# CFD Modeling of Gas Release and Dispersion: Prediction of Flammable Gas Clouds

**ABSTRACT** Advanced computational fluid dynamics (CFD) models of gas release and dispersion (GRAD) have been developed, tested, validated

and applied to the modeling of various industrial real-life indoor and outdoor flammable gas (hydrogen, methane, etc.) release

scenarios with complex geometries. The user-friendly GRAD CFD modeling tool has been designed as a customized module based

on the commercial general-purpose CFD software, PHOENICS. Advanced CFD models available include the following: the dynamic

boundary conditions, describing the transient gas release from a pressurized vessel, the calibrated outlet boundary conditions,

the advanced turbulence models, the real gas law properties applied at high-pressure releases, the special output features

and the adaptive grid refinement tools. One of the advanced turbulent models is the multifluid model (MFM) of turbulence,

which enables to predict the stochastic properties of flammable gas clouds. The predictions of transient threedimensional

(3D) distributions of flammable gas concentrations have been validated using the comparisons with available experimental data.

The validation matrix contains the enclosed and nonenclosed geometries, the subsonic and sonic release flow rates and the

releases of various gases, e.g., hydrogen, helium, etc. GRAD CFD software is recommended for safety and environmental protection

analyses. For example, it was applied to the hydrogen safety assessments including the analyses of hydrogen releases from

pressure relief devices and the determination of clearance distances for venting of hydrogen storages. In particular, the

dynamic behaviors of flammable gas clouds (with the gas concentrations between the lower flammability level (LFL) and the

upper flammability level (UFL)) can be accurately predicted with the GRAD CFD modeling tool. Some examples of hydrogen cloud

predictions are presented in the paper. CFD modeling of flammable gas clouds could be considered as a costeffective and reliable

tool for environmental assessments and design optimizations of combustion devices. The paper details the model features and

provides currently available testing, validation and application cases relevant to the predictions of flammable gas dispersion

scenarios. The significance of the results is discussed together with further steps required to extend and improve the models.

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**ABSTRACT:**This paper presents the comparison of IEC60079-10, CSA B108:99, NFPA 52 and API Standards requirements for determining sizes of hazardous locations with simulation results obtained by computational fluid dynamics (CFD) modeling. International standard IEC60079-10 determines the size of a hazardous location by a calculation of the hypothetical combustible volume caused by a fluid leak under specific temperatures, and ventilation rates. Canadian standard CSA B108:99 and American standard NFPA 52 use a prescriptive method to assign the size of a hazardous location depending on fuel quantities contained in the equipment. Considering hydrogen high buoyancy and diffusivity, requirements of both standards are likely too conservative. The PHOENICS CFD software package was used to solve the continuity, momentum and concentration equations with the appropriate boundary conditions, buoyancy model and turbulence models. Numerical results on hydrogen concentration predictions were obtained in the real industrial environment, which is the Hydrogen Energy Station (HES) produced by Stuart Energy Systems Corporation.01/2004; - SourceAvailable from: A. Tchouvelev[Show abstract] [Hide abstract]

**ABSTRACT:**In this paper, CFD techniques were applied to the simulations of hydrogen release from a 400-bar tank to ambient through a Pressure Relieve Device (PRD) 6 mm (¼") opening. The numerical simulations using the TOPAZ software developed by Sandia National Laboratory addressed the changes of pressure, density and flow rate variations at the leak orifice during release while the PHOENICS software package predicted extents of various hydrogen concentration envelopes as well as the velocities of gas mixture for the dispersion in the domain. The Abel-Noble equation of state (AN-EOS) was incorporated into the CFD model, implemented through the TOPAZ and PHOENICS software, to accurately predict the real gas properties for hydrogen release and dispersion under high pressures. The numerical results were compared with those obtained from using the ideal gas law and it was found that the ideal gas law overestimates the hydrogen mass release rates by up to 35% during the first 25 seconds of release. Based on the findings, the authors recommend that a real gas equation of state be used for CFD predictions of high-pressure PRD releases.01/2005; -
##### Article: Turbulence measurements in axisymmetric jets of air and helium. I - Air jet. II - Helium jet

[Show abstract] [Hide abstract]

**ABSTRACT:**Results are presented of measurements on turbulent round jets of air and of helium of the same nozzle momentum efflux, using, for the air jets, x-wire hot-wire probes mounted on a moving shuttle and, for He jets, a composite probe consisting of an interference probe of the Way-Libby type and an x-probe. Current models for scalar triple moments were evaluated. It was found that the performance of the model termed the Full model, which includes all terms except advection, was very good for both the air and the He jets.Journal of Fluid Mechanics 02/1993; · 2.29 Impact Factor

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Journal Information

© 2005 Springer. Printed in the Netherlands.

CFD MODELING OF GAS RELEASE AND DISPERSION:

PREDICTION OF FLAMMABLE GAS CLOUDS

VLADIMIR M. AGRANAT*, ANDREI V. TCHOUVELEV,

ZHONG CHENG

A.V. Tchouvelev & Associates Inc., 6591 Spinnaker Circle,

Mississauga, Ontario, L5W 1R2, Canada

SERGEI V. ZHUBRIN

Flowsolve Limited, 2nd Floor, 40 High Street, Wimbledon Village,

London SW19 5AU, United Kingdom

Abstract. Advanced computational fluid dynamics (CFD) models of gas

release and dispersion (GRAD) have been developed, tested, validated and

applied to the modeling of various industrial real-life indoor and outdoor

flammable gas (hydrogen, methane, etc.) release scenarios with complex

geometries. The user-friendly GRAD CFD modeling tool has been designed as

a customized module based on the commercial general-purpose CFD software,

PHOENICS. Advanced CFD models available include the following: the

dynamic boundary conditions, describing the transient gas release from a

pressurized vessel, the calibrated outlet boundary conditions, the advanced

turbulence models, the real gas law properties applied at high-pressure releases,

the special output features and the adaptive grid refinement tools. One of the

advanced turbulent models is the multi-fluid model (MFM) of turbulence,

which enables to predict the stochastic properties of flammable gas clouds. The

predictions of transient 3D distributions of flammable gas concentrations have

been validated using the comparisons with available experimental data. The

validation matrix contains the enclosed and non-enclosed geometries, the

subsonic and sonic release flow rates and the releases of various gases, e.g.

hydrogen, helium, etc. GRAD CFD software is recommended for safety and

environmental protection analyses. For example, it was applied to the hydrogen

safety assessments including the analyses of hydrogen releases from pressure

______

*To whom correspondence should be addressed. Vladimir Agranat, 6591 Spinnaker Circle,

Mississauga, Ontario, L5W 1R2, Canada; e-mail: info@tchouvelev.org and acfda@sympatico.ca

Page 2

CFD MODELING OF GAS RELEASE AND DISPERSION

2

relief devices and the determination of clearance distances for venting of

hydrogen storages. In particular, the dynamic behaviors of flammable gas

clouds (with the gas concentrations between the lower flammability level (LFL)

and the upper flammability level (UFL)) can be accurately predicted with the

GRAD CFD modeling tool. Some examples of hydrogen cloud predictions are

presented in the paper. CFD modeling of flammable gas clouds could be

considered as a cost effective and reliable tool for environmental assessments

and design optimizations of combustion devices. The paper details the model

features and provides currently available testing, validation and application

cases relevant to the predictions of flammable gas dispersion scenarios. The

significance of the results is discussed together with further steps required to

extend and improve the models.

Keywords: Computational fluid dynamics (CFD); numerical modeling tool; flammable

gas cloud; gas release and dispersion; environmental protection and safety analyses;

clearance distance

1. Introduction

In many industries, there are serious safety concerns related to the use of

flammable gases in indoor and outdoor environments. It is very important to

develop reliable methods of analyses of flammable gas release and dispersion

(GRAD) in real-life complex geometry cases. Computational fluid dynamics

(CFD) is considered as one of the promising cost-effective approaches in such

analyses. The objective of this paper is to describe the advanced GRAD CFD

models, which have been recently developed, tested, validated and applied to

the modeling of various industrial indoor and outdoor scenarios of releases of

flammable gases (hydrogen, methane, natural gas, etc.) in domains with

complex geometries.

There are many general-purpose commercial CFD software packages

capable of modeling and analyses of fluid flows and heat/mass transfer

processes, e.g. the PHOENICS software1. However, none of these packages is

properly customized for GRAD modeling and analyses of spatial and temporal

behaviors of flammable gas clouds. In particular, any direct practical

application of these codes to GRAD modeling requires a high level of user’s

expertise in CFD field due to the complexities of physical processes involved

and mathematical models analyzed. Moreover, in the GRAD modeling, proper

non-standard settings are needed for transient boundary conditions, real gas

properties, special numerical grid refinements and proper turbulence models. As

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CFD MODELING OF GAS RELEASE AND DISPERSION

3

a result, there is a practical need for developing a user-friendly and validated

GRAD CFD modeling tool, which is capable of predicting the behaviors of

flammable gas clouds.

Over the last three years, significant efforts have been undertaken by Stuart

Energy Systems Corporation (SESC) and A.V. Tchouvelev & Associates Inc. in

order to develop, test and validate a GRAD CFD modeling tool. Some results of

this work have been recently published2-8. This paper reviews the previously

published results, describes the modeling approach in more detail and provides

currently available validation and application cases relevant to the predictions

of flammable gas dispersion scenarios.

2. GRAD CFD modeling tool capabilities

The GRAD CFD modeling tool has been designed as a customized module

based on the commercial general-purpose CFD software, PHOENICS1. The

modeling approach, the general governing equations and the additional sub-

models are described in this section. Also, the similarity theory is described.

2.1. MODELING APPROACH

PHOENICS CFD software was selected as the flexible framework for

performing GRAD CFD analyses, in which pragmatic flammable gas release

and dispersion models were incorporated for practically affordable predictions

using the PHOENICS solvers. PHOENICS is a well-recognized general-

purpose CFD package that has been validated and successfully used around the

world for more than 20 years. Its main features and capabilities have been

described by its developers, CHAM Limited, in references item8 and on the

CHAM’s web site, www.cham.co.uk. One of the key features of PHOENICS is

its easy programmability, i.e. it enables a user to add user-defined sub-models

without a direct use of programming languages such as FORTRAN or C. This

feature was used to incorporate the non-standard advanced GRAD sub-models

described in section 2.3.

There are three major stages in GRAD modeling: 1) steady-state before-the-

release run aimed at preparing the initial 3D distributions of pressure and

velocity in the computational domain; 2) transient during-the-release run made

to describe the spatial and temporal behaviors of flammable gas cloud during

the gas release; and 3) transient after-the-release run aimed at predicting the

dispersion of the released gas to acceptable levels within the computational

domain. First, the modeling is performed under steady-state conditions without

any flammable gas leak. The velocity and pressure profiles obtained from the

steady-state calculations are then used as the initial conditions for the during-

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CFD MODELING OF GAS RELEASE AND DISPERSION

4

the-release transient simulations, which are performed with a flammable gas

leak at the specified rate and time increments. After-the-release transient

simulations predict the flammable gas dispersion in the computational domain

below the required values of volume concentrations of flammable gas (usually

below the Lower Flammability Level (LFL)). It should be noted that both the

during-the-release and the after-the-release transient simulations allow for: (i)

inclusion of the transient behavior of all calculated variables (pressure, gas

density, velocity and flammable gas concentration); (ii) simulation of the

movement of flammable gas clouds with time; as well as, (iii) evaluation of the

safety by analyzing the iso-surfaces of the flammable gas concentration. The

flammable gas convection, diffusion, buoyancy and transience are modeled

based on the general 3D conservation equations and the details of various

release and dispersion scenarios are introduced via the proper initial and

boundary conditions. One of the advantages of PHOENICS is that it contains

various turbulence models and enables to select a proper model suitable for a

particular practical case. In particular, the unique to PHOENICS turbulence

models such as the LVEL model and the multi-fluid model of turbulence

(MFM) were tested in the GRAD modeling.

2.2. GOVERNING EQUATIONS

The transient processes of flammable gas convection, diffusion and buoyancy

are governed by the general conservation equations, i.e. the momentum

equations, the continuity equation and the flammable gas mass conservation

equation. These governing equations are well described in the PHOENICS

documentation1 and could be expressed as:

,)(

)(

i

i

i effi

i

f

x

P

graduUudiv

t

u

ρ ρνρ

ρ

∂

+

∂

∂

−=−+

∂

i=1,2,3 (1)

0)(

=+

∂

∂

U div

t

ρ

ρ

, (2)

")(

)(

C gradCD UC

ρ

div

t

C

eff

=−+

∂

∂

ρ

ρ

, (3)

where

velocity components; U is the velocity vector;

component (per unit mass) in the

is the mass concentration of flammable gas; "

D

is the effective flammable gas diffusion coefficient in air;

1x ,

2 x and

3x denote the Cartesian coordinates;

1 u ,

3 , 2 , 1

=

2 u and

) is the body force

3 u are the

if (

i

ix - direction; P is the gas mixture pressure; C

C is the flammable gas source;

eff

eff

ν

is the

Page 5

CFD MODELING OF GAS RELEASE AND DISPERSION

5

effective kinematic viscosity of gas mixture and ρ is the gas mixture density,

which is dependent on the flammable gas mass concentration, C, or the

flammable gas volumetric concentration ,α :

T]RC CR[

P

air gas

) 1 (

−+

=

ρ

,

airgas

gas

RC CR

CR

1 ( −+

)

=

α

(4)

Here, T is the absolute temperature; and

of flammable gas and air, respectively.

The volumetric buoyancy force, acting on the fluid particles in the

direction (vertical direction), is represented by the term,

significance is proportional to the difference between the local transient gas

mixture density and the reference density of air under the ambient pressure and

temperature. According to the first equation (4), the gas mixture density is

calculated as an inverse-linear function of the local mass concentration of

flammable gas, C, with the coefficients dependent on the gas constants of air

and flammable gas and the local pressure and temperature. As a result, the

significance of the buoyancy force depends on the transient 3D flammable gas

mass concentration distribution.

The local values of effective viscosity and diffusion coefficient,

D

, include both laminar and turbulent components and are calculated

according to the following equations:

gas

R

and

air

R are the gas constants

3x -

3f ρ , in equation (1). Its

eff

ν

and

eff

ttll efftl eff

D Pr/ Pr/,

ννννν+=+=

(5)

Here, subscripts l and t are applied to the laminar and turbulent properties,

respectively; and Pr is the Prandtl/Schmidt number.

The laminar kinematic viscosity of gas mixture can be approximated by:

ρνραν αρν

/ ]) 1 ([

air air gasgasl

−+=

(6)

Here,

gas

ν

and

air

ν

are the laminar kinematic viscosities of flammable gas

and air, respectively; and

gas

ρ

and

air

ρ

air, respectively.

A proper turbulence model was used in each particular practical case in

order to calculate the local values of turbulent kinematic viscosity,

models used were the LVEL model, the k-ε model, the modifications of k-ε

model and the MFM.

are the densities of flammable gas and

t ν . Among

Page 6

CFD MODELING OF GAS RELEASE AND DISPERSION

6

2.3. ADVANCED MODEL FEATURES

A few advanced CFD sub-models were developed as a part of GRAD CFD

module. These sub-models simulate the following features: the dynamic

boundary conditions, describing the transient gas release from a pressurized

vessel; the calibrated outlet boundary conditions; the real gas law properties

applied at high-pressure releases; the advanced turbulence models; the adaptive

grid refinement tools; and the special output features.

2.3.1. Dynamic Boundary Conditions

In general, the transient (dynamic) boundary conditions should be applied at the

flammable gas release location in order to properly describe the released gas

mass flow rate, which depends on time. Depending on the pressure in the gas

storage tank, the regime of release could be subsonic or sonic (choked).

Assuming the ideal gas law equation of state and a critical temperature at the

leak orifice and solving the first-order ordinary differential equation for density,

)(t

ρ

, the transient mass flow rate at the sonic regime of release could be

approximated as6

,)()()(

1

1

)

1

2

+

(

V

AC

0

RTt

d

em

&

Atut

dt

d

Vtm

&

−

+

−

≈=−=

γ

γ

γ

γ

ρ

ρ

1

1

000

)

1

2

+

(

−

+

=

γ

γ

γ

γρ P ACm

&

d

(7)

where u(t) is the flammable gas velocity at the leak orifice; V is the tank

m &,

in the tank and the gas pressure in the tank, respectively, at t=0; A is the leak

orifice cross-sectional area; Cd is the discharge coefficient; and γ is the ratio

of specific heats for flammable gas:

γ

volume;

00

ρ and

0 P are the flammable gas mass flow rate, the gas density

V

PC

C

=

, with

P

C and

V

C being the

specific heat at constant pressure and constant volume, respectively. For

example, for hydrogen,

and the initial hydrogen mass release rate

corresponding to the tank with a pressure of 400 bars and a ¼” leak orifice is

=

0

m &

0.753 kg/s, based on the second equation (7) with Cd = 0.95. It

should be noted that the choked release lasts until the ratio of the pressure in the

41 . 1

=γ

about

Page 7

CFD MODELING OF GAS RELEASE AND DISPERSION

7

tank over the ambient pressure, namely,

atm

P

P0

is greater than or equal to

1

)

2

1

(

−

+

γ

γ

γ

(it is about 1.90 for hydrogen).

2.3.2. Real Gas Law Properties

Under high pressure, flammable gases display gas properties different from the

ideal gas law predictions. For example, at ambient temperature of 293.15˚K and

a pressure of 400 bars, the hydrogen density is about 25% lower than that

predicted by the ideal gas law. In order to account for real gas law behavior, the

GRAD CFD module was provided with additional sub-models6. In particular,

for hydrogen release and dispersion modeling the Abel-Nobel equation of state

(AN-EOS) was used to calculate the hydrogen compressibility,

empirical hydrogen co-density, dH2 :

ρ

ρ

2

H

z

, in terms of

1

) 1 (

2

2

22

2

−

−==

H

H

HH

H

dTR

P

z

, (8)

where ρ

temperature and gas constant, respectively. It should be noted that the hydrogen

compressibility,

2

H

z

, is equal to 1 for the ideal gas law. The hydrogen gas

constant, RH2, is 4124 J/(kgK). The hydrogen co-density, dH2 , is about 0.0645

mol/cm3, or 129 kg/m3. Equation (8) can be simplified as:

2

H , P, T and RH2 are the compressed hydrogen density, pressure,

TRd

P

z

HH

H

22

2

1+=

(9)

The AN-EOS accounts for the finite volume occupied by the gas molecules,

but it neglects the effects of intermolecular attraction or cohesion forces. It

accurately predicts the high-pressure hydrogen density behavior6.

2.3.3. Turbulence Model Settings

The turbulence models tested for GRAD modeling cases were as follows:

LVEL model, k-ε model, k-ε RNG model, k-ε MMK model and MFM. It was

found that the LVEL model performs better in releases of flammable gas in

congested spaces (indoor environment containing the solid blockages) and the

k-ε RNG model performs better for jet releases in open space. The details on

sensitivity runs related to the turbulence model selection are described in

previous papers2-8. MFM enables to predict the stochastic properties of

flammable gas clouds by way of computing the probability density functions,

Page 8

CFD MODELING OF GAS RELEASE AND DISPERSION

8

which record for what proportion of time the fluid at a point in space is in a

given state of motion, temperature and composition. However, the MFM

approach needs to be further developed for GRAD CFD modeling, with the aim

of finding a proper set of model constants and/or functions, which are suitable

for the prediction of turbulent flammable gas dispersion in both indoor and

outdoor environment.

2.3.4. Local Adaptive Grid Refinement (LAGR)

The LAGR techniques are needed in GRAD CFD modeling in order to

accurately capture the flammable cloud behaviors near the release location and

in the locations with significant gradients of flammable gas concentration while

considering large domains of practical interest. This refinement should be based

on the local features of flammable gas mass concentration as a key unknown

variable. The iterative technique of LAGR was developed, implemented into

the PHOENIS CFD software, tested and validated for the two GRAD CFD

module validation cases, namely, the hydrogen release within a hallway, and

the helium release within a garage with a car. The results of LAGR modeling

were more accurate than the fixed grid solutions obtained with the standard grid

refinement tools (see details in sections 3.2 and 3.3). However, additional

development work and testing are needed in order to use LAGR on regular

basis for GRAD modeling.

2.3.5. Special Output Features

The dynamics and extents of flammable gas cloud, containing the gas volume

concentrations between LFL and UFL, are of major interest in any GRAD

modeling. The total volume of space occupied by this cloud and the total mass

of flammable gas in the cloud are listed as the special output features. GRAD

CFD module calculates these special output quantities as functions of time

based on the transient 3D distributions of gas concentrations and gas mixture

density.

2.4. SIMILARITY THEORY

The solutions of GRAD governing equations under the prescribed boundary

conditions and properties depend on the following dimensionless parameters:

the Reynolds number (Re), the Schmidt number (Sc), the Mach number (Ma),

the Richardson number (Ri) and the density ratio (

follows to represent the turbulence, diffusion, compressibility, buoyancy and

density difference effects, respectively:

ρ

k ), which are defined as

Page 9

CFD MODELING OF GAS RELEASE AND DISPERSION

9

gas

gasL

ν

U

=

Re

,

gas

gas

D

Sc

ν

=

,

W

U

Ma

gas

=

,

2

)(

gas gas

gasair

ρ

U

gL

Ri

ρρ−

=

,

gas

(10)

air

k

ρ

ρ

ρ=

.

Here

size;

for hydrogen and 1.15×10-4 m2/s for helium);

coefficient of the released gas in the air (6.1×10-5 m2/s for hydrogen, and

ρ

is the reference density, i.e. the air density,

which is 1.209 kg/m3 at 1 atm and 20ºC; W is the gas sonic speed, which is

61. 1305

222

==

TRW

HHH

γ

gas

U

is the flammable gas release velocity at the orifice; L is the orifice

is the laminar kinematic viscosity of the released gas (1.05×10-4 m2/s

gas

ν

gas

D

is the laminar diffusion

5.7×10-5 m2/s for helium);

air

equal

W

He

to

R

He

m/s for

k

hydrogen and

35. 1005

==

T

He

γ

m/s for helium; and

ρ

ρ

is the parameter

) 1( 1 (

−+

k

air

ρ

characterizing the variable gas mixture density:

1

)

−

=

C

ρ

or

)) 1

−

( 1 (

1

αρρ

for the circular orifice. If the leak orifice is not circular, a hydraulic diameter,

which is defined as

perimeter. wetted

For a rectangular leak hole with sizes of a and b, the hydraulic diameter is

defined as

ba

+

In order to validate the CFD modeling results for hydrogen release and

dispersion, proper experimental data on hydrogen release and dispersion are

required. For reasons of safety, helium was often used in validation experiments

as an alternative for hydrogen. However, helium and hydrogen differ in their

buoyancy, turbulence, diffusion and density. This can be clearly seen from the

following comparison of the dimensionless parameters (10) for these gases and

the estimation of the distortions in flows of the two gases:

ρ

+=

−

k

air

. It should be noted that L is the leak orifice diameter

area sectionalcross

×

4

−

=

L

, is used for the scaling length.

ab

L

=2

.

91. 0

Re

Re

2

Re

==

H

He

α

,

17. 1

2

==

H

He

Sc

Sc

Sc

α

,

30. 1

2

==

H

He

Ma

Ma

Ma

α

,

47. 0

2

==

H

He

Ri

Ri

Ri

α

,

50. 0

2

,

,

==

H

He

k

k

k

ρ

ρ

ρ

α

The large distortions result in significant differences in hydrogen and

helium release processes: helium is less “turbulent” and “buoyant” but more

“compressible” than hydrogen. The hydrogen buoyancy and turbulence effects

Page 10

CFD MODELING OF GAS RELEASE AND DISPERSION

10

would be underestimated if helium were used for validation of hydrogen

modeling. The choked (sonic) release velocity would be smaller and, as a result,

the compressibility would be overestimated as well. Therefore, hydrogen,

though combustible, has to be used for the validation of CFD modeling of

hydrogen releases and dispersion. Some validation results are reported in the

following section.

3. GRAD CFD Software Validation

The GRAD CFD modeling software needs to be validated before it can be

widely applied to industrial projects. The predictions of transient 3D

distributions of flammable gas concentrations with the GRAD CFD module

were validated using the comparisons with available experimental data on gas

release and dispersion.

3.1. VALIDATION MATRIX

The validation matrix contains the enclosed and non-enclosed geometries, the

subsonic and sonic release flow rates and the releases of various gases, i.e.

hydrogen, helium, etc. The validation matrix and some validation cases are

described in this paper. Seven validation scenarios were selected to cover

different industrial release environments and leak types. Table 1 shows the

validation matrix, classified by the experiment conditions, such as leak types,

release directions and domain types, etc. Seven scenarios covered the leaks

from small subsonic releases to large choked releases. The validation work on

the wide range of the Reynolds numbers (50<Re<107), the Mach numbers

(0≤Ma≤1) and the Richardson numbers (10-5<Ri<104) helped validate and

calibrate the CFD models and find the suitable settings for the coefficients used

in the boundary conditions and the turbulence models for the GRAD modeling.

TABLE 1. GRAD CFD module validation scenarios

Description of experiment Case

No.

Case

name

Domain Leak

direction

Leak type Experimental

data

CFD Model Data source

reference

1

Helium

jet

Vertical

Subsonic,

helium

release

Steady-state,

velocities,

concentrations

and turbulence

intensities

Incompressible,

steady-state

Reference11

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CFD MODELING OF GAS RELEASE AND DISPERSION

11

2

3

H2 jet

Subsonic,

H2 release

Choked,

H2 release

Subsonic,

H2 release

Subsonic,

helium

release

Subsonic,

H2 and

helium

releases

Subsonic,

H2 release

and

dispersion

Transient,

concentrations

Steady-state,

concentrations

Transient,

concentrations

Transient,

concentrations

Incompressible,

transient

Reference13

INERIS

Jet

Open

Horizontal

Compressible,

steady-state

Reference14

4

5

Hallway

End

Hallway

middle

Reference9

6

Garage

with

a car

H2 vessel

Semi-

enclosed

Transient,

concentrations

Incompressible,

transient and

steady-state

Reference10

7

Enclosed

Vertical

Transient,

concentrations

during

dispersion

Incompressible,

transient

Reference15

3.2. HYDROGEN SUBSONIC RELASE IN A HALLWAY

An example of GRAD CFD validation work was described in detail in the

earlier paper2. This work was conducted by SESC using the experimental and

numerical data9 published by Dr. M.R. Swain et al. Below is a brief description

of this validation work.

A hydrogen release benchmark problem with a simple geometry was used

for CFD model validation in this case. In particular, in this scenario (see Figure

1), the hydrogen was released at the rate of 2 SCFM (standard cubic feet per

minute) from the floor at the left end of a hallway with the dimension of 114 in

× 29 in × 48 in (2.9 m × 0.74 m ×1.22 m). At the right end of the hallway, there

were a roof vent and a lower door vent for the gas ventilation. Four sensors

were placed in the domain to record the local hydrogen concentration variations

with time. Figure 1 shows the geometry and the numerical results obtained, i.e.

the 3% hydrogen volume concentration iso-surface at 1 minute after the start of

hydrogen release and the predicted values of the hydrogen volume

concentration at the four sensors locations. The initial grid used was a coarse

grid of 36×10×18 cells. It was found that the concentration differences between

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12

the predictions and the measurements were about 20% for sensors 1 and 2 and

10% for sensors 3 and 4.

Sensor 2

Sensor 1

Sensor 3

Sensor 4

Figure 1. 2-SCFM hydrogen release: hydrogen sensors and predicted 3%

hydrogen volume concentration iso-surface at 1 minute (left) embedded locally

refined adapted grid (middle) and velocity distribution on adapted grid (right).

LAGR was applied to the modelling of hydrogen release in a hallway as

illustrated by Figure 1. Table 2, comparing the predicted and the measured

hydrogen volume concentrations, confirms that LAGR improves the accuracy

of the simulations, both quantitatively and qualitatively. In fact, the simulation

on the initial coarse grid of 36×10×18 fails to predict the increase of

concentration at the position of Sensor 4 relative to that of the Sensor 1. This

flow feature, however, is realistically captured on the grid with LAGR.

TABLE 2. Steady-state results for hydrogen release in a hallway (k-ε MMK

turbulence model)

Simulations/Experiment Sensor 1 Sensor 2 Sensor 3 Sensor 4

Experimental observations

Initial coarse grid, 36×10×18

Adaptive refined, 36×20×23

1.35%

1.54%

1.34%

4.90%

5.58%

5.68%

4.95%

5.67%

5.77%

1.80%

1.42%

1.70%

3.3. HELIUM SUBSONIC RELEASE IN A GARAGE WITH A CAR

Another GRAD CFD module validation work was conducted using the

experimental and numerical data published by Dr. M.R. Swain et al.10 on the

helium subsonic release in a garage with a car. Figure 2 shows the geometry of

the case considered. The green blocks mark the locations of four helium sensors

in the domain.

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Figure 2. Geometry and helium sensors for helium subsonic release in a garage.

LAGR was also applied to this modeling case. Table 3 shows that LAGR

helps reduce the predicted concentrations at the locations of Sensor 1 and

Sensor 4 significantly. The predicted results are in accord with the CFD

simulations reported elsewhere.

TABLE 3. Steady-state results for helium release in a garage with a car (LVEL

turbulence model)

Simulations Sensor 1 Sensor 2 Sensor 3 Sensor 4

Swain’s CFD results

Initial coarse grid, 32×16×16

Adaptive refined, 39×26×24

Adaptive refined, 58×26×27

0.5%

1.92%

0.98%

0.79%

2.55%

2.53%

2.66%

2.70%

2.55%

2.52%

2.62%

2.67%

1.0%

1.94%

1.08%

1.01%

3.4. HELIUM TURBULENT SUBSONIC JET

Another example of GRAD CFD module validation work was described in the

reference paper5. Below is the brief description of the major findings. In this

validation work, a vertical helium jet reported by Panchapakesan and Lumley11

was simulated using the GRAD CFD module. The real geometry was simplified

by a 2D axi-symmetric computational domain to save the computational

resources. The mixed gas was assumed to have incompressible gas properties so

the inverse linear function was used to calculate the mixture density dependent

on the local helium mass concentration and the helium and air densities. The k-ε

RNG turbulence model was used while solving the governing equations to

predict the velocity and mass/volumetric concentration profiles. The numerical

results showed a good agreement with experimental data in both radial and

axial directions with the errors less than 10%. The simulation results were also

compared with other published helium experimental data obtained by Keagy

and Weller, Way and Libby, Aihara et al. and the correlations made by Chen

and Rodi12 for velocity and concentration. The satisfactory agreement (within

10%) between the experimental and numerical data in the three jet regions

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proved that the GRAD CFD model is robust, accurate and reliable, and that the

CFD technique can be used as an alternative to the experiments with similar

helium jets. It also indicated that the CFD model can accurately predict similar

hydrogen releases and dispersion if the model is properly calibrated with

hydrogen coefficients when applying to hydrogen jets.

4. GRAD CFD Software Applications

CFD modeling of flammable gas clouds could be considered as a cost effective

and reliable tool for environmental assessments and design optimizations of

combustion devices. In particular, the GRAD CFD software is recommended

for safety and environmental protection analyses. The transient behaviors of

flammable gas clouds can be accurately predicted with this modeling tool. For

example, it was applied to the hydrogen safety assessments including the

analyses of hydrogen releases from pressure relief devices (PRD) and the

determination of clearance distances for venting of hydrogen storages2-8. An

example of hydrogen cloud predictions is presented below.

4.1. RELEASE IN A HYDROGEN GENERATOR ROOM

This section discusses one of the potential hydrogen release scenarios – a

hydrogen release into the electrolytic hydrogen generator room during self-

purging start-up procedure3. At start-up, to ensure only high purity gas is

directed for compression, hydrogen is being vented for 10 min. After 10 min, a

regulator re-directs hydrogen flow from vent to process. The point of potential

release is the vent pipe at the roof of the hydrogen generator. The outlet pipe

size is 2” and the constant release flow rate is 0.0035 Nm3/s. First, the CFD

modeling was performed under steady-state conditions without any hydrogen

leak. The velocity profiles obtained from the steady state were then used as the

initial conditions for the during-the-release simulations, which were performed

with a hydrogen leak at the specified rate and time increments. After-the-release

simulations predicted the hydrogen dispersion in the room below 10% of the

LFL.

4.1.1. Before-the-Release Simulation

The existence of a louver and an exhaust fan (flow rate of 1 m3/s) creates a

steady-state 3D airflow in the generator room. This flow was simulated first,

before trying to simulate the transient 3D behavior of hydrogen cloud

introduced by the hydrogen release. Figure 3 shows the steady-state air

velocities created by the louver and the exhaust fan.

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Figure 3. Air velocities at X- and Y-planes before the hydrogen release in the

hydrogen generator room.

4.1.2. During-the-Release Simulation: Release from Hydrogen Vent Line

The hydrogen release scenario considers the worst case scenario when, for

whatever reason, during the hydrogen generator start-up self-purging procedure

the hydrogen vent line on the roof of the generator comes off, thus causing all

hydrogen being produced during the self-purging procedure (10 min) to leak

into the hydrogen generator room. It is also assumed that all hydrogen sensors

intended to shut down the generator during the self-purging procedure are

disabled. Room ventilation is provided by the louver and the exhaust fan during

the release. CFD predictions of 3D hydrogen concentration distribution are

shown in Figures 4, which shows the hydrogen LFL (4% vol.) iso-surface at the

end of the release (10 min). It is seen that the size of the cloud is very small in

comparison to the size of the room.

Figure 4. End of 10-min release from the hydrogen vent line: LFL hydrogen

cloud.