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Atmospheric Environment

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Dense gas dispersion model development and testing for the Jack Rabbit IIphase 1 chlorine release experiments

Simon Ganta,∗, Jeffrey Weilb, Luca Delle Monacheb, Bryan McKennaa, Maria M. Garciaa,Graham Tickled, Harvey Tuckerc, James Stewarta, Adrian Kelseya, Alison McGillivraya,Rachel Batta, Henk Witloxe, Mike Wardmana

aHealth and Safety Executive (HSE), Harpur Hill, Buxton, SK17 9JN, UKbNational Center for Atmospheric Research (NCAR), PO Box 3000, Boulder, CO 80307-3000, USAcHealth and Safety Executive (HSE), Redgrave Court, Merton Rd, Bootle, L20 7HS, UKdGT Science & Software, 29 Mount Way, Waverton, Chester, CH3 7QF, UKe DNV-GL, Vivo Building, 30 Stamford Street, London, SE1 9LQ, UK

A R T I C L E I N F O

Keywords:Jack Rabbit IIChlorineDispersionModellingValidation

A B S T R A C T

In 2015 and 2016, the US Army conducted a series of large-scale chlorine releases at the Dugway ProvingGround in Utah, known as the Jack Rabbit II trials. The purpose of these experiments was to improve ourunderstanding of pressure-liquefied chlorine releases and atmospheric dispersion, and provide useful practicalknowledge for emergency responders. The tests conducted in 2015 featured a grid of Conex shipping containersaround the release point to simulate a mock urban array of buildings, while the tests in 2016 studied differentrelease orientations and culminated in the full discharge of a 20-ton chlorine road tanker.

Before, during and after these experiments, a group of dispersion modelling experts from around the worldcollaborated in simulating the tests to help configure the experiments and evaluate the performance of models.This paper presents the progress made in that modelling activity by two of the groups involved: the UK Healthand Safety Executive (HSE) and the US National Center for Atmospheric Research (NCAR). The models tested byHSE and NCAR range in complexity from integral models that can be run quickly for emergency response toComputational Fluid Dynamics (CFD) models that require days of computing time. This paper summarizes thesegroups' analysis of the 2015 experimental data and initial model findings, and outlines future research direc-tions.

The discharge model predictions by HSE show that meta-stable models tended to over-predict the measuredrelease rate from the chlorine tank, whilst flashing models under-predicted the release rate. CFD simulations ofthe near-field flow behavior show that the chlorine cloud was initially directed laterally, out of the sides of themock urban array, due to the alignment of Conex containers. The two integral models tested by HSE (DRIFT andPHAST) provide best agreement with the downwind concentration data when they take into account the rainoutof liquid from the impinging two-phase jet. The two models tend to over-predict concentrations slightly, butmany of the measurements may have under-recorded the peak concentrations, due to sensors saturating and theclouds bypassing sensors. The newly-developed NCAR integral model accounts for the transition from mo-mentum to buoyancy-dominated behavior and shows promising agreement with the CFD results and measuredconcentrations. In addition, it reproduces the observed −5/3 power law decay in concentration at large dis-tances.

Efforts are continuing on analysis of the Jack Rabbit II experiments and a collaborative international modelinter-comparison exercise is currently underway. Additional papers by the coordinators of that exercise andother experts should be forthcoming. The Jack Rabbit II dataset will no doubt be used for decades to come as aseminal test case for validating flashing jet and dense gas dispersion models.

https://doi.org/10.1016/j.atmosenv.2018.08.009Received 3 April 2018; Received in revised form 2 August 2018; Accepted 5 August 2018

∗ Corresponding author.E-mail address: simon.gant@hse.gov.uk (S. Gant).

Atmospheric Environment 192 (2018) 218–240

Available online 09 August 20181352-2310/ Crown Copyright © 2018 Published by Elsevier Ltd. This is an open access article under the OGL license (http://creativecommons.org/licenses/OGL/3.0/).

T

1. Introduction

In 2008, a study was published by Hanna et al. (2008) that ex-amined three large chlorine railcar incidents in the US and comparedthe casualty figures to the results produced by six widely-used atmo-spheric dispersion models (ALOHA, HGSYSTEM, SLAB, SCIPUFF,PHAST, and TRACE). The work showed that all of the models con-sistently over-predicted the number of casualties in the incidents, by anorder of magnitude or more (Hanna and Chang, 2008). Hanna et al.(Hanna et al., 2008; Hanna and Chang, 2008) identified a number ofuncertainties in the models that could be responsible for the errors.These included the discharge models, the effect of liquid rainout andpool formation, the atmospheric dispersion models, the effect of terrainand chemical reactions (including photolysis), dry deposition, and thetoxic effects models.

In response to that work and to concerns raised by US Congressionalleaders about the risks due to Toxic Inhalation Hazard chemicals (TIHs)from accidental or terrorist-related railcar releases in metropolitanareas,1 the Chemical Security and Analysis Center (CSAC) at the USDepartment of Homeland Security (DHS) initiated a program of ex-periments, starting with the Jack Rabbit I trials in 2010 and continuingwith the Jack Rabbit II trials in 2015 and 2016 (Fox and Storwold,2011; Fox et al., 2016, 2017). The work was principally funded by DHSand US Defense Threat Reduction Agency (DTRA), Transport Canadaand Defence Research and Development Canada (DFDC), with sig-nificant contributions from the US Pipelines and Hazardous MaterialsSafety Administration (PHMSA) and the US Transport Safety Adminis-tration (TSA). A host of other organizations contributed to the projectwith both equipment and effort in kind.

The first set of trials (Jack Rabbit I) took place in April and May2010 at the Dugway Proving Ground, Utah, US. They involved 1 and 2ton releases of pressure-liquefied ammonia and chlorine that were di-rected downwards into a shallow depression that was 50m in diameterand 2m in depth. Various studies have been published on the findings(Hanna et al., 2012, 2016a), but the data from these tests have not beenreleased publicly.

The later Jack Rabbit II experiments, which are the focus of thepresent paper, were also conducted at Dugway Proving Ground in twophases. In the first phase, five tests were conducted in August andSeptember 2015 which consisted of 5–9 ton releases of chlorine from aspecially-designed 10-ton vessel. The release mechanism involved aflange on the underside of the tank fitted with a blanking plate held onby explosive bolts, which were fired to remove the plate and initiate thedischarge. In all five of the tests in 2015, the jet was directed verticallydownwards through a 6-inch (0.152m) diameter orifice onto a concretepad from a height of 1m. A grid of Conex shipping containers wasplaced around the release point to simulate an urban array of buildings(Fig. 1). Concentrations were measured in arcs downwind from therelease point at various distances out to 11 km.

The second phase of experiments in August and September 2016was conducted without the grid of Conex containers and with severaldifferent release orientations. The first three tests involved the jet beingangled either vertically downwards (180°), 45° downwards from hor-izontal (135°) or vertically upwards (0°). In all three tests, the same 10-ton vessel was used as in the earlier 2015 tests. The final test in 2016involved an explosive charge being used to cut a 6-inch hole in theunderside of a 20-ton chlorine road tanker to produce a downwards-directed jet.

The Jack Rabbit II dataset can be requested by emailing: Jack.Rabbit@st.dhs.gov, and a selection of photos and videos taken by Utah

Valley University can be found on their project website.2

Prior to the Jack Rabbit II experiments, various modelling teamswere invited to participate in the Modelers Working Group (MWG),which was coordinated by CSAC and DTRA. The purpose of the MWGwas to help design the experiments, to provide model predictions tohelp position sensors, to analyze the data, validate models and sharefindings.

The UK Health and Safety Executive (HSE) joined the MWG in April2015. The principal motivation for HSE's participation in the JackRabbit II project was to validate the DRIFT dispersion model used byHSE for its regulatory work in the UK, which includes providing publicsafety advice on risks around major hazards sites and assessing safetyreports prepared by operators under the UK Control of Major AccidentHazards (COMAH) regulations. In addition to validating DRIFT, HSEproduced simulations for the MWG using the DNV-GL PHAST software,which is a popular model used by industry and consultants for assessingmajor accident hazards. HSE also produced some Computational FluidDynamics (CFD) simulations to help understand the near-field flowbehavior.

The purpose of NCAR's MWG involvement was to support DTRA inits efforts to understand, analyze, and predict the behavior of dense gasreleases and toxic clouds, to develop and test a new integral model fordense gas dispersion, and ultimately to link the integral model ap-proach with a Lagrangian particle model (Weil et al., 2004, 2012) forprobabilistic concentration predictions.

This paper describes the progress made by HSE and NCAR in ana-lyzing the Jack Rabbit II experiments over the last two years. A range ofdifferent dispersion models have been developed and tested over thatperiod to help understand the complex dispersion behavior. The aim ofthe current paper is to disseminate the findings of this recent work andto document the strengths and weaknesses of the different modellingapproaches. Recommendations are also given for future work, whichincludes combined modelling approaches, where results from thecomplex CFD model developed by HSE could be used to help developthe integral model produced by NCAR.

The arrangement of this paper is as follows. Details of the HSE andNCAR models are given in Section 2. The Jack Rabbit II data from the2015 tests are then reviewed in Section 3 and model predictions arecompared to this data in Section 4. Results are discussed in Section 5,together with a short description of future research plans. Conclusionsare then given in Section 6.

2. Dispersion models

2.1. DRIFT

DRIFT is a commercially-available integral dispersion model pro-duced by ESR Technology that is used by HSE for its regulatory work inthe UK. The model originates from the Safety and Reliability

Fig. 1. Photo showing the dispersing chlorine cloud 5 s after the start of therelease in Jack Rabbit II 2015 Trial 1 (© CSAC, DHS).

1 See https://www.dhs.gov/science-and-technology/csac and https://archive.org/stream/gov.gpo.fdsys.CHRG-108hhrg91396/CHRG-108hhrg91396_djvu.txt, accessed 15 August 2017. 2 http://www.uvu.edu/esa/jackrabbit/, accessed 21 August 2017.

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Directorate (SRD) of the UK Atomic Energy Authority (UKAEA)(Webber et al., 1992), but much of its development over the last 15years has been led by HSE. Whilst DRIFT was originally conceived as adense-gas dispersion model, it has subsequently been adapted to modeldispersion of passive and buoyant sources (Tickle and Carlisle, 2008).

DRIFT incorporates a momentum-jet model to simulate pressurizedreleases. This includes both single and two-phase jet models, where thelatter assumes homogeneous equilibrium between the gas phase and thedispersed liquid droplet phase. To model the downwards impinging jetin the Jack Rabbit II 2015 trials, the DRIFT simulations were performedin two stages. The first stage modeled the expanding jet from the dis-charge point on the underside of the vessel to the point where the jet hitthe ground. The resulting conditions at the ground were then mappedonto an area source for the second stage of the DRIFT simulations,which modeled the dispersion of the cloud downwind.

When a two-phase jet impinges on the ground, a fraction of the li-quid is deposited on the ground (i.e., it rains-out) and the remainingliquid remains airborne as a dispersed liquid aerosol phase. Videofootage of the Jack Rabbit II experiments clearly showed that some ofthe liquid rained-out to form a chlorine pool on the concrete test pad.The dispersing cloud also appeared to contain aerosol droplets, whichlikely consisted of both chlorine droplets and condensed water vapor.The amount of chlorine rainout in the trials is the subject of ongoinganalysis (Spicer, 2017). Latest results suggest that 35% of the initialchlorine mass in the tank rained-out onto the pad in the Jack Rabbit IIPhase 2 (2016) Trial 6 (Spicer, 2017; Spicer et al., 2017a). This trialinvolved an initial mass of 8 tons and the same orifice size and down-wards-directed jet configuration as the Jack Rabbit II Phase 1 (2015)trials considered here.

To examine the impact of rainout on the dispersion model predic-tions, sensitivity tests were performed with DRIFT. Two bounding caseswere modeled: one where there was no rainout (i.e., all the liquid in thetwo-phase jet remained airborne as a dispersing aerosol) and another inwhich all the liquid that was present in the two-phase jet at the pointwhen it hit the ground rained-out to form an evaporating pool (i.e.,without producing any aerosol droplets). The thermodynamic models inDRIFT are able to account for multi-component mixtures and humidityeffects (condensation and evaporation of water droplets, and associatedlatent heat transfer), including their effect on cloud buoyancy (Tickle,2001).

DRIFT can model various types of dispersing clouds: from in-stantaneous, continuous, finite-duration and time-varying releases. Thefinite-duration model was used for the Jack Rabbit II simulations,which is based on the physics of the continuous release model, wherethe positions of the front and back of the cloud are tracked over time asthe cloud drifts downwind. A smoothing operation is applied to thecloud concentrations to account for along-wind diffusion effects at thefront and back edges. DRIFT also incorporates a sub-model to accountfor the initial gravity-driven spreading of a dense-gas release around thesource. For large release rates, this model expands the initial sourcediameter and allows the trailing edge of the cloud to move upwind ofthe source.

The version of DRIFT used was 3.7.2, and it was combined with twoother HSE models: STREAM version A.7 and GASP version 4.2.12(Webber and Jones, 1987). The STREAM model was used to calculatethe source conditions (mass release rate, pressure, temperature and li-quid fraction) at the orifice. Sensitivity tests were performed using twodifferent STREAM sub-models: one assuming metastable liquid flowthrough the orifice and the other assuming flashing two-phase flow. Inboth cases, the discharge coefficient used was 0.61. The GASP modelwas used to simulate the spreading of an evaporating liquid pool, forthose cases where there was liquid rainout. The outputs from GASPprovided a secondary vapor source term for the second stage of DRIFTdispersion simulations.

In common with most other integral models, DRIFT assumes thatthe terrain is flat. Obstacles are modeled as a uniform aerodynamic

surface roughness, and a correction to the wind speed profile fromHanna and Britter (2002) is used to model flows through urban canopylayers. To account for the presence of the urban array in the Jack RabbitII model, DRIFT simulations were performed for each trial using twodifferent roughness lengths: z0= 0.4 m for the urban array, andz0= 0.001m for the downwind desert playa. The results from the twosimulations were combined together by offsetting the z0= 0.001mresults to match the centerline concentrations from the z0= 0.4m re-sults at a distance of 100m (approximately the downwind edge of themock urban array). The choice of these two roughness lengths wasbased on guidance provided by the coordinators of the MWG, whocalculated roughness lengths from the mean obstacle height of the mockurban grid (2.68m) and the lambda value of 0.18 (for background tothis calculation method, see Hanna and Britter (2002)).

In the current release of DRIFT (version 3.7.2), there is no specificmodel to account for chlorine reaction or deposition effects (e.g., drydeposition, photolysis). However, a deposition model has recently beencoded into a development version of DRIFT, which has been used forsensitivity analysis (see Section 5 – Discussion).

Further details of the DRIFT model physics are given in the technicalreference manual (Tickle and Carlisle, 2013) and a report on the vali-dation of DRIFT for dense-gas dispersion is available from HSE(Coldrick and Webber, 2017). Recent model developments are alsosummarized by Cruse et al. (2016). A number of conference presenta-tions have been given by HSE on DRIFT and PHAST modelling of theJack Rabbit II experiments over the last two years (Gant et al., 2015,2017; Gant and Tucker, 2017; McKenna et al., 2016a, 2016b, 2017a,2017b).

2.2. PHAST

PHAST is a hazard-assessment software package produced by DNV-GL Software for modelling atmospheric releases of flammable or toxicchemicals (Witlox and Oke, 2008; Witlox, 2010; Witlox et al., 2017). Itincludes methods for calculating discharge and dispersion, and toxic orflammable effects. A principal component of PHAST is the UnifiedDispersion Model (UDM), which incorporates sub-models for two-phasejets, heavy and passive dispersion, droplet rainout and pool spreadingand evaporation. The model can simulate both unpressurised andpressurized releases, time-dependent releases (steady-state, finite-duration, instantaneous or time-varying), buoyancy effects (buoyantrising cloud, passive dispersion or heavy-gas-dispersion), complexthermodynamic behavior (multiple-phase or reacting plumes), groundeffects (soil or water, and flat terrain with uniform surface roughness),and different atmospheric conditions (stable, neutral or unstable).

To model the Jack Rabbit II experiments, the time-varying leakoption in PHAST version 7.11 was used, following the advice of DNV-GL. This model splits the source into multiple time-steps and thenmodels the dispersion of the corresponding release segments. Themodel does not account for diffusion and gravity spreading effects inthe along-wind direction and it was therefore expected that PHASTcould over-predict the concentrations in the far field. PHAST Version8.0 has a new sub-model for along-wind diffusion effects which shouldcorrect this behavior. However, its release came too late for it to beused in the current work. In the future, it would be useful to produceresults using this newer version of PHAST and to run sensitivity testswith PHAST's finite-duration release model.

Two different PHAST sub-models were used to model the dischargefrom the vessel: a metastable liquid flow model which used PHAST'sdefault discharge coefficient of 0.6 and a model that assumed flashingtwo-phase flow through the orifice, which used a calculated dischargecoefficient of 0.96. On the advice of DNV-GL, the downwards release inthe Jack Rabbit II 2015 tests was modeled by setting the release angle to−90° from the horizontal, instead of using the downwards impinging-jet sub-model. The expansion of the jet from the vena contracta con-ditions to atmospheric pressure (final conditions prior to air

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entrainment) was modeled using PHAST's conservation of momentumsub-model.

To model the different surface roughness lengths for the mock urbanarray and the playa in the Jack Rabbit II 2015 tests, the same approachwas used to that adopted for DRIFT, with simulations being performedusing two roughness lengths of z0= 0.001m and 0.4 m, which werelater combined into one result by matching the centerline concentra-tions at the edge of the urban array.

Similar sensitivity tests were also performed with PHAST to thoseundertaken for DRIFT on the amount of droplet rainout from the im-pinging two-phase chlorine jet. The default pool evaporation in PHASTwas used to provide an additional source of vapor in the dispersingcloud for those cases with rainout.

2.3. CFD

CFD simulations of the Jack Rabbit II experiments were performedby HSE using the general-purpose CFD code, ANSYS-CFX version 17(ANSYS, 2015). The purpose of these simulations was to investigate thenear-field flow behavior of the two-phase jet interacting with theground and to examine how the cloud dispersed through the mockurban array of obstacles. The CFD model accounted for the initialmomentum and turbulence generated by the impinging jet, whereas theDRIFT and PHAST models used a more simplified low-momentumsource. The CFD model also helped to show how the dense-gas cloudwas channeled in between the Conex containers.

ANSYS-CFX has two main options available to simulate two-phasejets of chlorine: either treating the evaporating chlorine droplets as adispersed droplet phase or as discrete particles that are tracked throughthe flow (i.e., an Eulerian or Lagrangian approach). The latter methodwas adopted to simulate the Jack Rabbit II experiments, since HSE hadpreviously obtained good results using a similar approach to modelsprays of methanol and gasoline (Gant et al., 2007; Coldrick et al.,2011), and multi-phase jets of carbon dioxide (Gant et al., 2014).

Source conditions for the CFD model were prescribed from theoutput of PHAST's discharge model at the point where the jet had ex-panded to reach atmospheric pressure. These conditions consisted ofthe mass flow rate, velocity, source diameter and liquid fraction. Theinitial temperature was specified as the chlorine saturation temperatureat the relevant atmospheric pressure (which was relatively low atDugway, given its altitude). The initial chlorine droplet size spectrumwas prescribed as a Rosin-Rammler size distribution with a mean dro-plet diameter specified from the PHAST outputs (using its Phase III JIPsize correlation (Witlox et al., 2011)) with a Rosin-Rammler power ofn=2. The drag between the chlorine droplets and the surroundingvapor phase was calculated using the drag model of Schiller andNaumann (1933) combined with the stochastic dispersion model ofGosman and Ioannides (1981) to account for turbulence effects. Heattransfer between the vapor phase and the droplets was modeled usingthe Ranz–Marshall correlation (Ranz and Marshall, 1952). These sub-models are all available in the standard version of ANSYS-CFX version17 and did not require user-coding.

The behavior of the droplets when they hit the concrete test pad is asignificant source of uncertainty in the CFD model. There are a numberof different possible outcomes: the droplets could stick and spread as athin film of liquid, or bounce and breakup. This type of interactionbetween droplets and walls has been studied for application to internalcombustion engines (e.g., Bai et al. (2002)) but it is unclear whether themodels developed there can be applied to the very different context ofthe Jack Rabbit II experiments. Sensitivity tests were therefore per-formed on the CFD model using three simple approaches: one where thedroplets lost all their momentum on impact (all the liquid rained-out) toproduce a spreading, evaporating pool of chlorine; one where thedroplets “bounced” elastically without losing any momentum (i.e., norain-out); and a third approach where half the mass of droplets rained-out to produce a pool and the remaining half splashed back into the air

as an aerosol with half the initial droplet momentum and with a re-duced droplet diameter of 10 μm. The splashed droplets in this finalcase bounced upwards from the jet impingement point with an initialdroplet trajectory distributed in a hemispherical pattern. Results fromthe sensitivity tests on these three impingement models are not shownhere (details can be found in the presentation by Gant and Tucker(2017)) but they had a strong effect on the results. A comparison of thepredicted concentrations to the data from Jack Rabbit II 2015 Trial 1showed that the final method (where half the droplets splashed and halfrained-out) was in best agreement with the measurements. The methodwas therefore used to simulate the remaining Jack Rabbit II trials.

The spreading and evaporating pool of chlorine was simulated inthe CFD model by coupling it to the GASP integral model. The couplingof the two models involved calculating the rate at which liquid rained-out in the CFD model, which was then input into GASP as the input flowrate of liquid. The pool vaporization rate was then calculated by GASPand used to provide an area-source input for the CFD simulation. Thelimitation of this approach is that it lacked feedback between thechlorine vapor concentrations predicted by the CFD model and thevaporization rate predicted by GASP. The DRIFT and PHAST modelsalso had this same limitation.

The CFD model was only used to simulate the dispersion behaviordownwind of the vessel to a distance of around 500m. This limitationwas partly due to the computing resources required to simulate largerdomains. For the 500m domain, the simulations typically took 12 hrunning in parallel on 24 processors to simulate the first 10 min of therelease. The second reason for limiting the domain size was that errorswere likely to become more significant in the model at larger distancesdownwind, where the dispersion behavior became dominated by at-mospheric transport effects (passive dispersion and wind meandering).These errors were due to the CFD model using a fairly simple inletprofile for the atmospheric boundary layer: a log-law profile that as-sumed neutral atmospheric stability, which did not vary over time(whereas conditions varied in the experiments – see Section 3). Thetype of turbulence model used in the CFD model (a Reynolds-AveragedNavier Stokes model, in this case the Shear-Stress Transport model ofMenter (1994)) is also known to produce errors in atmosphericboundary-layer profiles over distances of 1 km or more (Batt et al.,2018). These errors should, however, have limited effects in the nearest500m, where the flow behavior was instead dominated by jet-mo-mentum and dense-gas effects.

The computational grids used in the CFD simulations were un-structured, with tetrahedral cells clustered within the jet and prism-shaped cells along the ground (using between 1 and 2 million cells intotal). A second-order accurate numerical scheme was used for theconvective terms in both the momentum and turbulence model equa-tions, and the temporal scheme was also second-order accurate in time.The number of computational particles injected per second (to simulatethe chlorine droplets) was 1000 s−1 and the time-step was 0.1 s.Sensitivity tests were performed on the number of grid cells, the particlecount and the time-step, which showed that changing the chosen valuesby a factor of two had very little effect on the results (for details, seeGant and Tucker (2017)).

2.4. NCAR model

The NCAR dense gas dispersion model is an integral approach basedon equations for the conservation of puff volume (or mass), radialmomentum, buoyancy, and species (e.g., chlorine). The focus is on theprediction of the maximum surface concentrations of chlorine for eitheran instantaneous puff or a short-duration release from a storage tank“blowdown”. The puff is modeled as a squat or shallow-depth circularcylinder and includes: radial spreading driven by the high source mo-mentum flux, gravitationally-induced dispersion by the negativebuoyancy due to the chlorine density, initial “slumping” and heightchange with time, entrainment of ambient air at the puff top, and

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transport by the wind. The model has not previously been documentedin the literature, and the following discussion presents a brief modeloverview with equations and details given in Appendix A.

The source model is simple and is based on an all-liquid release,which may be appropriate for liquids stored under high pressure(Britter et al., 2011). However, as discussed in Section 4.1, this modeloverestimates the flow rate in the Jack Rabbit II experiments, where ametastable liquid state has been predicted and inferred from measure-ments (Spicer and Miller, 2017). Thus, we use measured flow rates ifavailable, as in Jack Rabbit II, or the all-liquid model as a default ap-proach. The latter is deemed an adequate but conservative approachwhich is consistent with our interest of simulating primarily the max-imum surface concentrations. The source mass, momentum, andbuoyancy fluxes are assumed constant for t≤ tr and zero for t > tr,where tr is the source duration. An extension for time-dependent fluxesobserved near the end of the Jack Rabbit II release (Section 4.1) isplanned.

The dispersion model is currently aimed only at downward ventingreleases and is divided into a short-time regime (t≤ tr) and a long-timeregime (t > tr). In the short-time region, the high-velocity jet or sourceexit momentum dominates the radial spreading initially with entrain-ment occurring over the puff-top area. Following the ideas of Mortonet al. (1956) for plumes, the entrainment is assumed equal to the toparea (R2) times an entrainment velocity we= αvr, where R is the puffradius, vr is the average radial velocity, and α is an entrainmentparameter of order 0.02–0.1. This relationship assumes that the puff-generated turbulence responsible for the entrainment is driven by andproportional to the radial velocity. In the momentum phase, the radiusis found to vary as R∝ Fm¼ t ½, where Fm is the source momentum flux.

At later times in the initial phase, a buoyancy-driven approach isapplicable and has been developed for puff growth based on a densitycurrent model (e.g., Britter (1989); Simpson (1982); Weil et al. (2002))and buoyancy conservation. This model applies when the vr due tosource momentum has decreased substantially, and the puff negativebuoyancy takes over as the primary driver of the velocity. For thebuoyancy model, the puff radius is predicted to grow as R∝ Fb¼ t3/4,where Fb is the source buoyancy flux; this R dependence on Fb and t alsohas been obtained using similarity arguments (Britter, 1989).

Two approaches have been adopted for determining the transitionfrom the momentum- to buoyancy-driven spreading: analytical andnumerical. In the analytical method, the transition is assumed to occurat a time tmb when the radial velocities from the two models are equal;the momentum-dominated case applies for t≤ tmb and the buoyancymodel for tmb < t≤ tr. However, this results in an undesirable kink inthe concentration gradient, dC/dx, at tmb, which motivated the devel-opment of a numerical method and solution. The latter adopts a mo-mentum equation that includes the radial pressure gradient, which isproportional to the local puff buoyancy, and leads to a seamless tran-sition from the momentum-to-buoyancy regimes as well as other ben-efits (Appendix A). At long times (t > tr), the total buoyancy FT (= Fbtr) emitted during the release provides the forcing for the radial velocityand spreading, and in this regime, the radius grows as R∝ FT1/4 t1/2.

For the buoyancy-driven puffs, both at short and long times, theentrainment at puff top is assumed due to the local ambient turbulence,since the puff momentum-driven turbulence (αvr) is small. The en-trainment velocity we is obtained from the Briggs et al. (2001) model,which assumes that near the ground the main turbulence forcing orvelocity scale is the friction velocity u*, i.e., for mechanically generatedturbulence over a rough surface. This forcing is modified by the puffRichardson number (Ri*), which characterizes the ratio of the localbuoyancy-to-inertial forces and results in decreased entrainment whenRi* is large (stable buoyancy forces dominate the mixing; see AppendixA).

The above overview highlights the puff radial spreading rate andradius, R, but the cloud height, h, is clearly important for determiningthe puff volume, concentration, and downwind transport; the

determination of h is discussed further in Appendix A.The puff is advected horizontally using the mean wind at the puff

centroid height, h/2. Currently, the wind is provided by a neutrallogarithmic profile for z > hc and an exponential profile for z≤ hc,which are smoothly joined at the canopy top, hc. The surface roughnessand displacement heights are estimated from the underlying surfaceproperties (Appendix A). For the 2015 Jack Rabbit II experiments: 1)the surface parameters are calculated from the MacDonald et al. (1998)approach using the container height and canopy frontal area fraction,and 2) the friction velocity necessary for the wind estimate is de-termined from these parameters and the upwind Portable Weather In-strumentation Data Systems (PWIDS) anemometer data (Section 3).

2.5. Empirical correlation for maximum mean concentration decrease withdistance

In previous work, Hanna et al. (2016b, 2017) found that thechlorine maximum surface concentrations from the 2015 Jack Rabbit IIexperiments decreased approximately as Cmax∝ x−5/3. This empiricalcorrelation is presented later in the NCAR model comparisons with theJack Rabbit II data and is derived below based on results from AppendixA.

The maximum concentration for an effective Gaussian distributionis proportional to the puff top-hat concentration Cth (Section 4.4),which is estimated from the puff radius R and depth h as Cth∝Q/(R2h).At long times or far downstream, R∝ t½ and h∝ t and with Cmax∝ Cth,the Cmax is given by

Cmax∝Q/t2 (1)

A conversion from t to x as the independent variable can be madeusing the mean wind profile and the puff entrainment relationship atlong times, where h∝ t. This h dependence means that the entrainmentvelocity we (= dh/dt) must be constant.

We can also write

dh/dx = (dh/dt)/(dx/dt)=we /U(h) (2)

where the mean wind is evaluated at z= h, and Eq. (2) can be rewrittenas

U(h) dh=we dx (3)

Now instead of using the logarithmic wind profile, we adopt asimple power-law profile of the form

U(z)=Uref (z/zref)p (4)

where Uref is the reference wind speed at the reference height zref, andthe exponent p is < 1.

By rewriting Eq. (4) as U(z)= a1zp and substituting this along withz=h into Eq. (3), we can integrate the latter to obtain

h1+p= [we (1 + p)]/a1 ⋅ x (5a)

or

h=b1 x– 1/(1+p) (5b)

where b1= f (we,p, a1). Far downstream, we have h=wet and by usingthis in Eq. (5b), we find

t = (b1/we) x– 1/(1+p) (6)

The above relationship can be substituted into Eq. (1) to obtain

Cmax∝Q x−2/(1+p) (7)

For neutral conditions and a typical exponent p=1/6 (Counihan(1975); Irwin (1979), with z0= 0.1 m), the exponent on x is −1.71 andfor p=1/7, it is −1.75. These x exponents are close to the empirically-determined value of −5/3, and for a slightly more stable environment

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with p=1/5, the exponent is identically −5/3. This simple derivationshows that for a sheared or height-dependent wind characterized by apower-law function of z, the shear will lead to a slower variation of Cmax

with distance than with time. A smaller value of p (< 1/6) as in un-stable conditions (Irwin (1979)) leads to a Cmax∝ x-s with s closer to 2,whereas a larger value of p results in an s less than and farther from 2.The analysis also can be conducted for a non-constant we and an hexhibiting a different time dependence.

3. Jack Rabbit II 2015 data

The US Army staff at Dugway Proving Ground and various JackRabbit II Working Groups went to considerable efforts to verify thequality of the Jack Rabbit II data and interpret the measurements.Significant work has been published by Spicer and Miller (2017) on therelease rate from the vessel and Hanna et al. (2016b, 2017) have ana-lyzed the concentration data to show that it decayed downwind in apower-law relationship, with Cu/Q decreasing in proportion to x−5/3,where C is the maximum arc-wise concentration, u the wind speed, Qthe release rate and x the distance downwind. In addition to that work,HSE has analyzed the measurement data to help understand how con-centrations varied across the width of the plume and to assess the im-pact of sensors saturating when they were exposed to high chlorineconcentrations. HSE's analysis of the plume concentrations is summar-ized below, since it is important to take it into account in model vali-dation studies. Further details can be found in the work presented byGant et al. (2017).

Table 1 provides a summary of the test conditions for the five trialsconducted in 2015. Data on the mass of chlorine and pressure inside thevessel were obtained from load cells and pressure gauges. The weathermeasurements came from the PWIDS, which recorded wind speed, winddirection, temperature, humidity and pressure. In total, there were 34PWIDS distributed across the Dugway test site, both in the area wherethe plume dispersed and at various other lateral and upwind positions.The weather information shown in Table 1 is taken from an average ofall the PWIDS at the start of the release, with the exception of Trial 2,where conditions are taken from just PWIDS 25 (located about 830mupwind from the release point). The PWIDS were mounted on tripods2m above ground level and therefore the reference height for the windspeeds given in the Table is 2 m instead of the usual 10m.

The Pasquill stability classes given in Table 1 were taken from aSurface Weather Observation Form provided by Dugway ProvingGround, but there was no stability class provided for Trial 1, so this wasestimated by HSE from the wind speed, cloud cover and time of day tobe Class F, using the flow chart from the US Army Research Laboratoryreport of Wetmore and Ayres (2000). There are plans to undertake amore thorough examination of the weather data in due course fromadditional sensors that were present at Dugway Proving Ground, whichincluded several 32m high meteorological towers, SODAR, radiometerand energy budget stations.

The PWIDS measurements showed that the wind speed and stabilitychanged during each of the Jack Rabbit II 2015 trials. The tests took

place in the early morning and, as the sun rose, conditions often becamemore unstable, the wind strengthened and it changed direction. Plots ofthe measured wind speed and direction are shown in Fig. 2. The changein the atmospheric stability is indicated by the Pasquill stability classesgiven in Table 1, where for example C → B indicates a shift from Pas-quill Class C to B over the period of the test. The weather data is an areaof uncertainty for dispersion models like DRIFT and PHAST that assumea fixed set of conditions for the entire duration of the dispersion period.To investigate the potential impact of this on the model predictions,sensitivity tests have been carried out (Section 5).

Several different types of chlorine concentration sensors were de-ployed on the arcs downwind of the mock urban array in the JackRabbit II trials. JAZ sensors were used within the mock urban array,Canary and MiniRAE sensors were used in the near-field and ToxiRAEsensors were used further downwind. The Canary sensors were few innumber, but they had a high calibration limit of 10,000 ppm. TheMiniRAE sensors had nominal upper saturation limit of 2000 ppm, butthe sensors were calibrated before and after each trial and the mea-surements were scaled accordingly, so that in some cases the reportedmaximum concentration exceeded 2000 ppm. The ToxiRAE sensors hada saturation limit of just 50 ppm.

The measured concentrations from the five 2015 trials are sum-marized in Fig. 3. The graphs show the maximum concentrations thatwere measured by the sensors at any point in time during the trial. Thedata was not averaged over time before calculating the maximumconcentrations, and the Canary, MiniRAE and ToxiRAE sensors outputdata at approximately 1 s intervals. At the foot of Fig. 3, a circle isshown that is proportional in diameter to the mass of chlorine releasedin that trial. An arrow also shows the wind speed and direction, wherethe arrow length is proportional to the wind speed and the angle of thearrow indicates the wind direction. Each graph shows the maximummeasured concentration versus the angle across the arc of sensors(where 0° is along the centerline of the mock urban array). For eachtrial, there are three graphs shown. The graphs at the foot of the pagepresent the concentrations measured nearest the release point, at adistances of 200 and 500m downwind, the middle graphs show theconcentrations at 1 and 2 km downwind, and those at the top show theconcentrations at 5 and 11 km.

There are several notable patterns exhibited in Fig. 3. Firstly, thearcs closest to the release point in Trials 1, 2 and 4 show signs that thecloud was bifurcated in the near field, with two lobes of high con-centration separated by a region of lower concentration. The twin peaksare visible at 200m and/or 500m in these trials, but they do not persistfurther downwind to 1 km, which suggests that the two lobes mergedbefore reaching this distance. Before the trials took place, CFD simu-lations carried out by Los Alamos National Laboratory (Gowardhan,2015) predicted this type of bifurcated cloud structure. The phenom-enon was produced by the cloud being channeled between the Conexcontainers, which were aligned in such a way that the path of leastresistance directed the cloud laterally out of the sides of the mock urbanarray, rather than directly downwind. Similar cloud behavior was laterpredicted by HSE's CFD model (Section 4.2).

Table 1Release and weather data for the Jack Rabbit II 2015 trials provided by Dugway Proving Ground.

Trial Chlorine MassReleased (kg)

Initial TankPressure (barg)

Wind Directiona

(degrees)Wind Speedb

(m/s)AtmosphericTemperature (°C)

RelativeHumidity (%)

Atmospheric Pressure(Pa)

Pasquill StabilityClass

1 4509 6.50 −18 2.0 17.7 39.2 87,350 Fc

2 8151 6.06 −7 4.2 22.7 33.6 87,512 C → B3 4512 5.71 +4 3.9 22.5 30.3 87,097 D4 6970 5.16 +18 2.3 22.5 26.9 86,926 D → C → B5 8303 5.87 +17 2.7 22.2 26.5 86,653 D

a Wind direction relative to centerline of urban array and the downwind sensor arcs, which were aligned at an angle of 165° to North.b Wind speed at reference height of 2m.c No Pasquill stability class was given by Dugway for Trial 1, so this was estimated by HSE based on wind speed, cloud cover and time of day.

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A second notable feature of the concentrations in Fig. 3 is that theCanary sensors measured much higher concentrations than theMiniRAE sensors at 200m. In Trial 1, both types of sensors were locatedat the same position and the difference in measured concentrations wasbetween a factor of two and a factor of five. In most (but not all) ofthese cases, the differences appear to have been caused by the

concentrations exceeding the saturation limit of the MiniRAE sensors.The factor of five difference in Trial 1, where the Canary recorded4048 ppm and the MiniRAE just 824 ppm, appears to be due to theMiniRAE sensor malfunctioning in that case. Since there were onlythree Canary sensors on the 200m arc in Trials 1 to 5, they may nothave captured the maximum cloud concentrations. There is therefore

Fig. 2. Wind speed and direction measurements from the Jack Rabbit II 2015 trials. Measurements are all taken from PWIDS 19, located 100m upwind of releasepoint, apart from in Trial 3 where PWIDS 19 failed and two sets of measurements are shown instead from PWIDS 25 and 31 (located approximately 1 and 2 kmupwind, respectively). No significant concentrations were measured on the 11 km arc of sensors in Trial 3.

Fig. 3. Measured maximum chlorine concentrations in the Jack Rabbit II 2015 trials. Three graphs are shown for each trial with the concentrations at 200 and 500m(bottom), 1 and 2 km (middle) and 5 and 11 km (top). The horizontal scale shows the angle across the arc of sensors. Circles at the base of the plot indicate the mass ofchlorine released and arrows indicate with wind speed and direction.

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some uncertainty in the peak concentration at 200m: the actual con-centration may have been higher than was indicated by the measure-ments.

Fig. 3 also shows that in Trial 3 the measured concentrations werevery low in the far-field. Only one or two sensors recorded any con-centrations at the 1 and 2 km arcs. Further downwind at 5 km, sig-nificant concentrations were only recorded by one sensor, which re-gistered a maximum of just 4 ppm. At 11 km, the measuredconcentrations were all essentially zero. These measurements are muchlower than those obtained in Trial 1, which involved a comparable massof chlorine released, where the maximum concentration was 20 ppm at11 km. The results suggest that the higher wind speed of 3.9m/s inTrial 3 (as compared to 2.0m/s in Trial 1) may have produced a nar-rower plume which passed in between the sensors at the furthest arcs(where the distance between neighboring sensors was nearly 1 km).Further analysis of the UV Lidar data for Trial 3 may help to showwhether this was the case. Another difference between the two trialswas that the atmospheric conditions were less stable in Trial 3 thanTrial 1, and so the cloud may therefore have entrained air at a fasterrate and diluted faster in Trial 3 than Trial 1.

Wind speeds were broadly similar in Trials 2 and 3, varying overtime between 4m/s and 6m/s (see Fig. 2). However, significant con-centrations were registered by the sensors on the 5 km and 11 km arcsin Trial 2. This is likely due to the cloud being much larger in Trial 2,since the mass of chlorine released was nearly double that of Trial 3(8151 kg versus 4512 kg). This also explains the higher concentrationsin the near-field in Trial 2 than Trial 3, at the 200m and 500m arcs.The reason for the lower concentrations at 1 km and 2 km arcs in Trial 2than Trial 3 is less clear. It may relate to the atmosphere being moreunstable in Trial 2 (Pasquill Class B/C as compared to Class D in Trial3).

The graphs in Fig. 3 also show that the wind direction in Trials 4 and5 caused the chlorine gas cloud to drift beyond the edge of the sensorarray at the 1 km and 2 km arcs. In Trial 4, the wind appears to haveblown the cloud back onto the array at the 5 km and 11 km arcs, but inTrial 5 the peak concentrations were measured at the edge of the arc,which suggests that the concentrations could have been higher outsidethe sensor array.

Caution is needed in assessing the concentrations at the 5 km arc inTrials 1, 2 and 5, since the ToxiRAE sensors saturated at 50 ppm inthese trials. This behavior was shown clearly by the plateau in the time-varying measured concentrations (Fig. 4). The saturation problem didnot affect the concentrations in Trial 3, since the concentrations werevery low in that trial (as discussed above). In Trial 4, the concentrationsat 5 km were below the saturation limit of 50 ppm, but the measure-ments at 5 and 11 km also showed very little reduction in concentrationwith distance (a small decrease from 26 to 22 ppm over 6 kilometers).This suggests that the centerline of the cloud passed in between sensorson the 5 km arc in Trial 4.

In model validation studies, the quantity that is often used to assessthe accuracy of model predictions is the maximum arc-wise con-centration, i.e., the highest concentration measured on an arc at a givendistance downwind, at any point in time during the trial. This data issummarized in Table 2 and a color key is used to identify the factorsdiscussed above that may have caused the measurements to under-re-port the maximum concentration at that distance. The height of thesensors was 0.3m Above Ground Level (AGL) in all cases with the ex-ception of the measurement at 0.2 km in Trial 3, which was at a heightof 0.7 m AGL. The data is compared to the model predictions shownlater in Section 4.

4. Results

Model predictions are compared to measurements below, startingwith the discharge conditions and then proceeding to the CFD results,the DRIFT and PHAST results, and then the NCAR model results.

4.1. Discharge

Fig. 5 compares the measured chlorine mass in the vessel as afunction of time to model predictions from STREAM and PHAST.Comparisons are only shown for the Jack Rabbit II 2015 Trials 1 to 4,since the sensors failed in Trial 5. The measured chlorine mass wascalculated from load cell data using the method described by Spicer andMiller (2017). Two different sub-models were tested for both STREAMand PHAST: one that assumed metastable liquid outflow and the otherfor flashing two-phase flow through the orifice.

The review by Britter et al. (2011) suggested that for sharp-edgedorifices (less than 10 cm in length, as in the case here), the flow hasinsufficient time to establish thermodynamic equilibrium and thereforethe fluid remains in a metastable liquid state at the orifice. Analysispresented by Babarsky et al. (2016), however, indicated that there wassome liquid boiling within the vessel in the Jack Rabbit II 2015 trials,which led to a non-zero vapor fraction in the fluid flowing through theorifice. The metastable liquid models in STREAM and PHAST assumedthat the flow through the orifice was 100% liquid, and the resultspresented in Fig. 5 show that these models over-predict the dischargerate (i.e. the mass in the tank decreases faster over time than themeasurements). In contrast, the STREAM and PHAST models that as-sumed flashing in the orifice under-predict the measured flow rate,although the STREAM results are fairly close to the data in Trials 1 and3. The reasons for the differences between STREAM and PHAST arecurrently under investigation. In future work, the authors plan to in-vestigate the potential effect of the errors in the predicted releasedurations on the downwind concentrations using the method describedby Hanna and Chang (2017). The DRIFT, PHAST and CFD dispersionmodel predictions shown in Sections 4.2 and 4.3 use the release ratespredicted by STREAM and PHAST, rather than those measured (detailsare given below).

4.2. CFD results

The CFD model predictions of the near-field dispersion behavior arecompared to the measurements in Fig. 6. At the top of the Figure, thecontours show the predicted maximum concentrations from the CFDmodel at ground level. The plots are aligned to the wind directionwhich varies from −18° in Trial 1 to +17° in Trial 5. Two graphs areshown for each trial, which show the maximum chlorine concentrationsat 200 and 500 m as a function of the angle across the arc of sensors(where 0° is along the centerline of mock urban array). At the foot of theFigure, the circles and arrows indicate the release size and the windspeed and direction (as in Fig. 3).

The contour plots from the CFD model in Fig. 6 show trends in theplume width that are consistent with the experimental observations. Itwas noted earlier that the plume was much narrower in Trial 3 than inTrial 1, despite both Trials involving similar amounts of chlorine re-leased. This is consistent with the pattern shown in the CFD modelresults, where the combination of the head-on wind direction andhigher wind speed in Trial 3 produced a narrower plume than in Trial 1.

The graphs in Fig. 6 show that the concentrations predicted by theCFD model are in good agreement with the measurements in Trial 1,both at the 200 and 500m positions. The shape of the concentrationprofile across the arc matches the data well. As noted earlier, theMiniRAE sensors recorded significantly lower concentrations than thethree Canary sensors in these trials.

The agreement between the CFD model and the measurements isreasonable in Trials 2 and 3 but significantly worse in Trials 4 and 5,where the CFD model predicted much higher concentrations than weremeasured. This behavior may be due to the CFD model assuming aconstant wind speed and direction, based on the average across all thePWIDS, whereas the conditions were highly variable in the experi-ments. The PWIDS at different locations in the experiments recordeddifferent wind speeds and directions, and the conditions also changed

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over time (it took between roughly 30 and 60min for peak con-centrations to arrive at the furthest 11 km arc). The measurementspresented earlier in Fig. 2 showed that in Trial 4 the wind direction atthe position closest to the release point (PWIDS 19, located 100 mupwind of the chlorine vessel) shifted from +21° to +51° in the timethat it took for the chlorine cloud to reach the 500 m arc, whereas theCFD model assumed a constant wind angle of +18° throughout thisperiod. In Trial 5, the change was even more extreme, with the winddirection shifting from +34° to +106° in the experiments, whilst theCFD model assumed a constant wind direction of +17°. The deviationsin wind direction in Trials 1, 2 and 3 were much less significant.

The CFD model may have produced more accurate results had itused inlet boundary conditions that more closely reflected the localwind conditions near the release point, rather than using an averageacross all the PWIDS. Another uncertainty in the CFD modelling is theatmospheric stability. The CFD model assumed neutral stability in all ofthe trials. In the experiments, the conditions were estimated to be stablein Trial 1 (Class F), and unstable in Trial 2 (Class C to B). In the othertrials, the conditions were close neutral (at least initially in Trials 4).The atmospheric stability may therefore be a factor in Trials 1 and 2,although it would be challenging to accurately model different stabilityclasses in these trials using the ANSYS-CFX CFD model (Batt et al.,2018).

Despite the limitations of the CFD model, the results still provideuseful predictions of the dispersion behavior. For example, the resultssuggest that the peak concentrations measured in Trials 4 and 5 couldsignificantly under-estimate the actual peak concentrations. Also, theCFD results in Fig. 6 confirm the earlier findings of Gowardhan (2015)

Fig. 4. Measured concentrations varying as a function of time at the MiniRAE and ToxiRAE sensors that recorded the highest concentrations in the Jack Rabbit II2015 trials. The nominal upper (maximum) calibration limits for the ToxiRAE and MiniRAE sensors are indicated by red dashed lines. (For interpretation of thereferences to color in this figure legend, the reader is referred to the Web version of this article.)

Table 2Maximum arc-wise concentrations in the Jack Rabbit II 2015 trials.

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that the twin peaks in the concentration profiles at 200 and 500m arecaused by the Conex containers channeling the cloud sideways out ofthe sides of the mock urban array.

4.3. DRIFT and PHAST results

Concentration predictions from DRIFT and PHAST are compared tothe measured values for Jack Rabbit II Trial 1 in Fig. 7. A “baseline”case was simulated using the metastable liquid discharge sub-modelsand assuming no rainout. Sensitivity tests were also performed usingthe alternative flashing discharge sub-models, with and withoutrainout. The experimental data is shown in Fig. 7 as symbols. Coloredtriangles are used to represent the measurements that may have under-recorded the actual maximum arc-wise concentration, for the reasonssummarized in the key. The black diamond symbols indicate mea-surements that were unaffected by these issues.

Both the DRIFT and PHAST models predict higher concentrationsthan were measured in Trial 1. The DRIFT simulations with rainoutproduce slightly lower concentrations in the near-field and higherconcentrations further downwind than the simulations without rainout.This behavior is probably due to evaporation of the aerosol droplets (inthe case without rainout) sustaining high concentrations in the near-field. Since the cloud is more dense in the near-field, the cloud spreadsmore rapidly in the lateral direction under gravity, which may explainthe steeper decay in concentration with distance. The differences inpredicted concentrations between using the metastable liquid and theflashing discharge models are less significant than the effects of rainout.

For PHAST, there is a more significant difference in behavior be-tween the various sub-models, with the baseline case over-predictingthe concentrations by around an order of magnitude. The differencesbetween this behavior and that observed for DRIFT may be due toPHAST not accounting for along-wind diffusion effects, and also the use

of the sub-model for initial gravity-driven spreading over the source inDRIFT. The very high concentrations produced by the PHAST baselinemodel (meta-stable liquid, without rainout) are notable because thestudy of previous chlorine railcar incidents by Hanna et al. (2008) useda similar set of modelling assumptions. The result could help to explainwhy PHAST (and perhaps the other models) over-predicted the numberof casualties in those incidents.

Fig. 7 shows that PHAST produces lower concentrations when eitherthe flashing discharge model is used or when the rainout model is used.The concentrations are lowest when both flashing and rainout optionsare combined, and this model result is in the best agreement with themeasurements of all the PHAST simulations. However, the model withboth flashing and rainout still over-predicts the measured concentrationat 11 km by a factor of three. One of the reasons for PHAST over-pre-dicting concentrations in the far-field may be due to a limitation of thetime-varying dispersion model in PHAST version 7.11, which does notaccount for along-wind diffusion effects.

Fig. 8 compares predictions of the maximum arc-wise concentrationvarying as a function of distance against the experimental data for allfive of the Jack Rabbit II 2015 trials. Results are only presented for theDRIFT and PHAST metastable liquid discharge models with and withoutrainout. Across all five trials, the PHAST predictions show a similarpattern of behavior where the baseline model without rainout over-predicts the measured concentrations by roughly an order of magnitudeat all distances. The PHAST model with rainout is in much betteragreement with the measurements, although it under-predicts con-centrations in Trial 3 at 1 and 2 km, and over-predicts concentrations inthe far-field at 11 km in the other trials.

For DRIFT, the baseline results without rainout are in betteragreement with the measurements than PHAST, but the model stillover-predicts all of the measured concentrations in the near-field, up toa distance of 1 km. The DRIFT model with rainout gives better overall

Fig. 5. Comparison of model predictions to measurements of vessel discharge in Jack Rabbit II 2015 Trials 1 to 4 (STREAM and PHAST metastable liquid lines lie ontop of each other).

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agreement with the measurements. DRIFT also under-predicts thechlorine concentrations on more occasions than PHAST (with orwithout rainout), notably in the far-field at the 11 km position.However, the degree of under-prediction is relatively modest.

One of the possible reasons for the models over-predicting con-centrations in the near field is that the cloud was relatively shallow andconcentrations varied significantly with height. The measurement datashown in Fig. 8 is from sensors located at a height of 0.3 m (with theexception of the 200m arc in Trial 3, where the sensor was at a heightof 0.7 m) whilst the DRIFT and PHAST model predictions are formaximum concentrations at any height. However, model sensitivitytests showed that outputting the concentrations at a height of 0.3 m hada fairly minor effect in the near-field, and a negligible effect beyond500m. As noted earlier in Section 3, the measured concentration at200m may have under-reported the peak concentration at that dis-tance, due to the sparse coverage of the sensors. Measured concentra-tions in Trials 4 and 5 may also have missed the peak concentration,due to the wind blowing the cloud outside the measurement array.

4.4. Toxic effects

To put the concentrations in Fig. 8 into context, the measured valuesat 11 km are close to 10 ppm, which is the Immediately Dangerous to

Life or Health (IDLH) concentration for chlorine, i.e., the value de-termined by US National Institute for Occupational Safety and Health(NIOSH) to present a level of toxicity that may pose life-threateningeffects to an individual after a 30min exposure. The IDLH is just one ofseveral defined toxicity criteria. The criteria used by HSE to assess risksaround major hazard sites in the UK is based on the toxic load, ratherthan the concentration, which is calculated by integrating over time theconcentration raised to the power of a substance-specific factor,3 whichin the case of chlorine is equal to two. The fact that the concentration issquared in the toxic load calculation for chlorine means that there is agreater impact on the calculated toxic load from exposure to a highconcentration for a short period of time, as compared to a lower con-centration for a longer period.

The Institute for Defense Analysis (IDA) calculated the toxic loadfrom the measured time-varying concentrations at each of the sensors inthe Jack Rabbit II 2015 trials (Fig. 9). In the experiments, the sensorsrecorded concentrations every second, but when a person is exposed toa toxic gas, there will be some time-averaging of the concentration dueto the time-taken to inhale and exhale (and, perhaps, other metabolicfactors). IDA therefore smoothed the measured concentration data

Fig. 6. CFD model predictions for the Jack Rabbit II 2015 trials. Contours at the top show the predicted maximum concentrations at ground level, with black linesshowing the location of the 200m and 500m arcs, and the outline of the mock urban array. Graphs compare the predicted maximum concentrations at the height ofthe sensors (lines) to the measured values (symbols) at two different distances: 200 and 500m. There are two sets of measured concentrations on the 200m arc: fromthe Canary sensors (dark symbols) and the MiniRAE sensors (yellow symbols). (For interpretation of the references to color in this figure legend, the reader is referredto the Web version of this article.)

3 http://www.hse.gov.uk/chemicals/haztox.htm, accessed 11 September2017.

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using either a six second or 1min running-average before calculatingthe toxic load. The difference between using either of these averagingtimes was found to be very minor. To ensure that DRIFT was comparedto these results on an equal basis, simulations were performed using alateral meander averaging time of either 6 s or 1min in the model,which also had a negligible effect on the results. In this context,meander refers to refers to the natural lateral back-and-forth motion of

the wind over time, which tends to broaden dispersing clouds and re-duce time-averaged plume centerline concentrations.

Predictions of the toxic load from DRIFT are compared to IDA'smeasured values in Fig. 9. Two results are shown for DRIFT: with andwithout the rainout sub-model (using the metastable liquid dischargesub-model in both cases). Threshold levels of toxic load are also shownin the graphs as horizontal lines, which represent two criteria that are

Fig. 7. DRIFT and PHAST predictions of the maximum arc-wise concentrations for Jack Rabbit II 2015 Trial 1.

Fig. 8. Maximum arc-wise concentrations from DRIFT and PHAST for the Jack Rabbit II 2015 trials.

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used in the UK for risk assessment: the “Specified Level of Toxicity”(SLOT) and “Significant Likelihood of Death” (SLOD). SLOT equates toapproximately 1% fatalities in the general population, as well as in-juries to others, and severe distress to practically everyone, whilst SLODequates to 50% fatalities in an exposed general population. Inter-polating between the measurements for Trial 1, the SLOD criteria isreached at around 2 km and SLOT at 3 km.

To provide some context to these SLOT and SLOD distances, theEmergency Response Guidebook published by PHMSA (PHMSA, 2016)provides “Isolate” and “Protective Action” distances for multiple toncylinders of chlorine, which are respectively 0.3 and 2.1 km for con-ditions relevant to Jack Rabbit II Trial 1 (light wind speeds and day-time). The Chlorine Institute also provides guidelines on estimating thearea affected by chlorine releases in their Pamphlet 74 (ChlorineInstitute, 2017). The latest version of this guidance (Edition 6) has beenwithdrawn and is currently being updated to take into account thefindings of Jack Rabbit II.

Fig. 9 shows that the general pattern in the DRIFT results is for themodel to over-predict the measurements, especially in the near-field,but to occasionally under-predict the measurements in the far-field. Thesimulations with rainout generally produce lower concentrations,which are in better agreement with the measurements. It should benoted that the toxic load is much more challenging to predict than themaximum concentration, since it requires the model to predict thecorrect time-varying behavior, not just the peak concentration value.

4.5. NCAR model results

Evaluation of the NCAR model with the Jack Rabbit II data has beenconducted for both the analytical and numerical formulations. For theanalytical case, Fig. 10 presents the modeled puff radius versus timeand a comparison of the predicted maximum chlorine concentrationswith observations. Results are shown for the buoyancy-only and mo-mentum-buoyancy sub-models. At short-times (t≤ tr, Fig. 10a), one canclearly see the R ∝ t1/2 and R∝ t3/4 regimes for the two sub-models andthat the source momentum leads to a much larger radial spreading rateand cloud radius R. At long times, both sub-models exhibit the same

R∝ t1/2 behavior.The existence of the momentum- and buoyancy-dominated regimes

at short times is supported by laboratory experiments (Chen, 1980) ofan analogous problem: wastewater outfall plumes in the ocean, whereindustrial waste is released at depths of about 50–100m below theocean surface. For buoyant releases, the vertically-rising outfall plumesproduce radially outward spreading plumes at the surface; such plumesare similar to atmospheric dense gas puffs, except that the buoyancyforce is up instead of down. Chen's (Chen, 1980) experimental datashow a clear region of momentum dominance with R∝ t1/2 at shorttimes followed by a buoyancy-regime with R∝ t3/4 at longer times.

In modelling the chlorine surface concentrations, there are threepoints to note: 1) the maximum concentrations are near-instantaneous,2) ambient turbulence effects on horizontal dispersion are ignored sinceat short-range such turbulence would be most important for puffmeander, not growth, and 3) the integral model is based on top-hatdistributions with the concentration (Cth) converted to the maximumvalue (Cmg=2.8 Cth) of a three-dimensional Gaussian distribution,which is used to predict the maximum concentration (see Appendix A).

At short times (t≤ tr), the chlorine aerosol is assumed to convertrapidly to vapor such that the puff vapor fraction fv= 1 at all times.Fig. 10b presents the modeled chlorine surface concentrations for twovalues of the entrainment parameter (α), which show little differenceexcept for the “bump” in the larger α (0.1) case near x=20m. Webelieve that this behavior and the concentration kink near x= 12m(blue line) are due to the simple method of transitioning from themomentum-to buoyancy-dominated spreading at the time tmb (seeAppendix). Note that the tmb ∝ 1/α and hence is shorter (∼13 s) for thelarger α. These results show the significance of the momentum-spreading in reducing the near-field concentrations from about 20% to50% of the buoyancy-only model (red line) in the nearest 30m down-wind. Unfortunately, there are no Jack Rabbit II observations to assessthese predictions.

At large times (t > tr) or distances, the predictions from the abovetwo models (Fig. 10b) tend to the same result, which shows that thepredictions are typically within a factor of 2–3 of the observations. Inparticular, the region of positive (upward) curvature of the modeled

Fig. 9. Maximum arc-wise toxic load predictions from DRIFT for the Jack Rabbit II 2015 trials.

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C(x) curve over 30 < x < 100m matches the data trend; the upperlimit (100m) is about the distance where the puff centroid (h/2) is atthe canopy top (hc= 2.6m). Thus, for x < 100m, the mean wind isgoverned by the exponential profile (i.e., z < hc), which has a negativecurvature with respect to z; see Fig. 11. Beyond z∼ hc, the wind followsthe logarithmic profile, which is marked by a positive curvature with z(Fig. 11), and the C(x) curvature transitions to a negative value. Thewind profile curvature is important in determining the C(x) curvaturevariations in the results, but this is significant mostly for understandingwind effects on the concentration rather than for practical importance.

One drawback of the analytical modelling noted above is the kink ordiscontinuity in the concentration gradient at tmb due to the change inthe radial spreading rate or vr. This motivated the development of anumerical model for the puff behavior that resulted in a continuousvariation of concentration at short times (Appendix A). Fig. 12 presentscomparisons of the numerical model results for the five trials of the2015 Jack Rabbit experiments for the larger α (= 0.1) case that pro-duced the dC/dx discontinuity in Fig. 10b. As can be seen, there is acontinuous variation of C with x in the near field (t≤ tr), but a kink inthe dC/dx remains at tr due to the change in the source fluxes at tr fromfinite to zero values.

For large distances (x > 50m), Fig. 12 shows that the model resultsexhibit: a) an agreement with the data mostly within factor of 2–3, b) adecrease with x similar to the observations, and c) similar C(x) curva-ture changes discussed above (Figs. 10b and 11). There are three otherfeatures: 1) for x < 50m, the differences in the model-data

comparisons among the 5 trials, 2) for x > 500m, the potential greaterimportance of ambient turbulence on the puff horizontal spreading, and3) the greater possible importance of stability, i.e., stable conditions, onthe Trial 1 results.

At short distances (x < 50m), better model-data agreement occursfor Trials 2 and 3 with higher winds (Uref∼ 4m/s) than in Trials 1, 4,and 5 with lighter winds (Uref < 3m/s). In the latter cases, the pooreragreement may be due to the larger vr/up ratio, where up is the modeledmean horizontal puff speed. For example, in Trial 1 this ratio decreasesfrom 10 at x= 1m to about 1 at x= 50m, suggesting that the transportspeed is probably greater than the local mean wind at short range. Theresult may be a puff displacement to greater downwind distances in agiven time and a shift in the near-field modeled C(x) profile. In addi-tion, the differences in the short-range results may be due to wind di-rection variations from the urban array normal as discussed earlier(Section 4.2) and below. The direction was nearly normal to the arrayin Trials 2, 3 but deviated by about± 18° from the array normal inTrials 1, 4, and 5.

For large distances, x > 500m, the model has some tendency to-wards over-prediction in Trials 2, 4, 5, which may be due to the neglectof ambient turbulence on the puff horizontal spreading. We find thatthe ratio vr/u* decreases continuously with distance, where the ratiocompares the puff momentum and buoyancy spreading rate (vr) to thatof the ambient dispersion driven by the friction velocity. At aboutx=500m, the vr/u* ≈ 1 and thus ambient turbulence becomes moreimportant at larger distances. Note that the ambient lateral dispersion(σy) at long times from either Taylor's (Taylor, 1921) or Batchelor's(Batchelor, 1950) theory predicts σy∝ t½ or the same time dependenceas in the integral puff model.

The Trial 1 data may exhibit the importance of stability or stableconditions especially for x > 200m, where they differ the most fromthe other trials in showing an exceedance of the modeled concentra-tions at those distances. Trial 1 has the lowest Uref (= 2m/s) and moststable Pasquill class, F. In addition, the locus of the observed C versus xfor 100m < x≤ 11 km has a shape similar to a model sensitivity runin which the entrainment velocity we included stability effects by usinga Lagrangian time scale (TL) typical of stable conditions. The we (= dh/dt) characterizes the puff vertical growth or h, and a smaller we ob-tained with the short TL would lead to a smaller h and higher con-centrations as found.

Fig. 12 also includes the CFD model results at the 200m and 500marcs and shows that there is generally good agreement among the NCARand CFD models and data. The agreement is quite good for Trials 2 and3, where the wind direction is within a few degrees (−7° to 4°) of thearray normal direction (Fig. 6). For Trials 1, 4, and 5, the CFD resultsare about a factor of 2 higher than the NCAR predictions, a result at-tributed to the larger deviations (−18° to 18°) of the wind directionfrom the array normal. As discussed earlier, such directions lead tomore of the chlorine release following the urban array “streets” and

Fig. 10. a) Scaled puff radius versus scaled time, and b) maximum arc-wise concentrations from NCAR analytical model for Trial 2 of the Jack Rabbit (JR) II 2015experiment compared with the JR II observations.

Fig. 11. Comparison of NCAR modeled wind profile (blue line) with wind-tunnel (WT) data of MacDonald (Macdonald, 2000) for an aligned array ofcubic obstacles having a frontal area fraction of λf = 0.2. Within the canopy (z/hc < 1), the model overestimates the mean wind by about 0.8u* but has samecurve shape as the data; above the canopy (z/hc > 1), the model agrees wellwith data. (For interpretation of the references to color in this figure legend, thereader is referred to the Web version of this article.)

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exiting the array to form high concentration side lobes shown in Fig. 6.The CFD results are consistent with the data in Trial 1 but not in Trials 4and 5 for reasons discussed earlier (Section 4.2).

Finally, we point out that the long-distance concentration resultsfrom the NCAR analytical and numerical models (Figs. 10b and 12)tend to approximate the slope of the C versus x−5/3 empirical correla-tion found by Hanna et al. (2016b, 2017) and discussed in Section 2.5.

5. Discussion

5.1. Model uncertainties

There are several uncertainties in the dispersion models and theirinputs that should be kept in mind when reviewing the results pre-sented in the previous section; these are discussed first for DRIFT andPHAST, and then for the NCAR model. Regarding the source conditions,the DRIFT and PHAST model predictions presented in Figs. 8 and 9 usedthe metastable liquid discharge sub-model which was shown in Fig. 5 toover-predict the release rate. The sensitivity tests performed for Trial 1(Fig. 7) showed that this discharge model gave rise to consistentlyhigher predicted concentrations in the dispersing cloud with PHAST,and a more complex pattern of behavior with DRIFT.

Secondly, there is uncertainty in the amount of chlorine liquid thatrained-out in the experiments. The models gave predictions that agreedbest with the concentration measurements when it was assumed that allof the liquid in the impinging jet rained out and formed a pool.However, in reality, a fraction of the liquid remained in the dispersingcloud as an aerosol. The latest results from Spicer et al. (Spicer, 2017;Spicer et al., 2017a) indicate that 35% of the mass released rained outin one of the 2016 trials that had the same downwards-directed jetrelease as the 2015 trials studied here. The DRIFT and PHAST resultspresented here with and without rainout should be interpreted asbounding cases.

The wind speed and weather conditions were also constant in theDRIFT and PHAST models, whereas the wind speed increased and the

atmosphere became progressively less stable with time in the experi-ments (see Fig. 2). Downwind concentrations will tend to decreasewhen the atmosphere becomes more unstable, but an increase in thewind speed will probably have the opposite effect for short-durationreleases like those in Jack Rabbit II (Hanna and Chang, 2014). Therecould be complex interactions between the atmospheric stability andwind speed, which may also depend on the duration of the release.

Other sources of uncertainty are the effects of deposition and che-mical reactions, which could decrease the chlorine concentrations inthe dispersing cloud, particularly in the far-field. The fact that DRIFToccasionally under-predicted the concentrations in the far-field suggeststhat either deposition effects were minor or that there may be anothercause to the under-prediction. Factoring-in deposition may worsen theagreement between DRIFT and the measurements in the far-field forthese particular cases.

Many of these factors are being studied in ongoing work. A globalsensitivity analysis is currently being performed at HSE on DRIFT inwhich seven parameters are being varied: the chlorine mass released,the choice of discharge sub-model, the rainout fraction, wind speed,atmospheric stability, temperature, and dry deposition rate. The aim ofthe work is to understand which factors (or combinations of factors)have the greatest influence on the predicted concentrations and to helpput error bounds on the model predictions in the Jack Rabbit II vali-dation exercise. Some preliminary results have recently been presentedby McKenna et al. (2017b) and a more detailed study is being prepared.

One of the challenges in conducting the sensitivity analysis is to putcredible bounds on the input uncertainties. Whilst wind speeds andtemperatures have measured limits, other factors such as the depositionrate are not well quantified. Previous work on the Graniteville chlorinerailcar incident by Buckley et al. (2012) found that simulations pro-duced best agreement with the observed vegetation damage and re-corded health effects when a deposition velocity of 1 cm/s was used.Other modelling work discussed by Hanna and Chang (2008) involvedvarying the deposition velocity from zero to 5 cm/s to study a hy-pothetical chlorine release in Chicago. Laboratory-scale tests by Hearn

Fig. 12. Maximum arc-wise concentrations from NCAR numerical model for the Jack Rabbit (JR) II 2015 experiment compared with measurements and CFD modelresults.

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et al. (2012) measured considerably lower deposition velocities on soilsamples, which suggested that the sparsely-vegetated Dugway playacould have a maximum deposition velocity of around 0.03 cm/s, thatcould also decrease over time due to saturation effects. Further la-boratory-scale work on deposition is ongoing at Arkansas University(Spicer et al., 2017b). An additional complication with deposition isthat some models may have already been tuned to experimental data-sets with deposition (e.g., the Desert Tortoise ammonia experiments(Goldwire et al., 1985)) and therefore to incorporate a separate sub-model for deposition may be to double-account for the phenomenon.

There are other limitations of DRIFT and PHAST that cannot beexamined in sensitivity tests. For example, the models are unable toaccount for the high momentum and turbulence levels present in theimpinging jet. The bifurcated cloud structure in the near-field that wasproduced by the cloud being driven through the street canyons of themock urban array is also not taken into account. One therefore needs tobe careful not to over-interpret the results of the validation exercise.

The NCAR model focus is on proper representation of the puff dy-namics and spreading due to the source momentum and buoyancy aswell as entrainment at the puff top. The source mass flux determinationis simplified by using the measured experimental fluxes, but the treat-ment of the chlorine thermodynamics and vapor fraction (fv) is highlysimplified. This treatment is adequate for this work, but needs con-siderable improvement for studies where measured fluxes are notavailable. Perhaps the techniques used in DRIFT, PHAST, and/or theCFD model can be adapted or modified for inclusion in the NCARmodel.

As identified in Section 4.4, improvements are needed in the NCARmodel treatment of: 1) the puff transport speed (up) in light winds andinclusion of the radial velocity (vr) contribution when the vr/up ratio islarge, as was true in some of the Jack Rabbit II trials; 2) effects ofambient turbulent dispersion characterized by u* when the ratio vr/u* issmall (< 1) and ambient dispersion is about the same as or greater thanthe buoyancy-driven growth; and 3) the mean wind profiles during theinitial stable period of the releases and their evolution to profiles forunstable conditions. The surface layer wind profiles for stable and un-stable conditions are available (Businger et al., 1971) and can be im-plemented. In addition, there are uncertainties in the entrainmentparameter α and gravity-current coefficient af, which must be de-termined experimentally; the Jack Rabbit II 2016 trials should be usefulfor such determination.

The above discussion suggests a need to address dispersion timedependence even when the wind speed is fixed, but the vr/up ratio islarge near the source. This may require modelling a puff by the su-perposition of elemental puffs each with an incremental mass of Fqδtemitted in a time interval δt, where Fq is the source mass flux. Theproblem is analogous to the treatment of dispersion in light winds(Csanady, 1973; Luhar, 2011), but is different in that the radialspreading is driven by momentum or buoyancy instead of turbulentdiffusion.

In addition to the modelling uncertainties, there are uncertainties inthe measurements, as discussed in Section 3. This uncertainty arisesfrom sensors not capturing the cloud centerline concentration and is-sues with sensors reaching saturation levels. In field scale tests such asthese, with short duration releases, there is also a large natural orstochastic variability between trials, even under nominally identicalconditions, due to the effects of atmospheric turbulence (Weil et al.,2012; Hall et al., 1992).

5.2. Model strengths and weaknesses

To summarize the strengths and weaknesses of the various modelspresented here, the CFD model tested by HSE has the advantage ofresolving the complex high-momentum jet behavior and channeling ofthe cloud between Conex containers in the near-field. However, it re-quires lengthy computer run times and there are uncertainties

introduced when simulating far-field dispersion beyond around 1 km,due to errors in the boundary layer profiles that develop beyond thisdistance.

The DRIFT and PHAST integral models tested by HSE provide morepractical tools for regulatory purposes and risk assessment. Both modelsare quick to run in principle, although they required some lengthy post-processing to give the results presented here, in order to take into ac-count the change in roughness length. DRIFT also needed separate runsto model the vertically-downwards jet and subsequent dispersion stagesof the releases. The results from the two models show that they canproduce concentrations in reasonably good agreement with the mea-sured values, although the models are sensitive to the choice of inputparameters (PHAST more so than DRIFT). In particular, PHAST pro-duced concentrations that were roughly an order of magnitude higherthan those measured when it was configured using a similar set of as-sumptions (meta-stable liquid outflow and no rainout) to that used inthe Hanna et al. (2008) study of chlorine railcar accidents.

The NCAR model has advantages over the DRIFT and PHAST modelsin that it accounts for the initial momentum-dominated behavior in thenear-field. The model is quick to run and the results are in goodagreement with the measurements. However, it is limited in its ap-proach for handling the thermodynamics of the two-phase source and itcurrently assumes only a neutral boundary layer profile. Various re-finements to this approach are planned.

5.3. Future work

In addition to the ongoing sensitivity analysis work discussed above,future work will focus on analysis of the data from the Jack Rabbit IIPhase 2 (2016) trials and further validation of models. This modellingwill take into account the recent findings on liquid rainout from Spicer(2017). A joint model inter-comparison exercise is currently underwaywith other members of the MWG. Perhaps as part of that exercise, itwould be useful to assess model performance on a statistical basis, usingcriteria commonly used in model evaluation, such as geometric meanand fractional bias, along the lines of previous work conducted on theModelers Data Archive (Hanna et al., 1993).

With respect to further development of the NCAR model, there areseveral paths that can be explored. An ultimate goal is to link the in-tegral modelling presented here with a Lagrangian particle model (Weilet al., 2004, 2012) to produce probabilistic concentration predictions.Other paths include: 1) different source conditions such as the ventingorientation and accounting for the thermodynamics of two-phase flow,2) other meteorological scenarios, e.g., stable wind profile, 3) cloudmodelling by superposing incremental puffs to treat the large initial vr/up ratios and improve near-source predictions, and 4) a new metho-dology to account for different puff spreading rates in the lateral anddownwind directions in urban areas and for the street canyon align-ment. This could involve collaboration with HSE by tuning the NCARintegral model coefficients to results from HSE building-resolving CFDsimulations.

An important final goal for HSE is to identify the root causes for themodel over-predictions observed in the chlorine incidents studied byHanna et al. (2008) and to develop greater confidence in the use ofmodels for chlorine releases, with an improved understanding of theimpact of modelling uncertainties.

6. Conclusions

A range of modelling approaches of different complexity have beencompared to data from the Jack Rabbit II Phase 1 (2015) experiments.The experimental data from Trials 1 to 5 were analyzed to determinemaximum arc-wise concentrations and to identify measurement issues,such as sensors saturating or plumes bypassing sensors. Dischargemodel predictions from STREAM and PHAST were presented using twodifferent sub-models for the flow through the orifice. The results

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indicated that meta-stable models tended to over-predict the measuredrelease rate, whilst flashing models under-predicted the release rate.

The flow behavior in the near-field was examined using CFD, whichshowed that the chlorine cloud was initially directed laterally, out ofthe sides of the mock urban array, due to the alignment of Conexcontainers. The trends in the plume widths predicted by the CFD modelwere consistent with observations from the experiments.

The behavior of the dispersing cloud further downwind was ex-amined using DRIFT and PHAST, with results presented in terms of bothconcentration and toxic load. In general, the models appeared to over-predict the measured values, particularly in the near-field, with the bestagreement obtained using DRIFT and PHAST sub-models that took intoaccount the rainout of liquid from the impinging two-phase jet.However, it should be noted that many of the measurements may haveunder-recorded the peak concentration, due to sensors saturating andthe clouds bypassing sensors.

The development of the NCAR integral model was presented anddifferent options were tested to account for the transition from mo-mentum to buoyancy-dominated behavior. Promising results were ob-tained on puff spreading due to source momentum and buoyancy andagreement was found between the modeled maximum concentrations,CFD results, and Jack Rabbit II data for Trials 1 to 5. In addition, theNCAR puff model reproduced the observed −5/3 power law decay inconcentration at large distances.

Whilst the work presented here shows that significant progress hasbeen made in interpreting the Jack Rabbit II Phase 1 data and in un-derstanding model performance, further analysis is needed to examine

the second phase of the experiments in 2016 and to understand in moredetail the model sensitivity to uncertainties in the input parameters.These efforts are continuing and a collaborative international modelinter-comparison exercise on Jack Rabbit II is currently underway.

Acknowledgements

The authors would like to express their sincere thanks to the orga-nizations responsible for funding and managing the Jack Rabbit II trials(primarily DHS and DTRA) and the MWG coordinators and participants;in particular to Shannon Fox (DHS), Ronald Meris (DTRA), RichardBabarsky (US Army), Thomas Mazzola and John Magerko (Engility),Steven Hanna (Hanna Consultants), Joseph Chang (RAND), ThomasSpicer (Arkansas University), Nathan Platt, Jeffry Urban and KevinLuong (IDA), John Boyd (ARA), Steven Herring (DSTL), Andy Byrnes(UVU) and Mike Harper (DNV-GL).

The contributions of HSE staff to this paper were funded solely byHSE. The contents, including any opinions and/or conclusions ex-pressed, are those of the authors alone and do not necessarily reflectHSE policy. The contributions of NCAR staff to this paper were fundedby the US Defense Threat Reduction Agency.

GT Science & Software contributed towards the work on DRIFT, andDNV-GL Software contributed towards the work on PHAST, but theDRIFT and PHAST simulations presented in this paper were performedby HSE and have not been independently checked by the software de-velopers.

Nomenclature

Abbreviations

ARA Applied Research AssociatesCFD Computational Fluid DynamicsCOMAH Control Of Major Accident HazardsCSAC Chemical Security and Analysis CenterDHS Department of Homeland SecurityDNV-GL Det Norske Veritas – Germanischer LloydDRIFT Dense Releases Involving Flammables or ToxicsDSTL Defence Science and Technology LaboratoryDTRA Defense Threat Reduction AgencyGASP Gas Accumulation over Spreading PoolsHSE Health and Safety ExecutiveIDA Institute for Defense AnalysesJIP Joint Industry ProjectMWG Modelers Working GroupNCAR National Center for Atmospheric ResearchPHAST Process Hazard Analysis ToolPWIDS Portable Weather Instrumentation Data SystemsRAND Research ANd DevelopmentSRD Safety and Reliability DirectorateUDM Unified Dispersion ModelUKAEA UK Atomic Energy AuthorityUVU Utah Valley UniversityEquations

C concentrationCmax maximum concentration at distance xCth puff top-hat concentrationF puff buoyancy at time tFb source buoyancy fluxFi initial puff buoyancyFm source radial momentum fluxFT total puff buoyancyFv source volume fluxQ source mass emission

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R puff radiusRi initial puff radiusRi* puff Richardson numberU mean wind speed at height zUh mean wind speed at canopy topUref mean wind speed at reference heightV puff volumeaf model constant, of order 1d displacement heightg gravitational accelerationg′ enhanced gravitational accelerationh puff heighthc canopy heighthi initial puff heightk von Karman constantp wind profile exponentP pressurePh surface hydrostatic pressureP′ perturbation from the background environmental pressuret timetr source durationtmb time of transition from the momentum-to buoyancy-driven spreadingu* friction velocityvr average radial velocityvrb average radial velocity in the buoyancy-dominated regionvrm average radial velocity in the momentum-dominated regionvri initial average radial velocitywe entrainment velocityx downwind distance from sourcez height above ground levelzref reference heightz0 roughness heightα entrainment parameterβ empirical parameter for radial pressure gradientγ attenuation or decay coefficient in the wind profileρ densityρa ambient densityρi initial puff density

Appendix A. NCAR integral model: Equations and assumptions

The integral equations for an instantaneous or short-duration dense gas release are presented below using a puff control volume consisting of ashort cylinder of radius R and height h, which evolves with time t. They are derived in much the same manner as the equations for a buoyant plumeby Weil (Weil, 1974; Weil, 1988) except that the unsteady terms in the equations of motion are retained. The integral equations include theBoussinesq approximation (Turner, 1979), which assumes that density differences can be ignored everywhere except in the buoyancy force term.This approximation is valid except at very early times or near the source, where the fractional density difference (ρ - ρa)/ρa is larger than∼ 0.1.

In the early time regime, the motion is dominated by the source radial momentum flux Fm, and the source density ρI is included in Fm as

F ρ ρ v R h( / )2m i a ri i i2

= (A.1)

where subscripts “i” and “a” denote source and ambient values, respectively, and vr is the radial velocity. The inclusion of source density in Fmcompensates somewhat for its neglect in the integral equations for the early time behavior. This follows a similar treatment for buoyant plumes(Weil, 1988; Briggs, 1984).

With the above approximations, the equations for the conservation of volume (or mass), radial momentum, and buoyancy are given by

d R h dt F αv R( )/ v r2 2= + (A.2)

d v R h dt F( )/r m2 = (A.3)

d R hg dt F( )/ b2 ′ = (A.4)

where

g g ρ ρ ρ( )/a a′ = − (A.5)

g is the gravitational acceleration, and g′ is the “enhanced” gravitational acceleration since ρ > ρa. For buoyant puffs and plumes, g′ is referred to asthe “reduced” gravitational acceleration since the density inequality is reversed. This analysis (Eq. A.4) applies to a neutral environment.

The αvrR2 term in Eq. (A.2) is the flux of entrained air or ambient air entrainment by the puff across its top surface area (∝ R2) due to the velocityshear, which is caused by the high radial velocity; the entrainment parameter α is assumed to be in the range 0.02 < α≤ 0.15. The upper value

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(0.15) is of the order of values found for jets (Hoult and Weil, 1972); currently, we adopt α=0.1. The Fv and Fb are the volume and buoyancy fluxesfrom the source and are assumed to be constant over the source duration time tr or as long as the source is “on” and zero for t > tr; the sameassumption is adopted for Fm.

As noted in Section 2.4, the model is divided into short-time (t≤ tr) and long-time (t > tr) regimes with the short-time region further split intomomentum- and buoyancy-dominated ranges. There are two model formulations and solution methods for dealing with the momentum-buoyancytransition, analytical and numerical; the analytical approach is presented first.

A.1. Analytical model and method for momentum-buoyancy transition

A.1.1. Short times, t≤ trIn the momentum-dominated range, Eqs. (A.2) and (A.3) are solved at relatively long times, but for t < tr and where the entrainment in (A.2)

dominates the volume change due to Fv. Using vr= dR/dt and ignoring buoyancy (Eq. A.4) and Fv, we find that the solution to the volume equationat long times is

h α R( /3)= (A.6)

By substituting Eq. (A.6) and vr (= dR/dt) into Eq. (A.3) and integrating, we obtain

R α F t(6/ ) m1/4 1/4 1/2= (A.7)

and find that the puff volume V varies as

V=R2h = (α/3)¼ (2Fm)¾ t3/2 (A.8)

These results show that the radius decreases with an increase in α (as α−1/4), the height increases as α3/4, and the puff volume increases as α1/4,which makes sense physically. In the momentum regime, the vr decreases as t−1/2 and ultimately the buoyancy-induced velocity exceeds that due tomomentum. The time tmb at which the momentum- and buoyancy-induced velocities are equal is determined later.

The buoyancy-driven spreading can be predicted from another simple approach with vr based on a gravity-current model (Britter, 1989; Simpson,1982; Weil et al., 2002) where

v dR dt a g h/ ( )r f1/2= = ′ (A.9)

and af is a constant of order 1. We also modify the puff volume equation (A.2) by considering entrainment due to ambient turbulence, where theentrainment velocity we is given by the Briggs et al. (2001) parameterization:

we=0.65u* /(1 + 0.2Ri*) (A.10)

Here, Ri* is the puff Richardson number

Ri*= g′h/u*2= F/(u*2 R2) (A.11)

and F (= R2hg') is the cloud buoyancy at time t. The Ri* includes the stabilizing effect of cloud buoyancy in reducing the mixing due to shear-driventurbulence, which is characterized by the friction velocity u*.

The cloud buoyancy found by integrating Eq. (A.4) is given by

F R hg F F t if t ti b r2= ′ = + ≤ (A.12)

and

F F F F t if t tT i b r r= = + > (A.13)

where it has been assumed that Fi « Fbtr, Fi is the initial cloud buoyancy, and FT is the total cloud buoyancy over the source duration.In the buoyancy-dominated regime, the puff volume is found using the larger of the two entrainment velocities, we= αvr or we from Eq. (A.10),

in the volume rate equation

d R h dt F w R( )/ v e2 2= + (A.14)

which can be integrated to yield

R2h=Ri2hi + Fvt + ∫ we R2 dt if t≤ tr. (A.15)

The integral on the right-hand-side of (A.15) is the one exception to an all analytical model and solution method for the momentum-buoyancytransition; a similar equation for t > tr is given below. The puff volume is necessary to estimate the puff concentration.

For t≤ tr, the radius can be found by equating vr= dR/dt and Eq. (A.9), substituting Eq. (A.12) into the result, and integrating to obtain

R2=Ri2 + (4af /3Fb) [(Fi + Fbt)3/2 – Fi3/2] (A.16)

this simplifies to

R = (4af /3)1/2Fb1/4 t3/4 (A.17)

for sufficiently large t such that Fi « Fbt and Ri « R, where Ri is the initial puff radius.Within the short-time regime, we find the time tmb separating the momentum- and buoyancy-dominated regions by equating the radial velocity vr

(= dR/dt) determined from the approximate equations for the puff radius: Eqs. (A.7) and (A.17). These velocities are given by the following for themomentum (vrm) and buoyancy (vrb) regions:

vrm = (3/8α)1/4 Fm1/4/t1/2 (A.18)

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vrb = (3af /4)1/2 Fb1/4/t1/4 (A.19)

The time tmb obtained by equating Eqs. (A.18) and (A.19) is

tmb= 2/(3αaf2) Fm /Fb (A.20)

For α=0.1 and af= 0.9, the value found to match the large-distance concentration data in the Jack Rabbit II experiments, the coefficient in(A.20) is 8.2.

A.1.2. Long times, t > trAt long times or when t > tr, the radius is found to be

R R a F t t2 ( )r f T r2 2 1/2

= + − (A.21)

where Rr=R (t= tr) and is evaluated from Eq. (A.16). The puff volume is obtained by integrating Eq. (A.14) and is

R2h=Rr2hr + ∫ we R2 dt if t > tr, (A.22)

where the volume at tr, Rr2hr, is evaluated from Eq. (A.15).

We also can estimate the asymptotic dependence of h on t at large times using Eqs. (A.21) and (A.22) assuming that R » Rr and h » hr. From (A.21),we have R2∝ t and from (A.22) we have R2h∝ t2, since we is essentially constant given that Ri*∼ 0 at long times when R is very large; see Eq. (A.11).This analysis yields h∝ t which is consistent with model calculated results at large times.

A.2. Numerical model and method for momentum-buoyancy transition

In the numerical model, the basic equation set is Eq. (A.14) for puff volume, Eq. (A.3) for momentum but with the addition of a radial pressuregradient, and Eq. (A.4) for puff buoyancy. Thus, the main difference from the analytical model is the pressure gradient term (dP'/dr) in themomentum equation, which reads

d(vr R2h)/dt= Fm – ∫ (1/ρa) (dP'/dr) dV (A.23)

where P′ is a perturbation from the background environmental pressure P, and the integral is over the puff volume. We assume that P′∝ ΔPh, thesurface hydrostatic pressure difference between the puff and environment at the puff center, ΔPh =(ρ - ρa)gh, the puff pressure being greater due toits higher density. Furthermore, we assume that the “effective” or average radial pressure gradient scales as dP'/dr= - βΔPh/R= -β(ρ - ρa)gh/R, andis negative since the pressure decreases with increasing radial position or r, and β is an empirical parameter.

By replacing the pressure gradient in Eq. (A.23) by the effective dP'/dr above, we find that the momentum equation can be rewritten as

d(vr R2h)/dt= Fm + βg'R2h ⋅ h/R (A.24)

or

d(vr R2h)/dt= Fm + βFh/R (A.25)

which includes the puff buoyancy, F, directly and the puff aspect ratio, h/R. The puff buoyancy is given by Eqs. (A.12) and (A.13) for two timeregimes.

The momentum equation (A.24) or (A.25) includes the cloud buoyancy as an additional forcing of the radial momentum, which is made possibleby the pressure gradient-buoyancy link. This results in a smooth transition for radial spreading initially dominated by momentum to spreading drivenby buoyancy. The parameter β is obtained by matching the puff spreading and volume at long times to the results from the analytical formulation forthe buoyancy regime and depends on the parameter af (= 0.9); the β is found to be 2.2 for af= 0.9.

A.3. Source and initial puff conditions

For the Jack Rabbit II experiments, the chlorine release is assumed to consist of a main cloud containing 90% of the emission with the remaining10% assumed to rain out and pool, leading to a delayed release that is not modeled. Thus, the source mass modeled in the main cloud is 0.9 of thereleased amount. This is fairly consistent with recent measurements (Spicer, 2017; Spicer et al., 2017a), which suggest that the airborne chlorinefraction is about 0.8 after a time of 25s–59s from the start of the release. The cloud chlorine aerosol is assumed to convert rapidly to vapor such thatthe puff vapor fraction is 1 at all times; this assumption is made for simplicity and because our interest is in the maximum surface chlorine vaporconcentrations.

A.4. Maximum concentration and gravity-current parameter af

The maximum modeled concentrations are based on the maximum concentration, Cmg, of an equivalent three-dimensional Gaussian distribution

Cmg= 2Q/[π (2π)1/2 σr2 σz] (A.26)

where Q is the puff mass, and r and z are in the ranges: 0≤ r < ∞ and 0≤ z < ∞. Here, equivalent means that the zeroth and second moments ofthe top-hat and Gaussian profiles are the same. One can verify that Eq. (A.26) is the correct form by integrating the assumed distribution to obtain

∫ ∫ 2πr Cmg exp[-(r2/σr2) - (z2/2σz2)] dr dz=Q (A.27)

where the integration is from 0 to ∞ in both integrals. The Cmg can be written in terms of Q, R, and h by using the equivalent second moments of thetop-hat profiles: σr2 =R2/2 and σz2= h2/3. Substituting these values into Eq. (A.26) gives

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Cmg = (24/π)1/2 Q/(π R2 h)≅ 2.8 Q/(π R2 h)= 2.8 Cth (A.28)

and

Cmax= 2.8 Cth (A.29)

Equation (A.29) serves as a convenient reference for the maximum concentration but clearly is a semi-empirical prediction in that: 1) we do notbelieve that the released puff has a Gaussian distribution, and 2) the viability of the predicted Cmax depends on 1/R2 and the value of af chosen sinceR2∝ af at long times. For the chosen af (0.9), Eq. (A.29) is a reasonable prediction of the maximum concentration as shown in Figs. 10b and 12.

The adopted af is close to the lower value, 1.1, found for an analogous or inverted problem of lateral dispersion of a highly-buoyant plume at thetop of a convective boundary layer (CBL) (Weil et al., 2002). In that work, the issue is estimating the lateral (sideways) spreading of a plume that istrapped by an elevated inversion, which acts like the ground in the dense gas problem. The earlier study (Weil et al., 2002) used a gravity-currentmodel for the one-dimensional spreading and found that af ranged from about 1.1 (for R) in laboratory experiments to 2.3 for field observations. Webelieve that the af difference between the two problems is caused mainly by the surface roughness which would impede the radial spreading of thedense-gas puff relative to a plume at the CBL top, i.e., a free surface, which offers less resistance to spreading. Thus, the af should be lower for theground-based puff than the inversion-limited plume as found. Another difference is the one-dimensional spreading for the plume versus two-dimensional spreading for the puff.

As a final test, we compare the predicted radius from the NCAR model, RNC, with an approximate radius determined from the CFD results, RCFD,using plots of the CFD concentration versus angular position (Fig. 6). The RCFD is approximated by RCFD= x⋅sin (Δθ/2), where Δθ is the total angularextent of the CFD concentration distribution. Figure A.1 shows that the model radius correlates well with the CFD results from the JR II arcs atx= 200m and 500m although it is about 25% higher than the CFD “radius.”

Figure A.1. Puff radius from NCAR model versus approximate radius from CFD results; solid line for equal values, RNC=RCFD, and dashed line for RNC= 1.26 RCFD.

A.5. Mean wind profile and surface roughness parameters

The modeled wind profile pertains to flow over the mock urban array during neutral conditions and consists of an exponential profile for z < hcand a logarithmic profile for z≥ hc, where hc is the urban canopy top or obstacle height in the array. The exponential profile is given by

U(z)=Uh exp [γ (z/hc - 1)] (A.30)

where γ is the attenuation or decay coefficient, and Uh is the mean wind at the canopy top. The logarithmic profile is given by

U(z) = (u*/k) ln [(z -d)/z0] (A.31)

where d and z0 are the displacement and roughness heights and k (= 0.4) is the von Karman constant.In Eq. (A.30), the γ is found by matching the mean wind and its gradient (dU/dz) at z= hc (Pereira and Shaw, 1980). This results in

γ=1/{(1 – d/hc) ln[(1 – d/hc)/(z0/hc)]} (A.32)

which depends only on the ratios z0/hc and d/hc and results in a continuous variation of U(z) and its gradient at hc. For the mock urban array, the z0/hc and d/hc were determined from the MacDonald et al. (1998) approach for obstacle arrays. The mean wind at z= hc obtained from Eqs. (A.30) and(A.32) is

Uh= (u*/k) ln [(1 – d/hc)/(z0/hc)] (A.33)

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