

all the sensor locations. Note that there is a relatively large scatter in the predicted-

to-observed ratios, but that under- and over-predictions seem to be evenly distributed.

This impression is confirmed by the statistical parameters given in Table 6.1 where we

note that both the Mean Relative Bias and the Geometric Mean Bias are fairly small,

while the large scatter is reflected in the Mean Relative Square Error and the Geometric

Mean Bias parameters that express the variance in the comparison.


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X (m)

1800 1900 2000 2100 2200 2300 2400

Y (m)

5200

5250

5300

5350

5400

5450

5500

5550

5600

5650

5700

5750

5800

5850

5900

Job=010105. Var.=LOG10(FUEL (-)).

Time= 900.080 (s). IJ plane, K=14

Figure 6.2: EMU Dense Jet experiment. Mass concentration of Chlorine in the release

plane. The scale is logarithmic and the range is from

to

.

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0 510

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

0.001

0.01

0.1

1

10

100

1000

EMU DJ mean C

Sensor

SIM/EXP

Figure 6.3: EMU Dense Jet experiment. Comparison of simulated and experimental

results of averaged concentration values.


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Test 17 of the BA-Propane experiments consisted of a continuous jet release of liquidpropane. The jet was obstructed by a 2m high arc-shaped fence downstream of the

release point. The obstacle was removed in the middle of the experiment such that

two data sets were generated. It is thus possible to compare the dispersion with and

without the obstacle. The two data sets are denoted by EEC170 and EEC171 for the

non-obstructed and obstructed case respectively.



propane was released at a rate of . The duration of the release was

, the average wind speed during the test was , and the wind direction

coincided with the release direction. The fence was located from the source, and

spans a circular arc of approximately with one end point approximately at the jet

axis such that more or less half of the jet was obstructed. Fig. 7.1 shows a plan of the

experimental site with the fence and the sensor locations.


To facilitate grid refinement by stretching towards the jet axis, the coordinate system

was rotated to align the jet and wind direction in the -direction. The computational

domain covers an area of in the horizontal plane and a height of ,

and is covered by a grid comprising

cells. Fig. 7.2 shows the horizontal

grid, and the layout of the obstacle and the monitor points in the source region. The

vertical grid is uniform close to the ground to resolve the height of the fence with five

control volumes. Above the height of the fence the grid is stretched towards the top

of the domain. The obstacle is modeled in CASD, the circular arc is represented as a

difference of two cylinders, and then differences with two boxes are used to cut off the

correct sector.


Because of the sudden de-pressurization during the release, the propane will undergo

a flash vaporization and turn into a two-phase aerosol jet. FLACS can only handle






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0

20

40

60

80

100

-100 -80 -60 -40 -20 00

20

40

60

80

100

-100 -80 -60 -40 -20 00

20

40

60

80

100

-100 -80 -60 -40 -20 0


EEC171 test.

by volume


has therefore been diluted.

7.4 Results

We show contour plots of the steady-state concentration field at ground level for the

non-obstructed and obstructed case in Fig. 7.3 and Fig. 7.4, respectively. Note in

particular that very little gas is trapped behind the fence. The jet is deflected a little by

the fence, but the distribution is nevertheless quite similar.


sensor locations.


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X (m)

-60 -40 -20 0 20 40

Y (m)

-0

20

40

60

80

100

Job=020100.


M1

M2M3

M4M5

M6M7

M8M9

M11

M13M15

M16M17

M19M21

M22

M23M25M27

M28

M29M31M32M34

M35

M40

X (m)

-60 -40 -20 0 20 40

Y (m)

-0

20

40

60

80

100

Job=020100.


Figure 7.2: Computational grid and obstacle layout for the simulation of tests EEC170

and EEC171.


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M1

M2

M3

M4M5

M6M7

M8

M9

M11

M13

M15

M16

M17M19

M21

M22

M23

M25M27

M28

M29M31M32

M34

M35

M40

X (m)

-40 -30 -20 -10 0 10 20 30 40 50 60 70 80

Y (m)

-0

10

20

30

40

50

60

70

80

90

100

Job=020100. Var.=LOG10(FUEL (-)).


Figure 7.3: Field plot of propane mass concentrations in the control volume next to

the ground for the simulation of case EEC170. The scale is logarithmic and the range

is from

to

.

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M1

M2

M3

M4M5

M6M7

M8

M9

M11

M13

M15

M16

M17M19M21

M22

M23

M25M27

M28

M29M31M32

M34

M35

M40

X (m)

-60 -50 -40 -30 -20 -10 0 10 20 30 40 50

Y (m)

-0

10

20

30

40

50

60

70

80

90

100

110

Job=020100. Var.=LOG10(FUEL (-)).


Figure 7.4: Field plot of propane mass concentrations in the control volume next to

the ground for the simulation of case EEC171. The scale is logarithmic and the range

is from

to

.


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0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5 30 32.5 35

0.01

0.1

1

10

100

EEC 170 Mean_C

Monitor

SIM/EXP

Figure 7.5: Comparison of simulated and measured mean concentrations for the

EEC170 liquid propane release experiment.



not taken into account for the calculation of the mean values.We show the point-wise comparison of simulated and experimental values in Fig. 7.5

and Fig. 7.6. For the un-obstructed case we see that there is very good correspondence

between the measurements and the simulated values. While this is not enough for

a full validation, it is nevertheless a strong indication that the FLASH model gives

reasonable results within its range of applicability.

For the obstructed case, there is larger scatter in the results. The severe under-

predictions we see in some locations are either from sensors that were located leewards

of the fence, or for sensors that lie on the edge of the cloud in regions where the

grid stretching has reduced the accuracy of the computed results. Note that in the

experiment, several sensor locations were equipped with two sensors as an internal

consistency check. The data set has not been corrected for this. As a consequence,we can observe that there is a kind of periodicity in Fig. 7.6. For example will the

under-predictions seen in the sensor pair (4,19) represent the same measurement. The

same applies to the sensor pairs (5,21) and (9,34).


the statistical performance measures in Table 7.1.

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0 2.5 5 7.5 10 12.5 15 17.5 20 22.5 25 27.5 30 32.5 35

0.01

0.1

1

10

100

EEC171 Mean_C

Monitor

SIM/EXP

Figure 7.6: Comparison of simulated and measured mean concentrations for the

EEC171 liquid propane release experiment.

Table 7.1: EEC Test 17. Statistical performance measures for the comparison of pre-

dicted and measured values of mean concentration.

Parameter Ideal value EEC170 EEC171

MRB 0

MRSE 0

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1


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Test 56 of the BA-Propane experiments consisted of a continuous jet release of liquidpropane. The jet was obstructed by a 2m high linear semi-permeable fence with 50%

blockage downstream of the release point. The obstacle was removed in the middle of

the experiment such that two data sets were generated. It is thus possible to compare

the dispersion with and without the obstacle. The two data sets are denoted by EEC560

and EEC561 for the non-obstructed and obstructed case respectively.



propane was released at a rate of . The duration of the release was , the

average wind speed during the test was , and the wind direction made an

angle

with the release direction. The fence was located

from the source,

and spans a circular arc of approximately

with one end point approximately at the

jet axis such that more or less half of the jet was obstructed. Fig. 8.1 shows a plan of

the experimental site with the fence and the sensor locations.


To facilitate grid refinement by stretching towards the jet axis, the coordinate systemwas rotated to align the jet and wind direction in the -direction. The computational

domain covers an area of in the horizontal plane and a height of ,

and is covered by a grid comprising cells. Fig. 8.2 shows the horizontal

grid, and the layout of the obstacle and the monitor points in the source region. The

vertical grid is uniform close to the ground to resolve the height of the fence with five

control volumes. Above the height of the fence the grid is stretched towards the top of

the domain.


Because of the sudden de-pressurization during the release, the propane will undergo

a flash vaporization and turn into a two-phase aerosol jet. FLACS can only handle






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0

20

40

60

80

100

0 20 40 60 80 1000

20

40

60

80

100

0 20 40 60 80 1000

20

40

60

80

100

0 20 40 60 80 100


EEC561 test. The release is located in the origin of the figure, the two other arrows

indicate the wind direction during the experiment.

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X (m)

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

Y (m)

-0

20

40

60

80

100

Job=020100.


M1

M2M3

M4 M5

M6M7

M8

M9

M10

M11 M12

M13 M14 M15

M16 M17

M18 M19

M33

M38

X (m)

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

Y (m)

-0

20

40

60

80

100

Job=020100.


Figure 8.2: Grid, monitor, and obstacle layout for the simulation of the EEC560 and

EEC561 tests.


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X (m)

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

Y (m)

-0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

Job=020100. Var.=LOG10(FUE L (-)).




to

by volume


has therefore been diluted.

8.4 Results

We show contour plots of the steady-state concentration field at ground level for the

non-obstructed and obstructed case in Fig. 8.3 and Fig. 8.4, respectively. See that

the jet is deflected by the fence and the width of the jet increases significantly in the

obstructed case.


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X (m)

-80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80

Y (m)

-0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

Job=020100. Var.=LOG10(FUE L (-)).




to


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0 5 10 15 20 25

0.01

0.1

1

10

100

EEC560, Mean conc.

Monitor

SIM/EXP



sensor locations.




not taken into account for the calculation of the mean values.

We show the point-wise comparison of simulated and experimental values in Fig. 7.5

and Fig. 7.6. In both cases there is generally good correspondence between the mea-

surements and the simulated values. In particular for the sensors located at ground

level (sensors no. 119) there is good agreement between the experimental and sim-

ulated values. For the sensors above ground level (2025) are the differences larger.

The two sensors that are under-predicted by a factor 10 in the obstructed case (12)

are located at the edge of the cloud.


the statistical performance measures in Table 8.1.

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0 5 10 15 20 25

0.01

0.1

1

10

100

EEC561, Mean conc.

Monitor

SIM/EXP



Table 8.1: EEC Test 56. Statistical performance measures for the comparison of pre-

dicted and measured values of mean concentration.

Parameter Ideal value EEC170 EEC171

MRB 0

MRSE 0

ln(MG) 0

ln(VG) 0

Fa2 1

Fa5 1


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9. SUMMARY AND CONCLUSIONS

The results of the simulations performed for the SMEDIS project are condensed intoFig. 9.1 in which we plot the Geometric Mean (MG) against the Geometric Mean Vari-

ance (VG). We have also plotted the validation parabola and the factor-of-2 limits.

The results of the BA-Propane tests show that the flashing model within its area

of applicability gives a good representation of the source during flashing release, and

that FLACS handles the subsequent dispersion well. In these cases we predicted much

larger over- and under-predictions including zero predictions for sensors located

above the ground that for those at ground level. The MG and VG measures are singular

for zero concentrations, and the computed values of MG and VG is very sensitive to

the choice of threshold values. Looking at the background data shown in Figures 3.5,

3.6, 7.5, 7.6, 8.5, and 8.6 we notice that the overall performance of the predictionsseems to be better than Fig. 9.1 indicates. This is also confirmed by the more robust

MRB and MRSE parameters. It is possible that the model does not take properly into

account heating of the cold gas cloud by the ground and that this can explain the low

predicted concentrations above ground level. We will be investigated further. We also

noted some discrepancies close to the fences.

The results of the predictions of the instantaneous release experiments TI21 and

DAT638 are generally good and confirms the limited results that were obtained for

the TI21 case earlier [2]. The predictions of the EMUDJ experiments also show that

FLACS is capable of simulating more realistic scenarios like release in a complex

industrial site with non-trivial topography quite well.The results of the simulations performed during the SMEDIS project increase the

confidence in FLACS to predict dense gas release and dispersion scenarios. The

present results are not sufficient for a full validation of the model with estimates of

the uncertainties of the computed predictions. The good performance across the board

of test cases show however that FLACS can be applied to obtain reliable predictions

of dense gas dispersion.


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1

10

100

1000

1/8 1/4 1/2 1 2 4 8

GeometricMeanVariance(VG)

Geometric Mean Bias (MG)

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC550

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC551

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



DAT638

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EMUDJ

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



TI21

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC170

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC171

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC560

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



EEC561

1

10

100

1000

1/8 1/4 1/2 1 2 4 8



1

10

100

1000

1/8 1/4 1/2 1 2 4 8



Figure 9.1: Geometric Mean Bias and Geometric Mean Variance for all the test cases

plotted in the validation parabola.

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REFERENCES

[1] H.-C. Salvesen. Modeling of jet release of liquefied gas under high pressure. Tech-nical Report CMR-95-F20062, Christian Michelsen Research as, Bergen, Norway,

1995. Confidential.

[2] H.-C. Salvesen and O. Asheim. Simulation of heavy gas dispersion with CFD

code FLACS. Technical Report CMR-95-F20021, Christian Michelsen Research

as, Bergen, Norway, 1995. Confidential.

[3] S.F. Jagger, B. Carissimo, and N. Daish. Definition of parameters for model runs,

WP4. Report SMEDIS/96/14/D, SMEDIS Project, October 1998. Draft, Version

0.5.

[4] M. E. Davies and S. Singh. The phase II trials: A data set on the effects of ob-

structions. J. Haz. Mater., 11:301323, 1985.

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