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Journal of Loss Prevention in the Process Industries 26 (2013) 117e128

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Journal of Loss Prevention in the Process Industries

journal homepage: www.elsevier .com/locate/ j lp

Computational fluid dynamics analysis of liquefied natural gasdispersion for risk assessment strategies

Biao Sun a,b, Ranjeet P. Utikar a, Vishnu K. Pareek a,*, Kaihua Guo b

aDepartment of Chemical Engineering, Curtin University, GPO Box U1987, Perth, WA 6845, Australiab Engineering School, Sun Yat-Sen University, Guangzhou 510006, China

a r t i c l e i n f o

Article history:Received 15 March 2012Received in revised form7 October 2012Accepted 7 October 2012

Keywords:LNGCFDDense gas dispersionRisk assessmentNFPA 59AImpoundment

* Corresponding author. Tel.: þ61 8 9266 4687; faxE-mail address: v.pareek@curtin.edu.au (V.K. Paree

0950-4230/$ e see front matter � 2012 Elsevier Ltd.http://dx.doi.org/10.1016/j.jlp.2012.10.002

a b s t r a c t

Computational fluid dynamics (CFD) simulations have been conducted for dense gas dispersion of liq-uefied natural gas (LNG). The simulations have taken into account the effects of gravity, time-dependentdownwind and crosswind dispersion, and terrain. Experimental data from the Burro series field tests,and results from integral model (DEGADIS) have been used to assess the validity of simulation results,which were found to compare better with experimental data than the commonly used integral modelDEGADIS. The average relative error in maximum downwind gas concentration between CFD predictionsand experimental data was 19.62%.

The validated CFD model was then used to perform risk assessment for most-likely-spill scenario atLNG stations as described in the standard of NFPA 59A (2009) “Standard for the Production, Storage andHandling of Liquefied Natural Gas”. Simulations were conducted to calculate the gas dispersion behav-iour in the presence of obstacles (dikes walls). Interestingly for spill at a higher elevation, e.g., tank top,the effect of impounding dikes on the affected area was minimal. However, the impoundment zone didaffect the wind velocity field in general, and generated a swirl inside it, which then played an importantfunction in confining the dispersion cloud inside the dike. For most cases, almost 75% of the dispersedvapour was retained inside the impoundment zone. The finding and analysis presented here will providean important tool for designing LNG plant layout and site selection.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Worldwide demand for liquefied natural gas (LNG) hasincreased rapidly in recent years (ExxonMobil, 2008; Kumar et al.,2011) because of expanding economies, growing awareness ofenvironmental protection, and enhanced transportation methodsfor cryogenic tankers (by road, ships, and rail). A great number ofLNG receiving terminals and satellite stations have been plannedand constructed in recent years across many countries (Dorigoni &Portatadino, 2008; Lin, Zhang, & Gu, 2010). Consequently, strongemphasis has been placed on the safety of LNG stations in regardsto site selection, transportation and regasification (Cleaver,Johnson, & Ho, 2007). For example, the exclusive distance fromLNG station to densely populated areas, in the event of LNG spill, isone of the most important factors to be determined in riskassessment. This paper shows application of computational fluiddynamics (CFD) for addressing such issues.

: þ61 8 9266 2681.k).

All rights reserved.

LNG has unique properties, cryogenic temperature, flamma-bility, and vapour dispersion characteristics, compared with otherfuels. Due to its cryogenic boiling point (111.7 K), once it is leakedonto subsoil or water surface, it vaporizes very quickly, withvaporization rate varying between 0.029 and 0.195 kg/(m2 s)(Luketa-Hanlin, 2006; TNO Report, 2005).

At atmospheric conditions, LNG vapour is 1.5 times heavier thanthe ambient air (Koopman & Ermak, 2007), and therefore is calledas a dense or a heavy gas. This manifests special behaviour thataffects its dispersion. The schematic of dispersion process is shownin Fig. 1 (Spicer & Havens, 1989; TNO Report, 2005). LNG dispersionexperiments show three distinct dispersion stages (Spicer &Havens, 1989), namely negative-buoyancy-dominated, stably-stratified and passive dispersion. Once the vapour is released, itdescends to the surface and spreads radially under the influence ofgravity. The dispersed cloud then behaves as a stably-stratifiedcloud embedded in the mean wind flow. Finally, the LNG vapouris diluted as a neutrally buoyant cloud by turbulent air flow(passive dispersion). Since the upper and lower flammable limits(UFL and LFL) of natural gas are respectively 15% and 5% in volume,for the safety consideration, according to NFPA 59A (NFPA, 2009),

Gravity Spreading

Air Entrainment

Vortex

Fig. 1. Gravity spreading of a dense gas cloud.

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128118

the exclusion distance in the downwind direction must be suchthat the average volume concentration of methane is lower than2.5%.

Numerical models for dense gas dispersion are mainly classifiedinto three types (Duijm, Carissimo, Mercer, Bartholome, &Giesbrecht, 1997), phenomenological models, integral models andcomputational fluid dynamics (CFD) models. Phenomenologicalmodels often simplify the physics of the flow and are written bynomograms or simple correlations from the LNG field tests todescribe the dispersion behaviour, for example, the B&M model(Britter, 1988; Duijm et al., 1997). Integral models assumea concentration profile for the downwind gas dispersion, such asGaussian profiles, and then perform the integration along thedownwind distance. Two of the most commonly used integralmodels DEGADIS (Havens, 1998; Spicer & Havens, 1989) and SLAB(Ermak, 1990), are schematically shown in Fig. 2. One of the major

Fig. 2. Schematic diagram of integral dense gas dispersion model.

disadvantages of these two types of models is that they work wellonly if the terrain is relatively flat (Gavelli, Bullister, & Kytomaa,2008). The CFD models solve mass, momentum and energyconservation equations numerically. Contrary to the phenomeno-logical and integral models, CFD models use physically completeterrain description. While CFD models are computationally inten-sive, they include the detailed flow physics and are capable ofmodelling various phenomena adequately.

CFD models have received increasing interest for the simulationof dense gas dispersion behaviour. Sutton, Brandt, and White(1986) were among the first to use CFD code to simulate thedense gas dispersion in the presence of obstacles in a boundary-layer wind tunnel (21.3 m long, 1.18 m wide, 1.68 m high). A two-equation turbulence viscous model, k�ε was employed in theirstudy. Several commercial CFD codes are now available (Coldrick,Lea, & Ivings, 2010) for LNG vapour dispersion simulation. Theseinclude, FEM 3 CFD (Luketa-Hanlin, Koopman, & Ermak, 2007),FLACS (Dharmavaram, Hanna, & Hansen, 2005), Star-CD (Deaves,Gilham, Mitchell, Woodburn, & Shepherd, 2001), FLUENT (Gavelliet al., 2008; Tauseef, Rashtchian, & Abbasi, 2011; Tauseef,Rashtchian, Abbasi, & Abbasi, 2011), and CFX (Cormier, Qi, Yun,Zhang, & Mannan, 2009; Qi, Ng, Cormier, & Mannan, 2010;Sklavounos & Rigas, 2006). Several LNG field tests of 1980s(Cornwell & Pfenning, 1987; Luketa-Hanlin, 2006; Mohan, Panwar,& Singh, 1995) have formed the basis of validating CFD simulation.Application of CFD for simulating LNG vapour cloud dispersion wasstrongly recommended by the Sandia National Laboratories 2004report (Hightower et al., 2004). Sklavounos and Rigas (2006) usedCFX to simulate the Coyote series trials and made a comparisonwith the box-models. However, they used tetrahedral meshing,which for the simple 3D box geometry (600 m long, 250 m wind,50 m high) is less computational efficient than hexahedral (brick)meshes (Gavelli et al., 2008). Luketa-Hanlin et al. (2007) comparedthe FEM 3 codes with Burro series test. MonineObuhkov similaritytheory was applied in their study to describe the vertical behaviourof air flow and turbulence properties within atmospheric surfacelayer. One of Falcon series tests (Brown et al., 1990), Falcon-1, wassimulated by Gavelli et al. (2008) in order to find the effects ofa billboard in the wind field on the vapour cloud dispersionbehaviour. Standard k�ε model together with Reynolds stressmodel (RSM) were applied in their study. The RSM model wascomputationally more costly and hard to convergence compared toother turbulent models. Qi et al. (2010) used CFX code inconjunction with the MonineObukhov similarity theory to simu-late the LNG vapour dispersion of Brayton fire Training Field tests.Tauseef, Rashtchian, and Abbasi (2011), Tauseef, Rashtchian, Abbasi,and Abbasi (2011) also considered the obstacles present in thedownwind direction, comparing with Trial 26 of the Thorney Islandseries tests (Coldrick et al., 2010). Realizable k�ε model was foundto give more accurate results than the standard k�ε mode inmodelling LNG vapour dispersion.

The above simulation studies have been primarily conducted tovalidate CFD models for LNG vapour dispersion. Most of thesesimulations did not consider the transient behaviour of LNG vapouror carry out any practical risk assessment of LNG stations. Thispaper presents a detailed CFD model for LNG dispersion and riskassessment. The commercial code FLUENT has been used to simu-late two typical experiments of the Burro series field test. Thedynamic simulation results were validated with the availableexperimental data and DEGADIS calculations results. NFPA 59A hasbeen used to design the “most likely spill” scenarios in LNG stations.The validated CFD simulation method was then used to carry outthe risk assessment under the designed spill scenario, in order tofind the mitigation effect of the impoundments, and reduction ofexclusive distance.

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128 119

2. Theoretical background

Present simulationswere carried out using commercial softwareFLUENT 12 (ANSYS, 2010), which utilizes the Finite VolumeMethod(FVM) (Versteeg & Malalasekera, 1995) to discretize the computa-tional domain and equations, combined with Reynolds averagedNaviereStokes (RANS) equations and Reynolds stressmodels (RSM)for calculating the processes of momentum. In this study, theequations involve continuity, energy, momentum, turbulencemodel and species model (ANSYS, 2010). These equations are givenbelow.

Continuity equation

vr

vtþ V$ðr v!Þ ¼ 0 (1)

where r is the density of dense gas cloud, v! is velocity vector ofthree dimension.

Momentum equation

vðr v!Þvt

þ V$ðr v! v!Þ ¼ �Vpþ V$ðsÞ þ r g!þ F!

(2)

s ¼ m

��V v!þ V v!T

�� 23V$ v!I

�(3)

where p is pressure, s is stress tensor, F!

is the sum of body forces,r g!means gravity works on dense gas cloud in vertical direction, mis dynamic viscosity.

Energy equation

vðrEÞvt

þV$ð v!ðrEþ pÞÞ ¼ V$

0@keffVT �

Xj

hj Jj!þðseff$ v!Þ

1Aþ Sh

(4)

E ¼ h� prþ v2

2(5)

where E is total energy, keff is effective conductivity, Sh is energysource (if chemical reaction exist).

The choice of turbulence model is a key in dispersion simulationusing CFD codes. In this study, realizable k�ε model (Shih, Liou,Shabbir, Yang, & Zhu, 1995) is applied, because this modelsatisfies certainmathematical constraints on Reynolds stress, and isconsistent with the physics of turbulent flows. Besides, this modelexhibits an excellent performance in capturing the phenomenon ofgravity slumping associated with dense gas dispersion, and issuperior to the standard k�ε viscosity model (Shih et al., 1995). Ithas been reported that realizable k�ε predicts the spatial andtemporal concentration profile of the vapour cloud in the presenceof obstacles more accurately than the hitherto oft-used standardk�ε model (Launder & Spalding, 1972; Tauseef, Rashtchian, &Abbasi, 2011; Tauseef, Rashtchian, Abbasi, & Abbasi, 2011). Realiz-able k�ε model differs from the standard k�ε model in two ways.

1) The realizable k�ε model contains a new formulation for theturbulent viscosity and a new eddy-viscosity formula involvinga variable Cm, shown in Equation (6), whereas Cm is a constantand equals 0.09 in standard k�ε and RNG k�ε model.

2) A new transport equation for the dissipation rate ε has beenderived from an exact equation for the transport of the mean-square vorticity fluctuation, shown in Equation (8).

mt ¼ rCmk2

ε

(6)

vðrkÞvt

þv�rkuj

�vxj

¼ v

vxj

"�mþmt

sk

vkvxj

#þGkþGb�rε�YMþSk (7)

vðrεÞvt

þ v�rεuj

�vxj

¼ v

vxj

"�mþ mt

sε

vε

vxj

#þ rC1Sε� rC2

ε2

kþ ffiffiffiffiffivε

p

þ C1εε

kC3εGb þ Sε (8)

where mt turbulence viscosity, k is kinetic energy, ε is turbulenceeddy dissipation, Gk and Gb are the turbulence production due toviscous and buoyancy forces, Sk and Sε are the source term for k andε.

An additional transport equation is needed to calculate thevolume concentration of natural gas, because of the involvement ofthe multi-component flow. Species transport equations areconsidered in the following form.

v

vxðrYiÞ þ V$ðr v!YiÞ ¼ �V$ Jj

!þ Ri þ Si (9)

Jj! ¼ �

�rDi;m þ mt

Sct

VYi � DT;i

VTT

(10)

where Yi is Mass fraction of species i, Jj!

is Mass diffusion, Ri is Netrate of production of species, Si is the source term, Di,m is massdiffusion coefficient.

3. The Burro series test

The Burro tests were performed by the Lawrence LivermoreNational Laboratory (LLNL) at the Naval Weapons Center, ChinaLake, California in 1980, and sponsored by the US DOE and the GasResearch Institute (Ermak, Chan, Morgan, & Morris, 1982; Koopmanet al., 1982a, 1982b; Koopman, Cederwall, et al., 1982). The mainpurpose was to obtain adequate samples of data under differentmeteorological conditions. These experiments involved eight spillsof LNG and one of liquid nitrogen. A total of eight LNG spills ontowater surface were performed with spill volumes ranging from24 m3 to 39 m3, spill rates from 11.3 m3/min to 18.4 m3/min, windspeeds from 1.8m/s to 9.1m/s, and atmospheric stability conditionsbeing fromunstable (Class B) to slightly stable (Class E).Water pondand instrument array (Koopman et al., 1982a, 1982b; Koopman,Cederwall, et al., 1982) are shown in Fig. 3. There were 25 gassensor stations were placed in arcs in the downwind distance 57 m,140 m, 400 m and 800 m. Meanwhile, 6 turbulence stations and 20wind field anemometer stations were arranged in both upwind anddownwind directions. The centreline of instrument array wasoriented at 225� from the southwest, in order to coincide with theprevailing wind direction. Atmospheric conditions of Burro seriestests are listed in Table 1 (Koopman et al., 1982a, 1982b; Koopman,Cederwall, et al., 1982). In this study, Burro 5 and Burro 8 (B5 andB8) tests have been selected to validate the simulations, because ofthe longest spill duration (190 s) in B5 test and the most stableatmospheric conditions (atmospheric stability E) in B8 test.

4. Simulation approach

The computational domain and mesh are shown in Fig. 4.X-direction has been assumed to be horizontal and parallel to thewind, from 100 m upwind to 900 m downwind. Y-direction was

Fig. 4. Computational domain and boundaries (a) and hexahedral mesh refined nearthe ground surface (b).

Fig. 3. Water pond (a) and instrumentation array (b) in Burro series dispersion tests(overhead view).

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128120

horizontal and perpendicular to the wind, y ¼ 500 m. Moreover,z-direction was vertical, z ¼ 50 m. The original point was placed atthe ground level in the centre of thewater pond. The computationaldomain was discretised in hexahedral cells, which are much morecomputationally efficient than tetrahedral meshes. In the massinlet area, O-grid was applied in order to improve the mesh quality.Overall, the mesh contained 420,080 cells inside the computationaldomain.

The density of vapour cloud was calculated using the ideal gaslaw. In order to judge whether the vapour was a dense gas or not,

Table 1Initial condition of Burro LNG dispersion series tests.

Trial No. B2 B3 B4 B5 B6 B7 B8 B9

Liquid pool diameter (m) 58 58 58 58 58 58 58 58Spill volume (m3) 34.3 34.0 35.3 35.8 27.5 39.4 28.4 24.2Spill duration (s) 173 167 175 190 128.9 174 107 79Spill rate (m3/min) 11.9 12.2 12.1 11.3 12.0 13.6 16.0 18.4Wind speed (m/s) 5.4 5.4 9.0 7.4 9.1 8.4 1.8 5.7Relative humidity (%) 7.1 5.2 2.7 5.6 5.1 5.6 4.5 13.1Atmospheric

temperature (�C)37.6 33.8 35.4 40.5 39.2 33.7 33.1 35.4

Atmospheric stability B B C C C D E D

themodified Richardson number (Britter,1989), Rim, was applied asa criterion, expressed in Equation (11). g is gravity (9.81m/s2). r andra are density of vapour cloud and air (kg/m3), respectively. Q0 isLNG spill rate (m3/s). D is characteristic length of LNG vapour cloud,and U0 is wind velocity (m/s). If Rim � 0.15, the vapour cloudbehaves as dense gas. In this study, Rim¼ 0.18 for B5 test, Rim¼ 0.78for B8 test, hence the gravity-induced effect must be taken intoconsideration.

Rim ¼

�gr� rara

Q0

D

13

U0� 0:15 (11)

4.1. Boundary conditions

At the air velocity inlet, a vertical velocity profile was applied asdescribed by Equation (12), where U(z) means the wind velocity atthe height of Z, U0 is wind velocity, z0 is the maximum height. Thepower-law exponent l depends upon the ground surface roughnessand the atmospheric stability. The exponent value can be analysedthrough MonineObukhov similarity theory (Arys, 1999). Theequations for non-dimensional wind shear are shown in Equations(13)e(16). A value l ¼ 0.007 was applied in this study, whichcorresponds to “slightly unstable” in class “C” in PasquilleGiffordatmospheric stability (Essa, Embaby, & Etman, 2003; Mohan &Siddiqui, 1998; Pontiggia, Derudi, Busini, & Rota, 2009).

UðzÞ ¼ U0

�zz0

l

(12)

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128 121

Non-dimensional wind shear

Fm ¼�KZU*

�vUvz

(13)

where Fm is calculated differently upon different atmosphericconditions, described as flows: For unstable conditions

Fm ¼�1� 15Z

L

�0:25

(14)

For stable conditions

Fm ¼ 1þ 4:7ZL

(15)

For neutral conditions

Fm ¼ 1 (16)

LNG evaporates very quickly from the water pond area. The heattransfer between LNG and water is due to convection flow in thewater, with very minor quantities of ice formed (Opschoor, 1980).The published LNG vaporization rate for leaking on water surfacevaries between 0.029 and 0.195 kg/(m2 s) (Luketa-Hanlin, 2006).Since LNG spill rates are 0.032 and 0.045 kg/(m2 s) in B5 test and B8test, therefore, this paper uses the spill rate as mass flow rate inboundary conditions. The outlet boundary condition was set aspressure outlet, since flow and energy field were not known at thisboundary. A fully-developed flow was assumed at this boundary.

A wall boundary condition was applied to ground surface, sinceno flow or energy exchanging occurs in this boundary. For side andtop surfaces, zero gradients of flow, energy and species variables

Fig. 5. FLUENT simulation of vertical volume frac

were assumed, because these surfaces are far enough from themass flow area.

4.2. Solution method

Prior to injection of LNG, a steady-state flow field was calculated.Once a converged solution was obtained for steady state flow field,the time-dependent simulations were performed using the steady-state as the initial condition. At time t ¼ 0 s, the injection ofnatural gas was switched on and the volume concentration ofnatural gas was obtained as a function of time. For the transientvapour dispersion simulation, the residuals convergence criterionwas set as 1.0 � 10�4, and the time step to 0.5 s. Approximately 20iterations per time step were required to reach the limited residuals.The total execution time of each transient simulation was about 5 hon a 2.93 GHz Intel� Core TM i7 processor with 8.00 GB RAM.

5. Results and discussion

5.1. Crosswind dispersion

Fig. 5 shows vertical concentration profiles at a downwinddistance of 57 m at different times after the LNG spill, 20 s, 70 s and130 s, respectively. It compares the lateral dispersion distancebetween experiment and simulation results. At the initial stage,negative-buoyancy was dominated, because of heavier density ofLNG vapour than ambient air. The width of vapour cloud was widerthan the source diameter under the effect of gravity leading to lateralspread. From 20 s to 130 s, lateral spreading distance increasedfarther. It is obvious that the vapour cloud was driven by the gravityand it moved laterally in both experiment and simulations as shownin Fig. 5. This demonstrates that the CFD simulations were in a goodagreement with the theory of dense gas dispersion and field

tion contours at 1 m height for Burro 5 test.

Fig. 6. CFD prediction of horizontal gas concentration profile at 1 m height for B5 test experiment. (a) Experiment (b) simulation (c) comparison.

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128122

experiment. In B5 test, iso-concentration contour 1% (volumeconcentration) could cover the lateral distance as far as 76m in 130 s,with the source radius of 29 m. However, the simulation predicteda higher spreading distance of about 110 m. This was due to atmo-spheric turbulence, and the boundary conditions applied (Koopmanet al., 1982a, 1982b; Koopman, Cederwall, et al., 1982). While, in thefield tests, the wind speed and the wind direction were non-uniform, in the simulation, the wind speed and its direction wereassumed to be uniform based upon the average wind speed and theprevailing wind direction of experiments.

5.2. Horizontal dispersion

The most valuable data for model validation are considered tobe the gas concentration plume parameters (Luketa-Hanlin et al.,2007). The four recommended values by Ermak, Chapman,Goldwire, Gouveia, and Rodean (1988) are maximum gas concen-tration, average ground-level plume centreline concentration,plume half-width and plume height, all as a function of downwinddistance. Fig. 5 shows the plume height and spread in a timedependent manner.

Fig. 7. CFD prediction of horizontal gas concentration profile at 1 m height for B8 test. (a) Experiment (b) simulation.

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128 123

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128124

In the B5 test, spill duration was 190 s, with a constant spill rate11.3 m3/min. According to the experimental results, steady statereached at about 90 s. The simulations achieved the steady state ataround 80 s and the dimension of the vapour cloud did not changesignificantly afterwards. Fig. 6 illustrates the horizontal iso-concentration contour at 1 m height level in the downwind direc-tion and compares with the experimental data with simulationresults at 20 s, 50 s, 90 s and 190 s. The experimental data are shownin Fig. 6(a); the simulation results are in Fig. 6(b) and comparison isshown in Fig. 6(c). As the time elapsed, the gas cloud grew bigger inboth downwind and crosswind direction. Lower volume concen-tration in the downwind direction and larger covered area by thedispersion cloud was also observed. The CFD simulations were ableto realistically capture these features. A very good quantitativeagreement was observed between the CFD simulations and theexperimental data for vapour cloud dimensions, especially as thesteady state was approached with an average relative error of60.30% compared with experiment as shown in Fig. 6(c). Thegravity-induced effect can also be identified in Fig. 6 with the cloudlateral distance wider than the source dimension. Because of the

Fig. 9. Computational domain of case study, and wind profile before LNG releasing. (a)Geometry (b) mesh (c) wind profile.

a

b

Fig. 8. Comparison of steady-state results for experiment results, FLUENT simulationand DEGADIS. (a) B5 test (b) B8 test.

Table 2Design spill in NFPA 59A (2009).

Design spill source Design spill criteria Design spill rate and volume

Containers withpenetrations belowthe liquid levelwithout internalshutoff valves

A spill through an assumed opening at,and equal in area to,that penetration below the liquid levelresulting in the largest flow an initiallyfull containerIf more than one container in theimpounding area. Use the container withthe largest flow.

Use the following formula: q ¼ 43d2

ffiffiffih

p

Until the differential head acting on the opening is 0.For SI units, use the following formula:

q ¼ 1:0610;000

d2ffiffiffih

p

Until the differential head acting on the opening is 0.

Containers withpenetrations belowthe liquid level withinternal shutoffvalves.

The flow through an assumed opening at,and equal in area to, that penetration belowthe liquid level that could result in thelargest flow from an initially full container.

Use the following formula: q ¼ 43d2

ffiffiffih

p

For SI units, use the following formula:

q ¼ 1:0610;000

d2ffiffiffih

pfor 10 min.

Note: q ¼ flow rate [ft3/min, (m3/min)] of liquid; d ¼ diameter [in, (m)] of tank penetration below the liquid level, h ¼ height [ft, (m)] of liquid above penetration in thecontainer when the container is full.

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128 125

changes in the wind direction during the execution of experiments,the plume shape deviates from the centreline by a small angle(within 5�), especially around the cloud front.

Fig. 7 shows the simulation results of B8 test under steady-statedispersion. Because of the high wind speed in B5 test, with anaverage of 7.4 m/s, downwind dispersion distance was as much as350m. It is clear that the CFD simulations qualitatively captured thevapour cloud. Under the more stable atmospheric conditions in B8test, the downwind dispersion distance was much shorter and thedense gas cloud also moved upwind a little, but the crosswinddispersion was much wider as shown in Fig. 7. Furthermore, in therelatively more stable atmospheric conditions, it was expected totake longer time to reach the steady-state dispersion. In B8 test, thesteady-state dispersion reached at 140 s approximately, and thespill duration was 107 s.

Fig. 8 compares the predicted maximum downwind gasconcentration profiles of DEGADIS and CFDwith experimental data.The relative errors of DEGADIS and FLUENT in comparison withexperimental data were 42.58% and 19.62% respectively, showingan excellent consistency of the CFD simulations even when theatmospheric conditions are changed. DEGADIS predicts the gasdispersion consistently with experimental data, but when dealingwith stable conditions, it overestimates the maximum downwindgas concentration. More importantly, DEGADIS cannot deal withthe case having complicated terrain. This is due to the concentra-tion assumption of Gaussian profile in the vapour cloud, whichcannot take into consideration of the non-flat terrain effects.

Fig. 10. Time-depended vapour cloud footprint (volume concentrat

6. Risk assessment

From the comparison with Burro tests, it is clear that CFDsimulations give more accurate prediction on temporal and spatialconcentration profiles of dense gas dispersion compared to othermodels, such as DEGADIS. Therefore, CFD simulation can be effec-tively used for risk assessment in LNG station siting, pre-accidentpredicting and post-accident analysis.

NFPA 59A (2009 Edition) regulates that impoundments arerequired for LNG tanks or containers for leak control. The minimumvolumetric liquid holding capacity must be 110% of the maximumtank capacity (Raj & Lemoff, 2009). Once LNG spills, the dike wouldprevent the liquid from spreading. For LNG gas dispersion, thevapour cloud will be confined inside the impoundments because ofthe dense gas effect. Exclusion zones must be built at the point ofvolume concentration of 2.5% (half of the LFL).

In this study, the container capacity was assumed to be 2000m3

(7 m radius and 13 m high), and the impoundment dimension was28 m long, 28 m wide and 3 m high. Geometry and mesh for thiscase are shown in Fig. 9. The CFD simulation method used in BurroLNG dispersion was applied for this case, and the boundaryconditions were set according to NFPA 59A (atmospheric stability F,wind speed 2 m/s). Wind direction was parallel to the x-axis inorder to predict the maximum dispersion distance. As calculated inTable 2, the spill rate was 18.5 kg/s (0.12 kg/(m2 s)) from the tanktop surface according to the Design Spill in NFPA 59A and the spillduration was 200 s.

ion contour 2.5%) in the impoundment area at different time.

Fig. 11. Time-depended vapour cloud footprint (volume concentration contour 2.5%) at different time (spill duration ¼ 200 s).

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128126

6.1. LNG spill with impoundment

The tank impoundment area plays an important role during LNGspills. Liquid LNG and vaporized gas will be confined inside thisarea. In the presence of impoundment walls, swirl and recirculationwill be formed inside the impoundment area as shown in Fig. 9(right). Because of this kind of effect, when LNG spills, vapourcloud will be dragged upwind. Fig. 10 shows the footprint of iso-concentration contour of 2.5% at time stages in the dispersion,from the beginning of spill to the end of spill. In the initial stage,because of the dense gas effect, the vapour cloud falls down alongthe tank outer wall into the impoundment area. Then, the cloudconfined in the area moves downwind over the front wall. Mean-while, the cloud is dragged backward because of the wind swirlinside the impoundment area. In the end, the impoundment isalmost full of vapour cloud. At T ¼ 100 s, the vapour dispersionstarts at steady-state, and the dispersion behaviour does notchange until the end of spill. Downwind distance and lateraldispersion distance for iso-concentration 2.5% are 35 m and 75 m,respectively. Almost 75% of the total dispersed vapour was confinedinside the impoundment area.

6.2. LNG spill without impoundment

Fig. 11 shows the spill case without the impoundment.Compared to Fig. 10, there was a subtle change in the gas dispersionbehaviour. In this case, the steady-state dispersion reached muchearlier at 60 s. At the steady-state, for iso-concentration of 2.5%, thedownwind distance and lateral distance were 39.5 m and 63 m,respectively. Thus the gas travelled 11% more in the downwinddirection whereas the spread was 16% less.

The comparison of the two cases shows impoundment is quiteimportant in controlling the effects of LNG spills. Within theimpoundment area, the wind swirl helps to drag the vapour cloudupwind, and the dispersion in downwind direction is weakened.Meanwhile, the impoundment walls strengthen the air flowturbulence. It therefore, takes a longer time to arrive at the steadystate dispersion, 100 s compared with 60 s of the case without theimpoundment. Thus, LNG spill can be controlled more easily withan impoundment.

However, further research is needed to evaluate the effect ofimpoundment on the LNG evaporation rate. When liquid LNG spillsat the tank bottom and is confined inside the dike, the main heattransfer to the cryogenic liquid is due to heat conduction from thedike wall and the impoundment subsoil. At initial stage, the LNGevaporation rate can be very high at around 0.19 kg/(m2 s). As

time elapses, it would decrease to a very low value (less than0.05 kg/(m2 s)) (ioMosaic, 2007). If LNG spills without any dike,then the liquid would flow freely without resistance. Evaporationwould bemore severe, and an even larger areawould be consideredhazardous.

7. Conclusion

The dispersion of LNG vapour has been simulated usingcomputational fluid dynamics (CFD). Simulation results werecomparedwith the experimental results from Burro series tests andwith integral model (DEGADIS). Compared to the integral models,CFD simulations were in a much better agreement with theexperimental data, especially for the downwind dispersion. TheCFD simulations are also advantageous when dealing withtemporal and spatial dense gas dispersion, including threedimensional analyses, turbulence modelling, gravity slumpingeffect, complicated terrain and time depended effect. In theprediction of downwind maximum concentration, relative errorbetween CFD simulations and experimental data was 19.62%. TheCFD model also took longer time to arrive at the steady statecompared to experimental results. These can be attributed toaveraging of the boundary conditions over the period of test. It wasalso observed that stable atmospheric conditions take longer timeto arrive to the steady-state compared to the less stable conditions.The influencing area was also larger in the later case.

Simulations were then used for industrial risk assessment ofLNG spill as defined by NFPA 59A in order to determine theexclusion distance from the most-likely-spill scenario. Dense gasdispersion scenario with or without impoundment was examined.In case of an impoundment, it was found that the dense vapour wascollected inside the impoundment area increasing the time toachieve the steady state, which was quite helpful in confining theliquid or vapour from spreading. Impoundment would also reducethe evaporation rate to a very low amount, compared with thescenario of without impoundment. These results and analysis areimportant in evaluating and designing LNG regasification terminalsand LNG station sitting.

Acknowledgement

This work was supported from the Key Laboratory of LNGCryogenic Technology of Guangdong High Education Institute (No.39000-3211101), the SYSU-BP LNG Centre (No. 99103-9390001)and Key Laboratory of Fire Science and Technology of Guangdong

B. Sun et al. / Journal of Loss Prevention in the Process Industries 26 (2013) 117e128 127

Province (No.2010A060801010). Author Sun also acknowledges thesupport received from the Australia-China council scholarship.

Nomenclature

b(x) Homogeneous characteristic half width of gas plume inDEGADIS model (m)

c(x, y, z) Local concentration in DEGADIS model (kg/m3)cc(x) Centreline concentration in DEGADIS model (kg/m3)C1 ConstantC2 ConstantC1ε ConstantC3ε ConstantCm Turbulence model constantD Characteristic dimension of the source (m)Di,m Mass diffusion coefficient (m2/s)DT,i Turbulent diffusivityE Total energy (J)F!

Force vector from source terms (N)g! Gravitational acceleration (m/s2)Gk Generation of turbulence kinetic energy due to mean

velocity gradients (kg/(m s3))Gb Generation of turbulence kinetic energy due to buoyancy

(kg/(m s3))h Cloud height in SLAB model (m)hj Enthalpy of species j (J/kg)I Unit tensor (1/s)Jj!

Mass diffusion (kg/(m2 s))k Turbulence kinetic energy (m2/s2)keff Effective conductivity (W/(m K)L Cloud half width (m)p Pressure (pa)Q0 Total vapour volumetric flow rate (m3/s)Ri Net rate of production of species i (kg/(m3 s))Rim Modified Richardson numberSct Turbulent Schmidt numberSh Energy source (including chemical reaction)Si User defined source (kg/(m3 s))Sk User-defined source terms of turbulence kinetic energySm Mass added or any user-defined sources (kg/(m3 s))Sy(x) Horizontal concentration scaling parameter in DEGADIS

model (m)Sz(x) Vertical concentration scaling parameter in DEGADIS

model (m)Sε User-defined source terms of turbulence dissipation ratet Time (s)T Temperature (K)U0, Ua Ambient air velocity (m/s)Ut, Ue Horizontal and vertical entrainment rates (m/s)Ux Wind velocity along z-direction in DEGADIS model (m/s)U(z) Wind speed at a given elevation (m/s)v Kinematic viscosity (m2/s)v! Overall velocity vector (m/s)x, y, z Elevation along downwind, crosswind and vertical

direction (m)YM Fluctuating dilatation in compressible turbulence to the

overall dissipation rate (kg/(m s3))Yi Mass fraction of species iz0 Height for detecting weed speed (8 m in this paper) (m/s)

Greek lettersa Constant in power law wind profile in DEGADIS modelε Turbulent dissipation rate (m2/s3)

l Dimensionless parameter depended upon atmosphericstability and surface roughness

m Dynamic viscosity (Pa s)mt Turbulence viscosity (Pa s)r Density of vapour (kg/m3)ra Density of ambient air (kg/m3)sk, sε Turbulent Prandtl numbers for k and ε

s Stress tensor (kg/(m s2)

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