American Society of Safety Engineers Middle East Chapter

11th Professional Development Conference & Exhibition

Kingdom of Bahrain, March 16-20, 2014

ASSE-MEC-2014-24

CFD Modeling of Flammable/Hazardous Gas Release and Dispersion

for Risk and Safety Assessments

Vladimir M. Agranat, PhD, President, Sergei V. Zhubrin, PhD, VP R&D

Applied Computational Fluid Dynamics Analysis ( www.acfda.org )

Thornhill, Ontario, Canada

Andrei V. Tchouvelev, PhD, CEO

A.V. Tchouvelev & Associates Inc. ( www.tchouvelev.org )

Mississauga, Ontario, Canada

Zivorad Radonjic, Senior Environmental Meteorologist

ARCADIS SENES Canada Inc. ( www.senes.ca )

Richmond Hill, Ontario, Canada

Abstract

• This presentation is an overview of recent customized applications of general-purpose Computational Fluid Dynamics (CFD) software, PHOENICS [1], to the CFD modeling of flammable/hazardous gas release and dispersion (GRAD) for risk and safety assessments.

• Three application areas are considered: safety analysis of use of flammable gases (hydrogen, methane, etc.), predicting atmospheric pollutant dispersion and modeling two-phase plumes from cooling towers (for air quality assessments).

• CFD models developed and validated are available as user-friendly templates for environmental and safety engineers.

• CFD software sales, training, user support and consulting are provided by ACFDA.

Overview

• Introduction: increasing use of CFD in risk and safety assessments

• CFD modeling approach: governing equations, general-purpose CFD codes (PHOENICS, ANSYS, CFX, etc.) and customization of CFD models for non-standard tasks

• Modeling of flammable gas release and dispersion (GRAD) for safety analysis

• Modeling of atmospheric pollution dispersion for air quality analysis

• Modeling of two-phase plumes from cooling towers for pollutant deposition prediction

• Conclusions

Introduction: increasing use of CFD in risk and safety assessments

• CFD modelling of flammable/hazardous GRAD has been used

extensively over the last 20 years in safety analyses and for air

quality assessments.

• CFD is applied in risk and safety assessments to compliment the

more traditional predictive methods such as AERMOD, CALPUFF

and other models (approved by the US Environmental Protection

Agency (EPA)) in the areas where these models demonstrate

significant deficiencies: near-field dispersion, complex

geometries, two-phase plumes, wide buildings at non-

perpendicular winds, etc. These limitations are described in [3],

[4] and other recent papers.

• CFD is capable of overcoming the above limitations by solving a

comprehensive system of general 3D governing equations

applicable for complex geometries and for multi-phase flows

under specific atmospheric conditions.

Introduction: Increasing use of CFD in risk and safety assessments

• In this presentation, three different CFD models and

corresponding case studies are described:

• advanced models of flammable GRAD for safety analyses (use of

hydrogen, methane, etc.);

• a single-phase plume model for air quality analyses;

• a two-phase multi-group model of drift drop plumes from cooling

towers.

• The models have been validated and applied to real-life industrial

applications.

• They are based on customizing the commercial general-purpose

CFD software, PHOENICS. PHOENICS is used as a framework and

a solver. It has been validated and used for more than 30 years.

CFD modeling approach: governing equations and

general-purpose CFD codes

• A commercial general-purpose CFD software package (PHOENICS, FLUENT, CFX, etc.) is equipped with user-friendly pre-processor (problem definition), solver (solution calculation) and post-processor (solution analysis).

• Governing equations include:

– conservation/transport equations for mass, momentum and energy for each phase

– constitutive equations (linkage between phases)

– turbulence model equations

• Proper boundary conditions are needed for solving the equations.

• Software customizations (specific sub-models) are required in non-standard situations.

• CFD models have to be validated before applying them for risk and safety analyses.

• Adequate training is required for use of CFD models .

GRAD CFD model [2]: prediction of flammable gas clouds

• Modeling of flammable GRAD scenarios is based on general transient 3D conservation equations (gas convection, diffusion and buoyancy) with proper initial and boundary conditions.

• The CFD modeling provides:

– transient behavior of all calculated variables (pressure, gas density, velocity and flammable gas concentration)

– movement of flammable gas clouds with time

– safety evaluation by analyzing a flammable gas concentration iso-surface (lower flammability level (LFL)) and total volumes of flammable gas.

• Three major stages in GRAD modeling:

– steady-state before-the-release simulations

– transient during-the-release simulations

– transient after-the-release simulations .

• PHOENICS software is used as a framework and solver. It

– is commonly used and well validated since 1981

– has user-friendly interface for incorporating sub-models

– has many turbulence models (LVEL, MFM, k-ε variants, etc.).

Flammable GRAD CFD model: governing equations [2]

• 3D momentum equations

• Continuity equation

• Flammable gas mass conservation equation

• Gas mixture density based on flammable gas mass concentration, C, or flammable gas volumetric concentration, α

• Effective viscosity and diffusivity

i=1,2,3 , ) grad ( div ) (

i

i

i eff i i f

x

P u Uu

t

u r rn r

r +

- = - +

0 ) ( div = +

U

t r

r

" ) grad ( div ) (

C C D UC t

C eff

= - +

r r

r

T R C CR

P

air gas ] ) 1 ( [ - + = r

air gas

gas

R C CR

CR

) 1 ( - + = a

t t l l eff t l eff D Pr / Pr / , n n n n n + = + = r n r a n ar n / ] ) 1 ( [ air air gas gas l - + =

Advanced flammable GRAD CFD model features [2]

• Dynamic Boundary Conditions at Release Orifice:

• Real Gas Law Properties:

• Turbulence Model Settings:

, ) ( ) ( ) (

1

1

) 1

2 (

V

A C

0

RT t d

e m A t u t dt

d V t m

-

+

+ -

= - =

g

g

g g

r r

& &

– Transient choked mass flow rate

– Initial choked mass flow rate

– Abel-Noble Equation of State for hydrogen

1 ) 1 (

2

2

2 2

2

- - = = H

H

H H

H d T R

P z

r

r T R d

P z

H H H

2 2

2 1 + =

– NIST data for methane, propane …

– LVEL model, k-ε model, k-ε RNG model, k-ε MMK model and MFM

• Local Adaptive Grid Refinement (LAGR) – Iterative technique, accurate capture of flammable cloud behaviors

near the release location and large gradient regions

1

1

0 0 0 ) 1

2 (

-

+

+ = g

g

g g r P A C m d &

Validation, calibration and enhancement of GRAD CFD module

capabilities for simulation of hydrogen releases and dispersion have

been provided using available experimental databases (see table below).

Case

No.

Validation Case

Name

Conditions

Domain Leak Type Process Available Data

1 Helium jet

Open

Vertical Steady

Velocity,

concentration,

turbulence

intensity

2 H2 jet Horizontal

Transient Concentration

3 INERIS jet Steady Concentration

4 Hallway end

Semi-

enclosed Vertical

Transient Concentration

5 Hallway middle Transient Concentration

6 Garage Transient Concentration

7 H2 vessel Enclosed Transient Concentration

Flammable GRAD CFD model validation matrix

Flammable GRAD CFD model validation case: HYDROGEN SUBSONIC RELASE IN A HALLWAY

• Concentrations at four sensors for 20 min.

duration – Domain: 2.9 m × 0.74 m ×1.22 m

– Grid size: 36 × 10 × 18

– H2 leak rate: 2 SCFM (0.944 m3/s)

– Duration: 20 min

– Concentration: 3 % iso-surface

Door

vent

Hydrogen

inlet

Roof

vent

Published results

Sensor 1

Sensor 2

Sensor 3

Sensor 4

Our results

Flammable GRAD CFD model validation case: HYDROGEN & HELIUM SUBSONIC RELEASE IN A GARAGE WITH A CAR

Garage size: 6.4 m x 3.7 m x 2.8 m

Leak size: 0.1 m x 0.2 m

Two vents: porous material

Car size: 4.88 m x 1.63 m x 1.35 m

Leak rate: 7200 L/hour

Leak direction: downwards

Leak location: bottom of the car

Helium release simulated by using LAGR (local adaptive grid refinement)

Simulations Sensor 1 Sensor 2 Sensor 3 Sensor 4

Swain’s CFD results 0.5% 2.55% 2.55% 1.0%

Initial coarse grid, 32×16×16 1.92% 2.53% 2.52% 1.94%

Adaptive refined, 39×26×24 0.98% 2.66% 2.62% 1.08%

Adaptive refined, 58×26×27 0.79% 2.70% 2.67% 1.01%

Flammable GRAD CFD model application case: RELEASE IN A HYDROGEN GENERATOR ROOM

• Existence of louver and exhaust fan in the

Generator Room creates a steady-state

airflow with 3D fluid flow pattern

Before-the-Release Simulation

Ventilation velocities before release :

During-the-Release Simulation

50% LFL 100% LFL

End of 10-min

release from

the vent line

• GRAD CFD model has been developed, validated and applied for various industrial real-life indoor and outdoor releases of flammable gases (hydrogen, methane, propane, etc.).

• Advanced modeling features:

– Real-life scenarios with complex geometries

– Dynamic release boundary conditions

– Calibrated outlet boundary conditions

– Advanced turbulence models

– Real gas law properties applied at high-pressure releases

– Special output features (separation distances, flammable volume)

– Adaptive computational grid refinement tools.

• Dynamic behaviors of clouds of flammable gas or pollutant could be accurately predicted.

• GRAD CFD model is recommended for safety and environmental protection analyses.

GRAD CFD model: summary

• Over the past two decades, CFD modeling of single-phase plumes has been widely used for air quality analyses in addition to use of AERMOD, CALPUFF and other traditional analytical models as the latter models are insufficient and inaccurate in many cases (wide buildings, near-field regions, two-phase conditions, complex geometries, etc.).

• Studies on comparing these models with CFD predictions are provided in recent papers, e.g. [3] - [5].

• A typical cross-validation case study [5] is presented here to illustrate the benefits of combined use of CALPUFF and CFD.

• In particular, the PHOENICS CFD software was applied in addition to CALPUFF in order to perform the near-field modeling of pollutant dispersion around a backup diesel generating station in Dawson City, Yukon, Canada.

CFD modeling of pollutant dispersion: AIR QUALITY ASSESSMENTS

CFD modeling of pollutant dispersion:

backup diesel generation station in Dawson City

• Geometry of the building housing the generation station and the

stacks located on it is shown below.

• The diesel engines are housed in a low building (blue building

on the left), vented through short, curved exhaust stacks such

that the exhaust gas is released horizontally rather than

vertically.

• The task of CFD modeling is to predict the pollutant cloud

behavior under various meteorological conditions.

CFD modeling of pollutant dispersion:

ground-level pollutant concentrations near power plant

Figure below shows a contour of relative (with respect to a value at the

source) mass fraction of pollutant, C1, predicted by CFD in the worst

case scenario. The power plant is shown in gray and the area of

maximum ground-level pollutant concentrations is shown in red.

CFD modeling of pollutant dispersion:

comparing CFD and CALPUFF predictions

• CFD modeling results are consistent with those derived from the

CALPUFF model, with CFD predicted maximum concentrations being

less than 10% higher than those estimated using the CALPUFF

model.

• However, whereas the CALPUFF model predicted the maximum point

of impingement to occur at the facility property line beside the power

house, the CFD model predicted the maximum concentrations to

occur 10-20 meters from the property line.

• The analysis provides justification for using the CALPUFF model to

represent near-field contaminant concentrations in similar regulatory

applications.

• Additional analysis is required to verify whether the two models

would continue to provide similar results for higher building heights,

higher stacks or more complex building shapes.

CFD modeling of two-phase plumes:

multi-group model of drift drop plume

• Drift drop plumes from industrial cooling towers can impose health

concerns due to deposition of water drops containing a pollutant.

• A new homogeneous two-phase multi-group CFD model was

developed for analyses of water deposition from these plumes.

• PHOENICS was customized to account for the deposition of drops.

• The model was validated based on the high-quality Chalk Point Dye

Tracer Experiment (CPDTE) data described in [3, 6, 7].

• A good agreement with experimental data on water deposition rates

at selected locations from the cooling tower has been observed. It is

better than that reported in [3, 6, 7].

• The details of CFD model and case study are provided below.

CFD modeling of two-phase plumes:

predicting water deposition in CPDTE case study

• Figure below shows the computational domain of Chalk Point Dye

Tracer Experiment (CPDTE), the cooling tower, the ground, the inlet

and the outlet. The probe (a red pencil with yellow end) shows the

location of the first measuring station (at a distance of 500 m from the

cooling tower centerline).

• The experimental data are described in detail in [3, 6, 7].

• The water/sodium deposition fluxes were measured at the sensors

located on the 35° arcs at 500 and 1000 m from the cooling tower

centerline .

• The CFD modeling task was to accurately predict the deposition fluxes.

CFD modeling of two-phase plumes:

features of multi-group model of drift drop plume • Conservation equations for mass fractions of water drops having

different sizes are solved in addition to standard conservation

equations for mixture mass, momentum, energy, water vapor mass

fraction, turbulent kinetic energy and its dissipation rate.

• Phase change effects (condensation, evaporation, solidification and

melting) are assumed to be negligible due to specific conditions of

the experiment (relative humidity of 93%).

• Extra terms are provided to the conservation equations for mass

fractions of liquid water to account for the drift of water drops due to

their gravitational settling.

• Various formulations for drift velocity and terminal velocity have

been tested and compared.

• The droplet-size distribution [6] available at the cooling tower exit

and containing the 25 groups of drops is simplified to 11 groups.

Also, the 3-group and 1-group options are considered for

comparison.

• The total deposition flux is calculated as a sum of products of

individual group mass concentrations of water drops and

corresponding terminal velocities.

CFD modeling of two-phase plumes:

CPDTE case study modeling results

• Figures below show the 1% iso-surfaces of relative mass fractions of

110- and 900-micron drops predicted with the 11-group distribution

model.

• 900-micron drops fall on the

ground at a certain distance from

the cooling tower (less than 500

m).

• 110-micron drops travel above

the ground and leave the domain

without touching the ground.

CFD modeling of two-phase plumes:

comparison of CFD results with CPDTE data

• CFD predictions agree well with the experimental data on the water

vapor plume rise and the total drift deposition fluxes.

• In particular, the plume rise predictions agree well with experimental

values (the errors are from 4% to 33% at different distances from the

tower centerline). Table below shows the detailed comparison

between the CFD predictions of plume rise, the experimental data

and the Briggs’s formula available in [6].

Distance

from

centerline

(m)

Experimental

plume rise

(m)

Briggs’s

formula plume rise

(m)

Plume rise predicted by CFD

(778,050 cells, k-ε model)

(m)

50 30 35 (17%) 40 (33%)

100 50 55 (10%) 48 (-4%)

200 100 87 (-13%) 72 (-28%)

500 N/A 160 121

1000 N/A 254 193

CFD modeling of two-phase plumes:

comparison of CFD results with CPDTE data

• Table below shows the predicted and measured maximum water

deposition fluxes at 500 and 1000 m from tower centerline .

• CFD results obtained with 3-group and 11-group models are provided

for comparison.

• The error factors are shown in red.

Grid Size

(number of

cells)

Location

(m)

Predicted flux

(3 groups)

(kg/s/m2)

Predicted flux

(11 groups)

(kg/s/m2)

Experimental

flux

(kg/s/m2)

212,500 500 1.22E-7 (0.90) 1.37E-7 (1.01) 1.36E-7

212,500 1000 5.72e-8 (1.27) 7.71e-8 (1.71) 4.52E-8

778,050 500 7.96e-8 (0.59) 1.12e-7 (0.82) 1.36E-7

778,050 1000 5.1e-8 (1.13) 6.81e-8 (1.51) 4.52E-8

• The CFD predicted maximum water deposition fluxes are in a

good agreement with the experimental values within a factor

of 1.7, which is well within the industry acceptable error limits

(a factor of 3) [3].

CFD modeling of two-phase plumes:

effect of number of groups on model accuracy

• Figure below shows the comparison of water deposition fluxes

predicted with use of 11-, 3- and 1-group options.

• The 3- and 11-group results are

close to each other and to the

experimental data available at

500 m and 1000 m from the

cooling tower (see table above).

• The 1-group results are close to

the 3- and 11-group results only

at large distances (more than 700

m) and are very different from the

above results at short distances

(smaller than 500 m).

CFD modeling of two-phase plumes:

multi-group model summary

• New two-phase multi-group CFD model was developed and validated

using the experimental data on water deposition fluxes available at

specific distances from a typical cooling tower (CPDTE data).

• The model is recommended for analyzing the drift drop plumes under

the conditions similar to the CPDTE conditions of small Stokes

numbers.

• It is easier to use and not less accurate than the multiphase Eulerian-

Lagrangian CFD models used recently in [3, 7] for modeling the

CPDTE plume.

• The multi-group model has a potential to supplant or complement the

latter in the computational analysis of gravitational phenomena in

engineering equipment and its environment.

Conclusions

• Three CFD models have been developed, validated and

applied to real-life industrial applications:

1. flammable gas release and dispersion model for safety

analyses (use of hydrogen, methane, etc.);

2. single-phase pollutant dispersion model for air quality

analyses;

3. two-phase multi-group model for analyses of drift drop

plumes from cooling towers.

• The models are based on customizing PHOENICS CFD

software, which is used as a framework and a solver.

• User-friendly CFD templates are available for applications by

environmental and safety engineers for risk and safety

assessments.

• More information on PHOENICS licensing, training, user

support and CFD consulting services is available from

info@acfda.org .

Acknowledgements

The authors gratefully acknowledge the financial support of Natural Resources Canada (NRCan) for development and validation of some single-phase GRAD CFD models.

Also, the authors would like to acknowledge helpful communications and information provided by Dr. Steven Hanna (Hanna Consultants), Prof. Robert Meroney (Colorado State University) and Jeffrey Weil (Research Applications Laboratory at the National Center for Atmospheric Research) on the experimental data and modeling results for Chalk Point Dye Tracer Experiment [3,6].

References 1. PHOENICS On-line Information System: www.cham.co.uk/ChmSupport/polis.php.

2. Agranat, V.M., Tchouvelev, A.V., Cheng, Z. and Zhubrin, S.V. (2007). CFD Modeling

of Gas Release and Dispersion: Prediction of Flammable Gas Clouds. Advanced

Combustion and Aerothermal Technologies, Edited by Syred, N. and Khalatov, A.,

Dordrecht, Netherlands: Springer, 179-195.

3. Meroney, R.N. (2006). CFD Prediction of Cooling Tower Drift. Journal of Wind

Engineering and Industrial Aerodynamics, 94 (6), 463-490.

4. Schulman, L.L. and DesAutels, C.G. (2013). Comparing AERMOD and CFD

Downwash Predictions for GEP Stacks. Proceedings of the Air & Waste Management

Association’s Conference Guideline on Air Quality Models: The Path Forward, Raleigh,

North Carolina, USA, March 19-21, 2013, Control # 32.

5. Radonjic, Z., Agranat, V., Telenta, B., Hrebenyk, B., Chambers, D. and Ritchie, T.

(2013). Comparison of Near-field CFD and CALPUFF Modelling Results around a

Backup Diesel Generating Station. Proceedings of the Air & Waste Management

Association’s Conference Guideline on Air Quality Models: The Path Forward, Raleigh,

North Carolina, USA, March 19-21, 2013, Control # 17.

6. Hanna, S.R. (1978), A Simple Drift Deposition Model Applied to the Chalk Point Dye

Tracer Experiment. Environmental Effects of Cooling Tower Plumes, Symposium on,

May 2-4, 1978, U. of Maryland, III-105-III-118.

7. Lucas, M., Martinez, P.J., Ruiz, J., Sanchez-Kaiser, A. and Viedma, A. (2010). On the

Influence of Psychrometric Ambient Conditions on Cooling Tower Drift Deposition.

International Journal of Heat and Mass Transfer 53 (4), 594-604.

Thank you!