RR1100 - Evaluation of the DRIFT gas dispersion model ...

Evaluation of the DRIFT gas dispersion model version 3.6.4

Prepared by the Health and Safety Executive

RR1100 Research Report

© Crown copyright 2017 Prepared 2014 First published 2017 You may reuse this information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view the licence visit www.nationalarchives.gov.uk/doc/open-government-licence/, write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email [email protected]. Some images and illustrations may not be owned by the Crown so cannot be reproduced without permission of the copyright owner. Enquiries should be sent to [email protected]. This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

The Health and Safety Executive (HSE) uses gas dispersion modelling in its assessment of the hazards and risks posed by toxic and flammable substances stored at major hazards sites. To update its dispersion modelling capability, HSE recently commissioned ESR Technology to develop a new version of the gas dispersion model DRIFT (Dispersion of Releases Involving Flammables or Toxics). The new version of the model, DRIFT Version 3 (DRIFT 3), includes a significant number of modelling enhancements over the version of DRIFT previously used within HSE (DRIFT 2.31). These include the extension of the model to treat buoyant plumes and time varying releases. Prior to DRIFT 3 being adopted for use by HSE, it must undergo thorough evaluation and assessment.

This report describes the evaluation of DRIFT version 3.6.4 in accordance with a Model Evaluation Protocol originally developed for the evaluation of liquefied natural gas (LNG) vapour dispersion models. The protocol sets out a method of scientific assessment, verification and validation for heavy gas dispersion models where the results are recorded in a model evaluation report (MER). Overall, the evaluation exercise found DRIFT version 3.6.4 to be fit for purpose.

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Simon Coldrick and David Webber Health and Safety Executive Harpur Hill Buxton Derbyshire SK17 9JN


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ACKNOWLEDGEMENTS The authors would like to thank Graham Tickle and James Carlisle of ESR Technology for their helpful and timely responses to queries and useful discussions.

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EXECUTIVE SUMMARY The Health and Safety Executive (HSE) uses gas dispersion modelling in its assessment of the hazards and risks posed by toxic and flammable substances stored at major hazards sites. To update its dispersion modelling capability, HSE recently commissioned ESR Technology to develop a new version of the gas dispersion model DRIFT (Dispersion of Releases Involving Flammables or Toxics). The new version of the model, DRIFT version 3, includes a significant number of modelling enhancements over the version of DRIFT previously used within HSE (DRIFT 2.31). These include the extension of the model to treat buoyant plumes and time varying releases.

Objectives

The objective of the project was to carry out an evaluation of DRIFT version 3.6.4 in accordance with a Model Evaluation Protocol for liquefied natural gas (LNG). The protocol sets out a method of scientific assessment, verification and validation for heavy gas dispersion models where the results are recorded in a model evaluation report (MER). The validation stage is carried out by running the model against a database of dense gas dispersion experiments and calculating a number of Statistical Performance Measures (SPM). These SPM are then compared against performance criteria for an acceptable model.

Main Findings

DRIFT version 3 represents a major revision of the model to address a number of shortcomings of the original model and add some new features. The instantaneous and steady continuous release models of the original DRIFT have been extended in a fairly straightforward (but nevertheless also fairly intricate) way to allow clouds to become buoyant. The models for transient and unsteady release models also add some new ideas and assumptions, but a number of details would benefit from further explanation in the specification. In the validation stage, DRIFT predictions generally compared well with the experimental data. SPM values within recommended levels were obtained for most of the comparison groups.

In summary, the advantages of DRIFT are as follows:

• The model runs very quickly. • It can output, on request, the evolution of a very large number of variables, allowing

easy qualitative understanding of what it is predicting.

• Documentation is available which covers model specification, user guide and validation.

• The model is based on a large amount of scientific research through the 1980s and early

90s.

Important aspects of the model to be aware of are:

• The model is complex. In attempting to get a physically well-founded understanding of the behaviour of heavy clouds into the form of an integral model (whose solutions can be found very rapidly on a computer) the original DRIFT was a more complex model than most of its contemporaries. The extended scope of DRIFT 3.6.4 follows the same aims and it is more complex still.

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• During the course of this review, it became apparent that care needs to be taken in relation to some output variables and how these relate to the model itself. For example, some output relates to the cloud before any post-processing calculations have taken place and therefore gives rise to what appear, at first sight, to be erroneous results.

• Pool evaporation results may be imported from the GASP model and are treated only as

“time varying” by default. This results in a more limited range of output from the user interface.

• In common with other similar models, DRIFT only accounts for flat terrain, though

modest variations in terrain can have a significant effect on dense gas dispersion.

• Additionally, this review identified that the finite duration and time varying models would benefit from further explanation in the documentation.

Overall, this evaluation exercise has shown DRIFT to be fit for purpose. The findings of this report have been used to inform guidelines on the use of DRIFT to ensure that the model is used correctly and to give sensible and appropriate advice.

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CONTENTS

1 INTRODUCTION ..................................................................................... 8 1.1 An overview of DRIFT .............................................................................. 8 1.2 The model evaluation process ................................................................. 8 1.3 Report layout ........................................................................................... 9

2 MODEL DESCRIPTION ........................................................................ 10 2.1 Current version ...................................................................................... 10 2.2 Scientific basis ....................................................................................... 10 2.3 The atmosphere ..................................................................................... 10 2.4 The dispersion model structure .............................................................. 11 2.5 User-oriented aspects of model ............................................................. 18

3 VERIFICATION ..................................................................................... 19

4 VALIDATION ......................................................................................... 20 4.1 Previous validation exercises................................................................. 20

5 VALIDATION UNDER MEP .................................................................. 21 5.1 Validation cases modelled ..................................................................... 21 5.2 Physical comparison parameters ........................................................... 23 5.3 Validation case descriptions .................................................................. 26 5.4 Model performance for key statistical evaluation parameters ................ 34 5.5 Evaluation against quantitative assessment criteria .............................. 35 5.6 Sensitivity studies .................................................................................. 38

6 PASSIVE RELEASES ........................................................................... 41 6.1 Selection of datasets ............................................................................. 41 6.2 Hanford experiments ............................................................................. 41 6.3 The Prairie Grass experiments .............................................................. 41 6.4 Test cases modelled .............................................................................. 42 6.5 Results ................................................................................................... 42 6.6 SPM results ........................................................................................... 45

7 CONCLUSIONS .................................................................................... 46

8 REFERENCES ...................................................................................... 48

9 APPENDIX 1: DRIFT MODEL EVALUATION REPORT ....................... 51

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1 INTRODUCTION

1.1 AN OVERVIEW OF DRIFT

1. DRIFT (Dispersion of Releases Involving Flammables or Toxics) is a gas dispersion tool intended for use in hazard analysis where a user may be interested in the consequences of a release. The model was developed by the Safety and Reliability Directorate (SRD) of the United Kingdom Atomic Energy Authority (UKAEA) on behalf of the Health and Safety Executive (HSE) in the late 1980s and early 1990s. Later, part of UKAEA became ESR Technology and that company is currently responsible for the code. The original model was for heavy or passive clouds released instantaneously or in a steady continuous source. It has been well validated against a range of experimental data. The current version of the model extends the scope in a number of ways. It embraces lighter-than-air clouds (previously, the model would halt upon detection of this condition), and not only instantaneous and steady continuous releases but also steady releases of finite duration and other transient releases. The extension to buoyant plumes necessitates a more complete description of the atmosphere than was present in the original DRIFT. This is provided in terms of the various length scales and regions of the atmosphere which may affect the behaviour of buoyant plumes as well as ground-based heavy clouds.

2. The instantaneous and steady continuous release models are easily recognisable from the original DRIFT but now allow clouds to transition between dense, passive and buoyant behaviour in a seamlessly continuous way. Structurally the models are the same as the original DRIFT and the extra details in the model which provide the generalisation to allow buoyant clouds are probably as simple as they can be, commensurate with providing a reasonable model.

1.2 THE MODEL EVALUATION PROCESS

3. There have been a number of studies producing guidance for the evaluation of a model (or models) so that an objective review may be carried out. These studies include the project by Hanna et al. (1991), the MEG (Model Evaluation Group) (MEG 1994a,b) and the SMEDIS project (Carissimo et al., 2001 and Daish et al., 2000). The evaluation protocol used in this review is that set out by Ivings et al. (2007). The work of Ivings et al. is based upon the SMEDIS project but is specific to the dispersion of Liquefied Natural Gas (LNG). The work is not confined to the modelling of LNG spills as other, simpler cases should also be taken into account in model evaluation. The Model Evaluation Protocol (MEP) by Ivings et al. (2007) consists of three stages:

• Scientific assessment: Is the mathematical basis of the model sound?

• Verification: Is the computer implementation of the mathematical model sound?

• Validation: Do the model and its implementation provide sufficiently accurate results?

4. The results of the three stages are recorded in a Model Evaluation Report (MER). Scientific assessment is carried out by a reviewer with an in-depth knowledge of dense gas dispersion. The information required by the assessor is gained using a questionnaire completed by the model developer or proponent. Verification under the MEP is treated under the scientific assessment and evidence of verification is recorded in the MER. The aim of validation is to determine if the model’s predictions are representative of experimental observations. This is performed by comparing model output with observations from a number of experimental datasets. One of the recommendations of the MEP by Ivings et al. (2007) was that the validation should be performed by running models against an experimental database. In 2008, HSL undertook a project for the US Fire Protection Research Foundation (FPRF) to construct such a validation database (see Coldrick et al., 2009). This report describes the evaluation of DRIFT and its validation using the validation database. The MER produced as a result of the exercise is located in Appendix 1. Much of

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the content of the MER has been incorporated into this report, with further expansion of some aspects. Furthermore, due to the enhancements made to DRIFT, in particular the passive dispersion aspects, the validation exercise has been extended to cover passive releases and these are covered in a separate section of the report. The results of the passive dispersion validation have not been included in the MER, as the MER is specific to dense gas dispersion.

5. The scenarios in this review have been based mainly upon dispersion of releases from evaporating pools - reflecting the experiments included in the dense gas validation database. The validation presented can therefore be considered to apply directly to such releases and be relevant also for ground based dispersion of low momentum clouds. Explicit validation of the jet model which is newly incorporated within DRIFT is therefore excluded from this study. Further validation studies testing the jet models within DRIFT are therefore desirable covering aspects of elevated and ground based jet dispersion. Existing experimental data and correlations as well as new datasets (e.g. carbon dioxide jet releases) might be useful here.

6. This review is based upon the supplied documentation describing the mathematical models used in DRIFT, in addition to a meeting with the current developers. One of the authors of the review (D M Webber) was involved in the development of the original version of DRIFT and completed the scientific assessment. As suggested by Ivings et al. (2007), verification was treated passively as part of the scientific assessment and evidence of verification was obtained from the documentation. The validation studies in this report were carried out by a third party (S Coldrick) and therefore constitute a “blind test” of the model. However, this aspect was not completed in isolation and the developers were contacted on various aspects of running the model.

1.3 REPORT LAYOUT

7. Sections 2, 3 and 4 give an introduction to DRIFT, the basis of the model and the verification performed to date.

8. Section 5 describes the validation database, including the steps undertaken in processing the experimental data. Section 5 continues with the application of DRIFT to each dataset, the results obtained and a discussion of the overall model performance.

9. Section 6 describes a validation exercise where DRIFT is compared to a number of passive tracer releases.

10. Conclusions of the report are presented in Section 7.

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2 MODEL DESCRIPTION

2.1 CURRENT VERSION

11. Recently, ESR Technology reissued DRIFT incorporating a number of modifications allowing a wider range of scenarios to be modelled. These modifications are summarised in the document “DRIFT Version 3 Mathematical Model” (Tickle and Carlisle, 2012) including, for example, buoyant lift-off and rise and extension to multicomponent materials.

2.2 SCIENTIFIC BASIS

12. DRIFT is an integral model for instantaneous, steady continuous or time dependent releases of a heavy, passive or buoyant gas or aerosol. It can model multi-components based upon the assumption of either an ideal solution or complete immiscibility in the liquid phase (or a combination of these). Additionally, there are specific interacting mixture models for hydrogen fluoride and ammonia. DRIFT’s thermodynamic module accounts for heat of vaporisation and (in the case of ammonia and hydrogen fluoride) heat of solution. The basic model is derived in terms of a set of coupled differential equations (in time for instantaneous releases, and in downstream distance for steady continuous releases). In the instantaneous case the independent variables are cross-wind and down-wind radii (the cloud has an elliptical plan), down-wind advection momentum, temperature and the number of moles of the five components in the cloud. Cloud volume and height are derived quantities. In the steady continuous release model cloud width, temperature, and down-wind advection momentum are independent variables, together with the fluxes of the various components. Sub-models are included for entrainment rates and heat transfer.

13. The model equations are based broadly on the assumption of self-similar profiles which allow the formulation of equations for “integral properties” of the cloud. The profiles assumed evolve smoothly from the dense gas phase into the passive regime, and can assume the forms of “top hat”, Gaussian, and exponential in various limits. Entrainment is modelled as a function of a bulk Richardson number (being the most appropriate dimensionless measure of whether the cloud is heavy or passive). Some of the sub-models (for example entrainment and its dependence on Richardson number) were formulated in a way that was guided by best practice in earlier models and were tuned in the process of validation. Other sub models (for example the obstacles) were formulated as a result of research sponsored by HSE and the CEC, (Commission of the European Communities) in which collaborators produced data that enabled validation to be done.

2.3 THE ATMOSPHERE

14. Heavy gas dispersion occurs typically within the “surface layer” – the lowest part of the atmospheric boundary layer. This can be characterised by the friction velocity u*, the atmospheric roughness length z0, and, for non-neutral stability, the Monin-Obukhov length, L. An appropriate description of the atmosphere taken from the literature is given in the original DRIFT documentation (Webber et al., 1992). Generalisation of DRIFT to encompass buoyant plumes requires a model of the atmosphere above the surface layer. Tickle and Carlisle (2012) present such a description, again extracted from the literature, for the whole atmospheric boundary layer. This is a more complicated description of the atmosphere than the original but is necessary to model a plume leaving the ground. The atmospheric model is clearly specified and further discussion here is unnecessary.

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2.4 THE DISPERSION MODEL STRUCTURE

2.4.1 Instantaneous releases

15. In the broadest possible terms the instantaneous model is a set of ordinary differential equations with time t as the independent variable:

)(vqdtdv

ii = (1)

in which vi is the i’th variable and qi, a function of all the v’s, models its rate of change. The variables in question represent the cross wind dimension of the cloud, the dimension along the wind, the depth of the cloud, its centroid position, content and assorted other properties. [Depending on how one views the various variables, it may be considered that there are other independent variables (than just the vi) related by algebraic constraints which render them expressible (in principle) in terms of the vi, but we shall not concern ourselves with that level of detail here.]

2.4.2 Steady continuous releases

16. In the case of steady continuous releases the independent variable is the distance sc along the cloud centre line, and the equations are of the form:

)(vqdsdv

ic

i = (2)

with different (but related) independent variables vi and functions qi.

2.4.3 Other releases

17. The models for other types of release are discussed in Sections 2.4.8 and 2.4.9.

2.4.4 Overview

18. At this level it is clear that the construction of the model (and quite generally any integral model) involves • Formulating the description of the cloud in terms of a number of variables • Defining sub-models for the evolution of those variables.

The variables and sub-models for the instantaneous and steady continuous model appear to be both well-defined and comprehensive in the DRIFT 3 specification.

2.4.5 Treatment of ground-based and elevated clouds

19. A novel feature of DRIFT 3 is that it can model both heavy gas clouds and buoyant plumes. The technique by which this is achieved deserves particular mention here. First, the variables describing the cloud (see above) are chosen to be such that they can describe a heavy cloud on the ground, or an elevated buoyant one. These include the height ‘z’ of the centroid above the ground, and a measure ‘a’ of the cloud height. A quantity 2)/(2 az

g ef −= (3)

is defined so that a completely ground based cloud corresponds to fg=1 and a completely airborne one corresponds to fg→0, as shown in Figure 1. The quantities ‘z’ and ‘a’ evolve according to the equations of the cloud, so that evolution between ground based and elevated states is smooth and mathematically

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uncomplicated. In the definitions of the sub-models (the functions q) there are numerous instances where some property X is modelled in a form which can be written schematically as

aggg XfXfX )1( −+= (4) where Xg models the behaviour of the property for ground-based clouds and Xa models the behaviour for airborne clouds. The carefully constructed models for ground-based and airborne clouds can therefore be obtained by going through the specification and replacing fg by 1 or 0 respectively. The process of a cloud lifting off from (or attaching to) the ground is not considered separately, but is simply modelled with an intermediate value of fg and therefore comprises a simple interpolation (simple in the model equations, though probably less so in the solution) between the ground-based and airborne clouds. This reviewer’s view is that with careful modelling of ground-based and airborne clouds, this is likely to be a very effective technique.

Figure 1 Treatment of ground based and elevated plumes

2.4.6 General comments on the models

20. The instantaneous and steady continuous release models are easily recognisable from the original DRIFT but now allow the cloud to transition between dense, passive and buoyant behaviour in a seamlessly continuous way. Structurally the models are the same as in the original DRIFT, and the extra details in the model which provide the generalisation to allow buoyant clouds are probably as simple as they can be, commensurate with providing a reasonable model.

2.4.7 Upwind spreading

21. Under low wind conditions, dense gas releases from an area source may result in a certain amount of spreading upwind of the source and the original version of DRIFT would halt upon detection of this condition. In the original version, the plume gravity spreading rate was given by:

22f

fgravity

UU

U

−=β (5)

where Uf is the gravity spreading velocity and U is the plume velocity. Clearly, this expression is not valid when U is less than Uf. In DRIFT version 3, the gravity spreading rate is more simply defined by:

Elevated plume fg→0 Ground based plume fg=1

z

a

a

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UU f

gravity =β (6)

and a new sub-model is incorporated to determine the extent of upwind spreading. This involves running the instantaneous model until a steady state has been achieved to determine a new source condition and then evolving the plume from the new source. This can be likened to a more precise version of the practice of manually diluting the source and calculating a new source dimension from which to run the dispersion model. The upwind spread model is active by default in the “include dilution over source” option and specific validation of this feature would be desirable if suitable data could be identified.

2.4.8 Steady releases of finite duration

22. The specification of the model for steady releases of finite duration is a new aspect. The following conclusions therefore follow an explicit discussion with the developers, focussing specifically on this aspect. Tickle and Carlisle (2012) built a transient release model (for a release which is steady over a period, and shut off thereafter) as follows.

1. Consider the steady plume as obtained from the continuous model. 2. Let the time taken for a parcel of fluid to go from the release point to a distance s along the centre

line be t(s). This function can be evaluated from the steady plume model. 3. Define the inverse function s(t). This is the distance along the centre line travelled by a parcel of

fluid in a time t since its release. 4. Now consider a release which is steady for a period T starting at time t=0 and shut off thereafter.

To a first approximation, at time t the front is considered to be at a distance: )(tss f =

and the back (the trailing edge) is considered to be at

TtTtsTtsb

≥−=<=

)(0

23. In this first approximation the prediction for a transient release is a cloud which is a section of the steady plume, with the front (and after time T the back) advancing with time, as shown in Figure 2:

s

sf sb Cm(s)

Figure 2 A finite duration segment from a continuous release

24. This “cut out” section could be obtained by some function, P(s, sf(t), sb(t)), which is applied to the centreline concentration, Cm(s): ))()(,()(),( tstssPsCtsC bfm= (7)

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25. If the effects of longitudinal diffusion can be ignored then a transient plume with square ends is obtained by setting the function, P, to be the Heaviside Step Function (Figure 3A). In the limit of large duration T→∞, and large time t→∞ (keeping t<T), the section becomes the whole plume and the steady plume is recovered. It should be noted that this is not how a steady plume develops, but for many purposes it may be a useful approximation. However, this results in sharp back and front ends on the transient plume; a smoother behaviour is required. The approach adopted by Tickle and Carlisle (2012) involves a “convolution” (merging) of the sharp cloud profile with a normalized instantaneous profile, I:

)())(),(,( ssIdststssP c

sf

sbbf −≡ ∫ (8)

where P(s, sf(t), sb(t)) becomes a smoothing function, rather than a step function (the step function manifests itself in the limits of the integral in Equation 8). Note that the amount of material along the centre line is preserved according to the area property of the convolution integral. If the concentration varies slowly except at the sharp ends of the section, the bulk will not be strongly affected, but the sharp ends will be smoothed out (Figure 3B).

Figure 3 Representation of concentration (c) with distance (s) in a finite duration plume in DRIFT

C

S sb sf

C

S sb sf

C

S sb sf

A

B

C λb λc λf

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26. Tickle and Carlisle (2012) introduce a final modification to allow the front and back end smoothing functions (i.e. Pf and Pb) to be different. The resulting concentration profile is then determined using an interpolation procedure in which the concentration at a point is a function of interpolation λ values (Figure 3C) which are weighted according to distance from the plume ends.

27. A fairly straightforward test can be carried out on the finite duration model by running it with increasing durations and comparing the results to those from the continuous release model. This is shown in Figure 4 where the Thorney Island 45 trial (see Section 5.3.4) was run for three release durations and the steady state model was run with and without source dilution activated. It can be seen that the results tend toward the steady continuous model as the duration is increased. One notable difference between the results is that the profile from the steady state model with source dilution shows a lower initial concentration than the other profiles. This is an artefact of the presentation of the results in the DRIFT GUI where, for all runs of the finite duration model, the results from the tabular output are the maximum over time and therefore show concentrations over the source of 100%. For the steady continuous model with source dilution the tabular results in DRIFT are presented once the steady state has been reached and the required amount of source dilution has been calculated, hence a lower value is displayed.

Figure 4 Concentration profiles for differing release durations

2.4.9 Time varying releases

28. DRIFT’s time varying model is based on dividing the release into a number of segments each of which is treated as a finite duration release using the method described above. The time varying plume is then assembled by summing all the finite duration segments. Tickle and Carlisle (2012) define this in shorthand using the following expression:

∑=

+=N

iiimi sssPsCtsC

11 ),()(),( (9)

where si+1 and si are the front and back ends of the ith segment and Cmi is the steady state concentration prediction for that segment. Tickle and Carlisle (2012) note that the implementation of Equation 9 in

1

10

100

1000

10000

100000

1000000

0 500 1000 1500 2000

Con

cent

ratio

n (p

pm)

Distance from source (m)

Steady state

Finite duration 30 s



Steady state no source dilution

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DRIFT uses the interpolation technique described in Section 2.4.8 but this is not included in the description for brevity.

29. The essence of the finite duration and time varying models is that a steady plume can be divided into segments which independently advect downstream, gaining smoothed ends in the process. In the case of the time varying model, these independent segments are then reconstituted into a plume by addition. However, as information is not shared between the segments as they disperse, the reconstituted plume will not necessarily give a uniformly varying concentration profile and anomalies may appear in the results. Tickle and Carlisle (2012) acknowledge this noting that if the source strength varies slowly, then it can be shown that a time varying plume assembled from segments does sum to the finite duration result. For release segments that vary greatly, particularly when considering dense gases where each segment’s dispersion behaviour differs, there is the possibility of generating artefacts in the summed concentration profile.

30. DRIFT allows a user to specify the minimum time for each segment of a time varying release. This makes it possible to change the number of sections a time varying release is split into. A useful test of the model is therefore to run it with different numbers of segments and compare the results to a continuous release. This is shown in Figure 5 for the Burro 3 LNG release trial (see Section 5.3.2). In this trial, the source was not steady but featured two peaks in the vaporisation rate. The time varying model was run using the default of three segments and 1 segment and compared with a fixed input. In practice, there is very little difference between the results for this release; slightly higher concentration is obtained from the time varying model. When a single segment is used for the time varying model, the results are close to the steady continuous model. The discrepancy is likely to be due to the slightly different averaging methods used in GASP and DRIFT.

Figure 5 Comparison of differing numbers of segments in the time varying model

31. In constructing a time varying plume from a number of steady state segments, there will be an unavoidable dependence on the discretisation. This is because sections from a set of steady state plumes whose concentration is derived as a function of distance and air entrainment (amongst other things) will not necessarily behave in the same way as a truly time dependent plume. However, Tickle and Carlisle (2012) offer an extension to the steady state model which is conceptually straightforward and avoids the

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

0 500 1000 1500 2000 2500 3000 3500 4000

Distance from source (m)

Conc

entra

tion

(ppm

)

Time varying 3segments

Time varying 1 segment

Steady continuous

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complexities that could be introduced in modelling a time dependent plume. The general approach appears to be sound and quite elegant in many ways and this reviewer would agree with Tickle and Carlisle (2012) in that it would be difficult to improve on. The main comment on the time varying and finite duration models is therefore in their specification in the documentation which would benefit from further detail.

2.4.10 Time averaging

32. In the original version of DRIFT, the treatment of time averaging was somewhat ad-hoc. In the passive regime, fitting to particular dispersion experiments meant that the predicted concentration represented an average over typically 10 minutes. In the dense gas regime, fitting to concentrations averaged over a shorter period (in the order of seconds) resulted in a different prediction. Given the use of the model, this approach was not deemed to be inappropriate. DRIFT version 3 incorporates a time averaging function which is applied in post-processing to determine the concentration profiles over a user-specified averaging time. The time averaging function employed in DRIFT is the Langevin type plume meander model described in Nielsen et al. (2002) and which has been employed by other modellers. This modifies the concentration profiles based upon the ratio of the user input averaging time to the plume travel time and the wind speed. As with the dilution over source model, the implementation of the meander model in DRIFT is another area that would benefit from a specific validation exercise.

2.4.11 Treatment of obstacles

33. The ability to handle numerous obstacles is perhaps unusual for an integral model. Several sub-models were developed as a result of CEC sponsored research and are documented in Webber et al. (1994). These cover the interaction of a dense gas cloud with a downstream fence or individual obstacle such as a building and the dispersion of dense gas from the wake of a building. These models were based on a large number of observations from wind tunnel testing and are implemented as simple “virtual source” models in DRIFT. When a dense gas cloud encounters a fence, the calculation procedure is as follows: 1) Evolve the profile of the cloud as if the fence were not present. 2) Continue until the height of the cloud reaches the fence height at the fence location, without reporting the results. 3) Use the new cloud properties at the fence location as a virtual source and compute the evolution of the cloud downstream of the fence from that source.

34. The downstream building model assumes that obstacles take a cuboidal form. There are two “dilution conditions”: one based upon the height of the building and the other based upon the eddy size. The latter assumes the effect of the building is to cause eddies which mix the cloud with air, increasing its size. As with the fence model, the gas cloud is evolved until one of these “dilution conditions” is reached. When this happens, the cloud in the position immediately upstream of the obstacle is replaced with a modified one. The upstream building model works on the principle that releases into the wakes of buildings are modified by the turbulent flow induced by the building. This modified flow is reintroduced as an initial condition into the calculation in the near wake of the obstacle. Although the fence and building models are conceptually simple, they have been shown to agree well with wind tunnel measurements. In cases where the models differed from the data, they were found to be conservative (Webber et al., 1994).

2.4.12 Solution of the equations

35. DRIFT employs the “DDRIV3” solver from “CMLIB” for the resulting governing equations. A variable step ODE solver is used, solving a mixed set of both first order differential equations and purely algebraic equations. Temporal steps are used for instantaneous releases; spatial steps are used for steady continuous releases.

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2.5 USER-ORIENTED ASPECTS OF MODEL

36. DRIFT 3.6.4 can be operated in a number of ways. DRIFT files can be created and run using the graphical user interface (GUI), or the model can be run entirely from the “COM” (Walton, 2011) interface via a Microsoft Excel spreadsheet to which results can be output. A third option (and the one used for this review) is that DRIFT files set up using the GUI can be run in batch using the COM interface. This allows output not otherwise available from the GUI to be extracted.

37. DRIFT runs are straightforward to set up using the GUI as there is context sensitive help and the various input options are covered in the user manual. A large amount of output is available from the GUI in terms of plots of flammable or toxic limits and tabulated data. Some care is needed when examining the tabulated output as certain output variables are not subject to the effect of the user input cloud meander averaging time (a note of this is made in the user manual). This is also relevant to some of the output obtained using the COM interface. For example, “cloud” output variables are treated slightly differently to “slice” output variables. This is perhaps more relevant in the context of this review than for risk assessment purposes where there is less of a requirement for non-standard output. In particular, the format of the model evaluation database uses concentration and cloud width output at fixed downstream distances and this is not directly available from the DRIFT GUI. Similar care also needs to be exercised when using the finite duration and time varying models. Like the time averaging function, these models are applied in post processing so that their effect is not accounted for in some of the tabulated output.

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3 VERIFICATION

38. Verification is the process of checking that the computer implementation of a model matches its mathematical basis. Under the MEP of Ivings et al. (2007) verification is treated passively as part of the scientific assessment instead of as an exercise in its own right. Therefore, evidence of verification is obtained from the developer and documentation and is recorded in the model evaluation report. Verification of DRIFT version 2.31 is discussed in detail by Jones et al. (1993). The method used was essentially:

• examination of graphs of a large number of variables plotted against one another to check that the results are “qualitatively sensible”;

• deeper follow-up tests where results were unexpected.

39. These tests included comparison of results with approximate analytical solutions – for example:

• the expected increase of the top area of an instantaneously released cloud with time; • the down-wind advection velocity; • the asymptotic power law decrease of concentration; • the integrated thermodynamic balance in the cloud; • behaviour of height and width in continuous releases with momentum.

40. Verification of DRIFT version 3 is discussed in Tickle et al. (2011a,b). The first of these documents is a comparison of DRIFT version 3 against version 2.31 and several experimental datasets. This is presented as a verification check that the models broadly agree where expected. The second document covers two aspects: the lift-off of buoyant puffs in low wind and thermodynamic modelling of HF-iso-butane mixtures. DRIFT’s buoyant puff rise model is based upon that of Turner (1973) and comparison with an analytical solution to Turner’s model is used as a check of DRIFT’s computer implementation. Tickle et al. (2011b) demonstrate that DRIFT predictions for HF mixtures are in agreement with a thermodynamic model for HF mixtures.

41. It is also noted that the instantaneous and continuous release modules share a lot of common code so that DRIFT’s verification in one scenario carries over to the other. It is believed that sufficient verification was carried out to give confidence that the equations have been programmed correctly.

19

4 VALIDATION

4.1 PREVIOUS VALIDATION EXERCISES

42. This Section is a brief review of previous validation exercises carried out during the development of DRIFT. The validation following the LNG MEP is described in the next Section.

43. Validation of DRIFT 2.31 is discussed in detail by Jones et al. (1993) and by Jones et al. (1994a). A more detailed validation of the concept of homogeneous equilibrium of aerosol clouds is given by Kukkonen et al. (1993, 1994) and later validation for hydrogen fluoride clouds is discussed by Tickle (2001).

4.1.1 Instantaneous releases

44. Jones et al. (1993) compare DRIFT extensively with the Thorney Island trials (isothermal instantaneous releases). Jones et al. (1993) also present comparison with other models including DRIFT’s predecessor DENZ and with the Britter-McQuaid Workbook (Britter and McQuaid, 1988).

4.1.2 Continuous releases

45. Jones et al. (1993) compare DRIFT with the continuous release field trials conducted in Lathen by TÜV. Both momentum free and jet releases were included. These trials also provide data on concentrations as a cloud passes a transverse fence downwind of the source and were used to validate the fence model previously described – see Webber et al. (1994). Jones et al. (1993) also compare DRIFT with the Lyme Bay chlorine trials from 1927, though the data are limited, and with the Nevada Desert “goldfish” trials with HF. It is worth noting that none of the free parameters in DRIFT needed be set differently to fit continuous and instantaneous releases.

46. Tickle et al. (2011a) present comparisons of DRIFT version 3 against DRIFT version 2 and a range of experiments covering dense and passive releases including two-phase ammonia and hydrogen fluoride.

20

5 VALIDATION UNDER MEP

47. This section describes the validation of DRIFT that has been carried out as part of the current work in accordance with the Model Evaluation Protocol set out by Ivings et al. (2007).

5.1 VALIDATION CASES MODELLED

48. The validation cases were provided by the database described by Coldrick et al. (2009). The database holds sufficient information on a range of test configurations (datasets for field and wind tunnel trials including release conditions, meteorological data, etc.) to permit model set-up and simulation. The database also contains test results in the form of tabulated concentration and temperature data against which model output can be compared. A total of 33 individual trials are included in the database covering dispersion over water and land, with and without obstacles. The database is not limited to LNG and includes dispersion of heavy gases such as freon 12 and sulphur hexafluoride (SF6). A number of wind tunnel tests are also included in addition to the field trials. The wind tunnel studies provide increased environmental control over their full scale counterparts and therefore result in useful validation datasets. However, wind tunnels cannot reproduce the atmospheric conditions and heat transfer effects that govern full scale releases, so are limited to isothermal releases.

49. DRIFT is intended for modelling releases over flat terrain and with simple obstructions such as fences and buildings. Many of the test cases in the database can be simplified to fit in with these capabilities without introducing excessive approximations and assumptions. However, not all the test cases in the database are applicable to the model evaluation. Table 1 lists the specific validation cases modelled.

21

Table 1 Test cases modelled to validate DRIFT 3.6.4 Trial Field (F)

or Wind tunnel (WT)

Obstructed (O) or unobstructed (U)

Trial/Case number and/or description

Maplin Sands, 1980

F U U U

27 dispersion over sea 34 dispersion over sea 35 dispersion over sea

Burro, 1980 F U U U U

3 7 8 9

Coyote, 1981 F U U U

3 5 6

Falcon, 1987 F O O O

1 with vapour barrier fence 3 with vapour barrier fence 4 with vapour barrier fence

Thorney Island 1982-4

F U U

45 continuous release 47 continuous release

CHRC, 2006 WT U O O

A without obstacles B with storage tank & dike C with dike

BA-Hamburg WT U O O O

Unobstructed Upwind fence Downwind fence Circular fence

BA-TNO WT U FLS – 3-D mapping

50. The field trials are primarily releases of LNG (Maplin Sands, Burro, Coyote and Falcon), other than the Thorney Island trials which are releases of Freon 12. The latter set of trials includes releases in stable atmospheric conditions, whereas the LNG field trials data are largely restricted to neutral or unstable conditions. All of these field trial releases are over unobstructed terrain, with the exception of the Falcon trials in which a large fence surrounded the LNG source. The wind tunnel tests comprise recent work undertaken at the Chemical Hazards Research Center at the University of Arkansas as well as two series of tests carried out as part of European Commission-funded projects, referred to as BA-Hamburg and BA-TNO. Tables 2 and 3 summarise the field and wind tunnel trials, respectively.

Table 2 Field trials summary Maplin Sands Burro Coyote Falcon Thorney

Island Material LNG LNG LNG LNG Freon

12/nitrogen Maximum amount spilled

3658 kg 17289 kg 12676 kg 28074 kg 4855 kg

Maximum spill rate

27.1 kg/s 135 kg/s 129 kg/s 202 kg/s 10.67 kg/s

Maximum duration

160 s 174 s 98 s 301 s 465 s

Source Spill onto water pool

Spill onto water pool



Vapour source

22

Table 3 Wind tunnel trials summary CHRC BA-Hamburg BA-TNO Material CO2 SF6 SF6 Maximum spill rate

0.001056 kg/s 0.000872 kg/s 0.001045 kg/s

Maximum duration

Continuous Continuous Continuous

Source Area vapour source

Area vapour source

Area vapour source

5.1.1 Excluded cases

51. A number of the test cases in the database are outside the scope of DRIFT and no attempt was made to model these cases or approximate them into a form that could be modelled. Four of the BA-Hamburg wind tunnel trials are releases onto sloping terrain. These releases were carried out in conditions of zero wind speed, which, along with non-flat terrain, are not treated in DRIFT. Two BA-TNO datasets are not included due to the nature of the arrangement of the concentration sensors.

5.2 PHYSICAL COMPARISON PARAMETERS

52. The physical comparison parameters (the parameters comparing the model predictions against the measured data) used in the database are those recommended by Ivings et al. (2007) as follows:

• Maximum concentration across an arc at radius/distance ‘x’ from the source. • Cloud width across an arc at radius/distance ‘x’ from the source.

53. The above parameters are not measured directly during experiments but are derived from arrays of concentration data obtained over the duration of the release. The process of converting the basic concentration data into the physical comparison parameters should be given consideration when assessing the performance of a model.

54. An ideal continuous release trial would result in a uniform set of steady state concentrations at various distances downwind from the source. In practice, a time series of varying concentration values is obtained for each concentration sensor over a time period covering the release. These “instantaneous” concentration values are recorded, for example, every second for the release duration. Inspection of the instantaneous concentration over the release duration typically shows cloud arrival followed by a concentration peak and then cloud departure. Over this period, the instantaneous concentration may fluctuate considerably. In order to convert these time dependent values into a steady state representation of the cloud, some processing is required. The processing method of Hanna et al. (1991) was adopted for the database and is described in Coldrick et al. (2009). For some of the datasets, concentration values are presented for two averaging times. The first is a “short” averaging time (e.g. 1 second) giving a peak instantaneous value and the second a “long” averaging time (e.g. 100 seconds) giving the peak averaged value for the release duration. These two concentration values may differ by an order of magnitude and raise the question of which best represents the state of the cloud at any given moment. Ideally, when comparing model results against the measured values, the averaging time used by the model should correspond to that for which the measured values are presented.

5.2.1 Calculation of cloud width

55. The second physical comparison parameter, the cloud width across a measurement arc, has been determined where possible in the database. The width is calculated using the following formula for the standard deviation of a frequency distribution (Pasquill, 1983):

23

22

2

−=∑∑

∑∑

MMY

MMY

yσ (10)

where M is the concentration recorded at each sensor and Y is its crosswind displacement. The values of M have been taken as the long time average values. Cloud width was not calculated for all the datasets in the database. In some cases, where the cloud was bifurcated or badly aligned with the sensor array, the calculation of cloud width was not meaningful. For a number of the wind tunnel trials, the concentration data were not available over a 2D array. The constraints used to calculate the cloud width are set out by Coldrick et al. (2009).

5.2.2 Source modelling

56. The Thorney Island trials and the wind tunnel trials feature a well defined vapour source which is suitable for input directly into DRIFT as the contaminant vapour was emitted from a source of known dimensions. However, DRIFT is a dispersion model and not a source model and so in the other cases, additional modelling is needed to be used to provide the source term – e.g. from vaporising pools. The approach used in each case is discussed below.

57. For continuous releases of LNG onto water, it can be assumed that the pool spreads out to a given diameter and reaches a steady state. The evaporation rate (kg/m2/s) can be equated to the spill rate (kg/s) to determine the pool radius for input into DRIFT. This method relies wholly on accurate determination of the evaporation rate that is specific to each situation (and indeed on the validity of the popular assumption that the equilibrium of spill rate and vaporisation rate is stable). However, when the LNG is delivered onto the water as a downward jet, there is considerable mixing of the liquids that will increase the heat transfer.

58. In the Maplin Sands trials, the spill pond was of sufficiently large diameter as to allow unconstrained spreading of the LNG pool on the water. For these trials, Puttock (1987) lists an evaporation rate of 2x10-4 m3/m2/s (0.085 kg/m2/s), but it is not clear how this was obtained. The method of LNG delivery onto the water was through a pipe discharging vertically downwards into the water without a splash plate. The Burro and Coyote trials at China Lake used a similar method of LNG delivery differing only in that a splash plate was fitted slightly below the pool surface. This has the effect of directing the LNG outwards onto the pool surface and therefore decreasing the mixing. For these trials, Koopman (2004) states that the evaporation rate was 0.17 kg/m2/s which is double that estimated for the Maplin Sands trials.

59. In addition to this basic method of estimating the pool diameter, the program GASP (Gas Accumulation over Spreading Pools) version 4.2.3 (Webber and Jones, 1989) was used to model the source for the unbunded spills onto water. GASP describes the spreading of evaporating or boiling liquid pools on land or water and the results can be imported into DRIFT (a predicted vaporisation rate for the Burro 3 trials is shown in Figure 6). By default, GASP runs are imported as “time varying” releases. Alternatively, GASP runs can be exported as steady continuous releases by creating a legacy .DIN DRIFT input file which gives the average vaporisation rate and pool radius. These can be imported into DRIFT 3.6.4 as a legacy input file. For this review, all GASP runs were imported using the legacy input file method to enable the finite duration model to be used.

24

Figure 6 GASP prediction of Burro 3 source

60. All the LNG spills were modelled as pure methane. Although LNG is typically 95% methane, the main effects of the impurities are generally only considered to be significant in the late time period when the methane has boiled off preferentially. Throughout the boiling process, a further effect of impurities is to suppress film boiling which is much more readily achievable with pure methane. Accordingly the GASP runs did not include film boiling.

61. DRIFT version 3.6.4 permits a certain amount of upwind plume spread from low momentum area sources through “initial dilution over the source” (Tickle and Carlisle, 2011). This feature is active by default and was used for all the low momentum area source runs, see Section 2.4.7.

5.2.3 Atmosphere

62. Three schemes are available in DRIFT for specifying atmospheric conditions: Monin-Obukhov, Pasquill and Holtslag. Both Monin-Obukhov and Pasquill conditions are listed for all the field trials in the validation database. DRIFT uses Monin-Obukhov length and friction velocity as working parameters and estimates these values if the Pasquill scheme is used. There is some uncertainty over the most appropriate values of Pasquill stability class for any particular day and given that a length scale and friction velocity can be directly input, the Monin-Obukhov scheme was used for all the field trials. The wind tunnel trials were conducted in neutral stability conditions and where a friction velocity was available, the Monin-Obukhov scheme was used together with a reciprocal length scale of zero (to represent an infinite length).

5.2.4 Wind tunnel and large trial modelling

63. All the wind tunnel trials were modelled at wind tunnel scale and were treated as isothermal releases consisting of only contaminant gas and dry air. The wind tunnel releases differ from the field trials in that they are truly continuous releases with little temporal concentration variation. For this reason, the DRIFT runs were carried out with time averaging set to 1 second.

25

5.2.5 Extracting output from DRIFT

64. As indicated in Section 2.5, DRIFT files can be created and run using the GUI or from Microsoft Excel using the “COM” interface. All the runs for this review were initially set up manually using the GUI and run so that any error messages arising during the simulation could be examined. An Excel spreadsheet application was then written to batch run all the files and extract the required output. The two outputs required for entry into the model validation database are the concentration and cloud width parameter (σy) at specific downstream distances and averaging times. Whilst tabulated output is available through the GUI, it is not possible to directly obtain values at specific downstream distances, hence the necessity to use the COM interface.

65. Since the treatment of time averaging in DRIFT 3.6.4 is applied in post processing and does not affect all the output variables, some care is required in obtaining output. For the finite duration and steady state releases, concentrations were obtained using the DRIFT variable “maximumConcAtDistanceAndReceiverHeight” and cloud width, σy, using the DRIFT variable “slice.OutputValue(ContinuousOutputVariable_SigmaY_Meander).” These variables are subject to the effect of averaging time. However, contact with DRIFT’s developers indicated that σy as computed in this way does not account for the effects of longitudinal diffusion. The suggested alternative was to obtain the maximum half width to e-0.5 times the centreline concentration. This returns σy for a Gaussian profile and is a good approximation in the dense regime. Cloud width data were not available for all the water spills, so in practice it was only necessary to use this alternative for the Burro and Coyote trials.

5.3 VALIDATION CASE DESCRIPTIONS

5.3.1 Maplin Sands

66. The Maplin Sands trials were conducted by Shell Research limited in 1980 and consisted of 34 spills of liquefied gases onto the sea (see Puttock et al., 1982 and Colenbrander et al., 1984 a,b,c) The aim of the trials was the study of combustion and dispersion of flammable gases. Both continuous and instantaneous releases of propane and LNG were carried out. The spill point was 350 m offshore and liquid was delivered along a pipeline from the gas handling plant. A 300 m diameter dike was constructed around the spill point to retain water so that trials could be conducted at low tide. The water level behind the dike varied by 0.75 m. At the spill point a 150 mm diameter pipe directed LNG vertically downward onto the water surface. For the three trials modelled, a splash plate was not fitted under the pipe so that the jet of LNG was free to penetrate into the water, resulting in increased mixing. No measurements were made of the pool diameter and there is some uncertainty over the evaporation rate given the increased mixing of the LNG and water. The two methods discussed in Section 5.2.2 were used to derive the vapour source; the first assumed the evaporation rate was equal to the spill rate (hence leading to a source diameter), the second was to run GASP and import the results. Due to the level of uncertainty in calculating an evaporation rate, the GASP-DRIFT predictions were used in the database.

67. The Maplin Sands concentration data are listed in the database for a “short” 3 second averaging time and this was applied to the DRIFT predictions. The results are shown in Figure 7. The Maplin sands trials were carried out in neutral conditions other than trial 27 which was carried out in slightly unstable conditions. Closer agreement was obtained for this trial. DRIFT underpredicted the maximum arcwise concentration in all cases but closely replicated the decay of concentration from the source.

26

a

b

c

Figure 7 Measured and predicted maximum arcwise concentration for Maplin Sands

5.3.2 Burro and Coyote

68. The Burro series of experiments were performed at the Naval Weapons Center (NWC), China Lake, California in the summer of 1980 (see Koopman et al., 1982a,b). There were eight spills of between 25 m3 and 40 m3 LNG onto water. The Coyote series (see Goldwire et al., 1983) followed a similar format and were intended for the study of vapour burn and Rapid Phase Transition (RPT) explosions that had been observed in the Burro trials. For both the Burro and Coyote trials, a 25 cm diameter line ran from the spill valve to a water test basin where it terminated 1.5m above the surface of the water basin. A splash plate was fitted below the outlet to limit penetration of the LNG into the water. The water test basin had an average diameter of 58 m and a water depth of approximately 1 m. The water level was approximately 1.5 m below the surrounding ground level. The terrain downwind of the spill pond sloped upward at about 7 degrees for 80 m before levelling out to an approximately 1-degree slope. No attempt was made to account for the level of the water test basin and the terrain was assumed to be flat. This is valid for distances greater than 80m where the majority of the sensor arcs were positioned.

69. As with Maplin Sands, two estimates were made of the source area. A splash plate was fitted, so it could be expected that this would decrease mixing and hence the evaporation rate. Both means of calculating the evaporation rate resulted in overprediction of gas concentrations. The GASP-DRIFT predictions were entered in the database.

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Maplin Sands 35

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70. The concentration measurements for both the Burro and Coyote trials are presented in the database for two averaging times whilst cloud widths are only available for the longer averaging time. DRIFT runs were carried out for both averaging times and the results are shown in Figure 8. In DRIFT version 3.6.4, the plume meander model used to determine the time averaged concentration is only applied in the passive phase. The two averaging times therefore resulted in almost identical concentration predictions. Burro trials 7 and 9 were carried out in neutral conditions whilst the conditions for trials 3 and 8 were slightly unstable and slightly stable respectively. Relatively good agreement was obtained for the Burro 7 and for the Coyote trials 3 and 5 in slightly unstable conditions. The Coyote 6 trial was carried out in neutral conditions and DRIFT overpredicted concentration.

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g

Figure 8 Measured and predicted maximum arcwise concentrations for Burro and Coyote for different averaging times

5.3.3 Falcon

71. The Falcon trials were a series of large scale LNG spill tests carried out by the Lawrence Livermore National Laboratory (LLNL) (see Brown et al., 1990). The trials were carried out at Frenchman Flat, an extremely flat playa (salt flat) with little vegetation. The trials had the purpose of evaluating the effectiveness of vapour fences as a mitigation technique for accidental releases as well as providing a dataset for model validation purposes. The spills were onto a specially designed water pond equipped with a water circulating system to maximise evaporation. LNG was supplied to the spill area along pipes by means of nitrogen drive gas. The spill pipes terminated in a multi exit “spider” to provide a uniform distribution of LNG at the spill pond. The spider consisted of four arms of 11.6 m length, each arm fitted with a capping level with the pond water surface to direct the LNG horizontally. The spill pond was 40 m by 60 m and filled to a depth of approximately 0.76 m. An 8.7 m high vapour fence structure 44 m by 88 m surrounded the spill pond, with the spill pond located at the downwind end. Immediately upwind of the spill pond was a 13.3 m high and 17.1 m wide “billboard” structure intended to generate turbulence typical of a storage tank within the vapour fence.

72. The process of dispersion from within the fenced area is not straightforward due to the effects introduced by turbulence in the wake of the upwind “billboard”. Whilst it may be possible to estimate the vaporization rate from the pond, the rate at which vapour leaves the fenced enclosure is altogether less certain. The simplest approach to modelling the Falcon trials with DRIFT is to assume the entire fenced area forms the source. The vapour can then be assumed to leave this fenced area at a rate equivalent to that spilled. Alternatively, the source may be set as the spill pond area and the fence model in DRIFT used to account for the downwind section of the vapour barrier fence. Both methods were tried and resulted in almost equal concentration and cloud width predictions. The values produced by the former method were entered in the database.

73. DRIFT predictions were made for the two averaging times presented in the database and the results are shown in Figure 9. As with the Burro and Coyote trials, both averaging times resulted in almost identical concentration predictions. The Falcon trials were carried out in neutral/slightly stable conditions other than trial 1 which was in stable conditions. Relatively good agreement was obtained for Falcon 3, but DRIFT tended to overpredict concentration for Trials 1 and 4, particularly in the near field. The reason for this is not clear.

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a

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Figure 9 Measured and predicted maximum arcwise concentrations for the Falcon trials

5.3.4 Thorney Island

74. The Heavy Gas Dispersion Trials at Thorney Island were carried out by the Health and Safety Executive for the study of the dispersion of fixed volume releases of heavy gas (McQuaid and Roebuck, 1985 and McQuaid, 1987). The programme was subsequently extended to include continuous release trials 45, 46 and 47. The test area was largely clear for a length of 2 km and a width of 500 m and flat to within 1 in 100. Gas was ducted below ground from a 2000 m3 container to the release position. The release position consisted of a vertical duct emerging at ground level with a 2 m diameter cap situated 0.5 m above the ground. This arrangement provides for a well defined vapour area source for input directly into DRIFT. The gas used in the Thorney Island trials was a mixture of Freon 12 and nitrogen. This was approximated in DRIFT as pure inert gas with its molecular weight set to reflect that of the mixture.

75. The release durations for the Thorney Island trials were typically several minutes, however the concentration data are presented for a 30 second averaging time. The results are shown in Figure 10. Relatively good agreement was obtained for these trials and this may be due in part to the fact that DRIFT’s parameters were originally set using these trials. DRIFT’s tendency to underpredict concentration in the near field was reduced if the runs were carried out with dilution over source switched off.

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Figure 10 Measured and predicted maximum arcwise concentrations for Thorney Island

5.3.5 CHRC

76. The Chemical Hazards Research Center (CHRC) at the University of Arkansas carried out wind tunnel experiments of the dispersion of CO2 over rough surfaces, with and without obstacles (Havens and Spicer, 2005, 2006, Havens et al., 2007). The experiments were at a scale of 150:1 and consisted of low momentum area sources with the following three geometries:

• Case A: without obstacles. • Case B: tank and dike. • Case C: dike only.

77. The wind tunnel was an ultra low speed boundary layer wind tunnel able to simulate the constant stress layer of the atmospheric boundary layer. Airflow from the driving fans passed through a 7×20×80 ft (2.1×6.1×24.4 m) working area in which the floor was covered with smooth rubber matting on which the roughness elements were mounted. A 150:1 model of the tank and dike was installed on the floor surface. The dike was square with an inner dimension of 63 cm and a wall height of 3.7 cm and the tank model was 31 cm in diameter with a spherical dome top and an overall height of 28.3 cm. The tank was located in the centre of the dike on a mesh screen through which the gas flowed. The CO2 was released at 32.9 standard litres per minute (slpm) with propane added as a tracer at 0.5 slpm.

78. Case A consists of dispersion from a well defined area source. For this case, DRIFT was initiated using a vapour source of half width equal to the width of the mesh screen.

79. Case B is more difficult to model with DRIFT as the storage tank sits in the centre of the source surrounded by the dike. The tank was modelled in DRIFT as a building located downwind from the source. A fence was then positioned downwind from the tank at a distance equal to that between the edge of the tank and the dike. This approximation assumes that the entire cloud is modified by both the tank and the fence. In reality, the presence of the tank causes an initial birfurcation of the cloud at the source.

80. Case C was modelled as a fence positioned downwind from the area source.

81. The results are shown in Figure 11. Relatively poor agreement in the near field was obtained for cases A and C, possibly due to the source being excessively diluted. In these cases, switching off dilution over source resulted in increased near field predictions (these results have not been included).

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Figure 11 Measured and predicted maximum arcwise concentrations for the CHRC wind tunnel trials

5.3.6 BA-Hamburg

82. The BA-Hamburg trials were conducted in an open circuit wind tunnel at the Meteorological Institute at the University of Hamburg (UH) (Nielsen and Ott, 1996). The experiments were carried out on a wide range of different geometries in an open circuit wind tunnel using a floor level area gas source of sulphur hexafluoride (SF6). The source consisted of an array of holes covering a 7 cm diameter circular area flush with the tunnel floor. A circular cap approximately 9 cm in diameter was located 5 cm above the source. Five different arrangements of obstacles are included in the database, of which four were modelled with DRIFT. These are an unobstructed reference case, an upwind fence, a downwind fence and a circular fence surrounding the source. Both the upwind and downwind fences are semicircular. There are 2 releases for each case, differing in sensor location or fence height/diameter, requiring a total of 8 runs of DRIFT.

83. The unobstructed reference cases were modelled as dispersion from an area source. For the downwind fence cases, the fence was assumed to be straight. The fence model in DRIFT only acts upon the cloud and therefore upwind fences have no effect, resulting in the two upwind fence cases being effectively modelled as unobstructed. The cases with a circular fence surrounding the release point reduce to a problem similar to the Falcon trials. Again, this can be considered as an arrangement of fences or as an area source of the same diameter as the fence, the latter assuming that the entire fenced area surrounding the source fills with gas. As with the Falcon trials, this approach was found to give better predictions and these results were

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entered into the database. All of the results and comparisons with experiments are shown in Figure 12. Generally very good agreement was obtained for these wind tunnel trials apart from the first unobstructed case. As with the unobstructed CHRC A trial, the near field concentration was underpredicted.

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Figure 12 Measured and predicted maximum arcwise concentrations for BA-Hamburg wind tunnel trials

5.3.7 BA-TNO

84. The BA-TNO wind tunnel trials (see Nielsen and Ott, 1996) used SF6 gas released from a floor level source at a scale of 78:1. The source consisted of a 107 mm diameter orifice covered in 50% porosity gauze to give a low momentum release. The single TNO-FLS experiment used in this review was a continuous release with an unobstructed measurement field. The DRIFT results compared with the experiments are shown in Figure 13. Near field concentrations were underpredicted as with the CHRC A and unobstructed BA-Hamburg trials.

Figure 13 Measured and predicted concentration for BA-TNO wind tunnel trials

5.4 MODEL PERFORMANCE FOR KEY STATISTICAL EVALUATION PARAMETERS

85. The Statistical Performance Measures (SPM) used in the evaluation database are those recommended by Ivings et al. (2007) and reproduced in Table 4.

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Table 4 statistical performance measure definitions SPM Definition

Mean Relative Bias

( )mp

pm

CC

CCMRB

+

−=

21

Mean Relative Square Error ( )( )2

2

41

mp

mp

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CCMRSE

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FAC2: the fraction of predictions which are within a factor of two of the measurements

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≤

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=

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CMG logexp

Geometric variance 2

logexp

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CVG

86. The angle brackets used in the formulas denote an average over all the measured/predicted pairs of concentrations entered in the database, for all test cases including both the field and wind tunnel trials. As noted previously, the wind tunnel trials have been modelled at wind tunnel scale as this is possible within DRIFT and removes the uncertainty over the effects of scaling the data. Modelling the wind tunnel trials at both scales would result in an undue bias to those datasets.

87. SPM have been calculated for two parameters: the maximum arc-wise concentration at various downwind distances and the plume width at those distances. In the database, it is recognised that not all models are capable of accounting for obstacles. Therefore, the SPM are split into two basic groupings: dispersion in the presence and absence of obstacles. Additionally, the data in the database are presented for differing averaging times depending on the specific dataset. SPM are computed separately for nominally “short” and “long” averaging times. These correspond to peak instantaneous concentration (averaging times typically in the order of seconds) and a maximum average concentration over a longer time period (typically minutes).

88. Values of SPM corresponding to an “acceptable model” have been put forward by Ivings et al. (2007) and have been adopted in this review. The proposed acceptance criteria are as follows:

• A mean bias within ±50% of the mean, corresponding to: –0.4<MRB<0.4 and 0.67<MG<1.5. • A scatter of a factor of three of the mean, corresponding to: MRSE<2.3 and VG<3.3. • The fraction of model predictions within a factor of two of observations to be at least 50%.

5.5 EVALUATION AGAINST QUANTITATIVE ASSESSMENT CRITERIA

89. Tables 5 and 6 present the values of SPM as computed for the pairs of predicted concentrations and measurements. The validity of the model can be assessed by comparing the SPM with the acceptability range given in the final column in each table, where SPM results outside the range are in bold.

35

Table 5 SPM for maximum arc-wise concentration for short time averages SPM Value for unobstructed

cases (Group 1) Value for obstructed cases

(Group 2) Acceptability range

MRB -0.11 -0.53 -0.4< MRB < 0.4 MG 0.86 0.56 0.67 < MG < 1.5 MRSE 0.36 0.53 MRSE < 2.3 VG 1.64 1.97 VG < 3.3 FAC2 0.79 0.56 0.5 < FAC2

Table 6 SPM for maximum arc-wise concentration for long time averages SPM Value for unobstructed



MRB -0.08 0.06 -0.4< MRB < 0.4 MG 0.92 1.06 0.67 < MG < 1.5 MRSE 0.85 0.48 MRSE < 2.3 VG 3.13 1.86 VG < 3.3 FAC2 0.30 0.59 0.5 < FAC2

90. The SPM in the first column in Table 5 are generated from the Maplin Sands, Burro and Coyote trials. These are unobstructed spills and the concentration comparisons have been made against the short averaging time (corresponding to 3 seconds for Maplin Sands and 1 second for Burro and Coyote trials). The resulting SPM values fall within the acceptance criteria outlined previously. Some further information can be gained from the values of MRB and MG. As MRB is negative and MG is less than 1, there is a tendency to overpredict the concentration. Inspection of the graphs of concentration versus downwind distance show this is true for the Burro and Coyote trials, but not for the Maplin Sands trials where the concentration was generally underpredicted. However, it can be noted that the Maplin Sands trials are a smaller dataset and therefore have lower influence on the overall result than the Burro and Coyote trials. In all three cases, the spills are onto water which carries an additional element of uncertainty in the specification of the source term. Since GASP has been used to model the source for the spills onto water, the validity of two models is effectively being evaluated.

91. The second column in Table 5 contains SPM generated from the Falcon trials only. The value of MRB is outside the acceptable limit, and indicates that DRIFT is overpredicting the concentration. The plots of concentration versus distance show that the near-field values, particularly for Falcon 1 and 4, are overpredicted by as much as a factor of 4. The comparison of DRIFT with the Falcon trials needs to be viewed with some caution as the source is very complex and subject to uncertainty (similar conclusions about these trials are given by Hansen et al., 2010). The two methods used to approximate the source (which both produced similar results) will tend to overestimate the vapour generation rate and hence predicted concentrations.

92. The first column in Table 6 lists SPM for datasets which present unobstructed concentration measurements for a long time average. These include the Burro, Coyote and Thorney Island continuous release trials in addition to the CHRC and Hamburg wind tunnel trials. The negative value of MRB and an MG of less than one indicate that, overall, DRIFT is overpredicting concentration. This is the case for the Burro and Coyote trials, whereas the wind tunnel trials were generally underpredicted. As a result of these discrepancies and despite the mean relative bias (MRB) being close to zero, the value of FAC2 falls outside the acceptable range. The second column in Table 6 relates to the obstructed Falcon trials as well as the CHRC and Hamburg wind tunnel trials. The values of MRB and MG indicate that DRIFT is underpredicting concentration; however all SPM are within the acceptable range. It is worth noting that in the Hamburg

36

upwind fence cases, the obstacles are not included in the calculation, as the wake of a fence does not influence the calculation. The Falcon trials and Hamburg circular fence cases were modelled without representing the fences and therefore there is greater uncertainty in the source configuration. However, particularly good agreement was obtained for the Hamburg downwind fence cases where the obstacles are more readily defined.

93. Table 7 lists SPM computed for cloud width predictions. In the database, cloud widths were only calculated where concentration values were available for the long time average datasets. Furthermore, cloud widths were not calculated for those datasets where the cloud shape was not well defined (for example where the cloud had bifurcated) or had veered excessively from the sensor array (see Coldrick et al., 2009). The SPM for cloud width therefore represent a smaller set of paired comparisons than for the concentration measurements. The values of SPM fall well within the acceptance criteria. Values of MG less than one and negative values of MRB show that DRIFT tends to slightly overpredict the cloud width for the obstructed cases.

Table 7 SPM for cloud width SPM Value for unobstructed



MRB 0.04 -0.19 -0.4< MRB < 0.4 MG 1.04 0.82 0.67 < MG < 1.5 MRSE 0.06 0.12 MRSE < 2.3 VG 1.07 1.14 VG < 3.3 FAC2 1.00 0.92 0.5 < FAC2

94. Figure 14 shows a graphical representation of the results of Tables 5, 6 and 7 using the now commonly adopted approach of Hanna et al. (1993). The vertical lines of MG = 0.5 and MG = 2 represent factor of two overpredictions and underpredictions about the mean. The lines VG = exp(ln MG)2 define the minimum possible values of VG for a given value of MG and the acceptability criteria are shown by the red box. The geometric mean bias shows how well a model matches the experiments on average, where a value of one is ideal. However, a model which vastly underpredicts and overpredicts by equal amounts could also achieve a value of one, by virtue of being good on average. Therefore, the geometric variance is also included on the plot to give an indication of the level of scatter of predictions about the mean.

95. Evaluations of models against the same Model Evaluation Protocol have been carried out by Kohout (2011), Hansen et al. (2010) and Witlox et al. (2013). The Model Evaluation Protocol is not intended to be a platform for comparison of models against each other, though reference to plots of concentration with distance presented in Kohout (2011) and Hansen et al. (2010) show that DRIFT gives comparable results to the other models.

37

Figure 14 Graphical depiction of MG and VG. The vertical lines of MG = 0.5 and MG = 2

represent factor of two overpredictions and underpredictions about the mean. The lines VG = exp(ln MG)2 define the minimum possible values of VG for a given value of MG and the

acceptability criteria are shown by the red box.

5.6 SENSITIVITY STUDIES

5.6.1 Variations in source model for the water spills

96. The various means of calculating the source term for the LNG spills onto water were discussed in Section 5.2.2. The influence of the source term was assessed for one case from each of the water spill trials i.e. Maplin Sands 27, Burro 3 and Coyote 3. In addition to using GASP, two different evaporation rates were considered: 0.085 kg/m2/s and 0.17 kg/m2/s (see Section 5.2.2). The results are shown in Figures 15 to 17. For Maplin Sands 27, the results obtained by the different methods are significantly different in the near field. However, the concentration predictions obtained using GASP source modelling are nearer to those obtained assuming an evaporation rate of 0.085 kg/m2/s. Lower concentration predictions were obtained with a rate of 0.085 kg/m2/s as this results in a proportionally larger pool radius (because the same overall vaporisation rate in terms of kilogrammes per second was assumed). As with Maplin Sands 27, the results for Burro and Coyote 3 for the different source models show most difference in the near field. However, the actual dimensions of the source are not known for any of the water spills. Therefore, using different source terms can, at best, give an indication of the uncertainty associated with this part of the dispersion calculation.

38

Figure 15 Source model effect for Maplin Sands 27

Figure 16 Source model effect for Burro 3

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Figure 17 Source model effect for Coyote 3

5.6.2 Atmospheric humidity

97. There is some uncertainty over the appropriate value of relative humidity in connection with the Maplin Sands trials. The data report lists a value of 72% onshore, but it is understood to have been closer to 90% at the release point (see Hanna et al., 1991). The atmospheric water vapour content is significant in terms of cloud thermodynamics due to the heat released by the condensation of the water vapour. In the near field, it may be expected that the increased heat transfer into the cloud would result in lower measured concentrations. However Figure 18 shows that the increased humidity results in higher predicted concentrations, though the effect is not significant in comparison to the overall error.

Figure 18 Effect of different values of relative humidity on DRIFT predictions for Maplin Sands 34

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6 PASSIVE RELEASES

98. In addition to the validation in the previous sections using the LNG model validation database, a separate exercise was carried out to validate DRIFT’s passive dispersion capabilities. This was carried out as a stand-alone exercise and not recorded in the Model Evaluation Report.

6.1 SELECTION OF DATASETS

99. Numerous passive datasets are available for model evaluation and these are generally aimed at model evaluation studies for dispersion of pollutants from stacks. Some well known examples are Kincaid, Copenhagen, Lillestrom and Indianapolis; these four datasets are available as part of the “Model Validation Kit”1 and have been used in numerous validation exercises. In risk assessment, the types of releases of interest are those occurring at major hazards sites and pipelines, therefore this validation study has concentrated on low-level passive releases, rather than those from elevated stacks. Whilst fewer dispersion experiments have been carried out using ground level sources, two extensive experimental programmes are the Hanford Krypton (Nickola et al., 1970) and Prairie Grass (Barad, 1958) experiments, described in the following sections. Both datasets and test descriptions are also available as part of the Modeler’s Data Archive (MDA) of Hanna et al. (1991). The main drawback of the Hanford releases is the lack of information on the source conditions; the initial concentration, release rate and source geometry are not known, therefore more limited use can be made of the data.

6.2 HANFORD EXPERIMENTS

100. The Hanford passive experiments were carried out in 1967 to study the dispersion of the radioactive tracer krypton-85 and are described in Nickola et al. (1970), in which both instantaneous and continuous releases were performed in atmospheric conditions ranging from stable to slightly unstable. The continuous releases were run for between 10 and 20 minutes and concentrations were measured on arcs at 200 m and 800 m. The dispersion field was largely flat with the detectors placed approximately 1.5 m above the ground surface. The source consisted of a cylinder of argon gas to which the krypton was added and the mixture subsequently released at a controlled rate. However, the initial mixing ratio is unspecified and therefore the release rate of tracer is not known. This does not prevent the releases being modelled altogether however as a release rate can be arbitrarily assigned and the quantity of interest then becomes the decay of concentration between the release point and the detectors. This is the approach used by Hanna et al. (1991) when they modelled the Hanford trials.

6.3 THE PRAIRIE GRASS EXPERIMENTS

101. Project Prairie Grass was a series of field experiments carried out in the summer of 1956 (Barad, 1958), the aim of the experiments being to determine the diffusion of a tracer gas as a function of weather conditions. A total of 70 trials were run under a variety of atmospheric conditions during the day and at night, all of which were 10 minute continuous releases of sulphur dioxide (SO2) from a low level source. The area over which the experiments were performed was largely flat, uncultivated and covered with native prairie grasses. The release point consisted of a 50 mm diameter pipe rising approximately 0.5 m from the ground with the pipe ending in an elbow so that the discharge was parallel to the ground. SO2 gas was released at atmospheric temperature and pressure with a maximum release rate of 0.1 kg/s. Concentration measurements were made at 1.5 m elevation on arcs at 50 m, 100 m, 200 m, 400 m and 800 m from the release point. The data report (Barad, 1958) suggests that the largest source of error in the measurements was due to background contamination which accounted for a measurement error of 6-9%. Meteorological data including temperature, windspeed and direction were collected at a height of 2 m at two locations.

1 http://www.harmo.org/kit/ 41

6.4 TEST CASES MODELLED

102. The MDA contains data for five of the Hanford trials and these are listed in Table 8. The approach used for these trials was to run DRIFT with the release rate given in the MDA and then to scale the release mass flow by the difference between measured and predicted concentration at the 200 m arc. DRIFT was then re-run with the scaled release rate (this is the release rate given in Table 8). The result is that the measured and predicted concentrations at the 200 m arc are identical and meaningful comparison can only be made by examining the difference in measured and predicted concentration at the second (800 m) arc.

103. Of the 70 Prairie Grass releases undertaken, the five modelled for the current evaluation corresponded to those used in the validation exercise of Tickle et al. (2011a). The release quantities and weather conditions are listed in Table 8 and data for the test cases were taken directly from the MDA. The source conditions for the Prairie Grass experiments were particularly well defined and as the release point was directional with exit velocities in the order of 17 m/s, the discharge was modelled in DRIFT as a momentum jet. The releases were modelled as 10 minute finite duration releases and the results were averaged over this period.

Table 8 Release conditions for the Hanford and Prairie grass passive releases

Trial Release rate modelled (kg/s) Windspeed (m/s) Atmospheric stability (Pasquill)

Hanford C1 0.0227 1.3 F Hanford C2 0.0417 3.9 C Hanford C3 0.114 7.1 C Hanford C4 0.115 3.9 C Hanford C5 0.0202 2.6 E Prairie Grass 9 0.092 6.9 C Prairie Grass 10 0.092 4.6 B Prairie Grass 32 0.0414 2.2 F Prairie Grass 33 0.0947 8.5 D Prairie Grass 36 0.04 1.9 F

6.5 RESULTS

104. Results for the Hanford trials are shown in Figure 19 in which the comparisons of interest are those at the 800 m arc. Particularly good agreement was obtained for trials 2, 4 and 5. DRIFT overpredicted concentration for trials 1 and 3, though the reason for this is not clear. Trial 1 was run in the lowest wind speed (1.3 m/s) but with stable conditions.

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Figure 19 Results for DRIFT predictions of the Hanford experiments

105. Results for the Prairie Grass experiments are shown in Figure 20. In less stable conditions (Prairie Grass 10), DRIFT predictions were in good agreement with the observations. In stable conditions (Prairie Grass 32 and Prairie Grass 36), DRIFT tended to underpredict concentration, especially in the near field in the case of Prairie Grass 36. This behaviour is consistent with that found in Tickle et al. (2011a). It was also noted that better agreement was obtained by specifying a shorter averaging time, which may reflect a weakness of the lateral meander model in stable conditions.

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Figure 20 Results from the DRIFT prediction of the Prairie Grass passive experiments

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6.6 SPM RESULTS

106. The statistical performance measures introduced in Section 6.2.2 were suggested by Ivings et al. (2007) as appropriate measures for dense gas dispersion. These values were informed in part by the work of Hanna et al. (2004) and Chang and Hanna (2004), who suggested appropriate values of SPM for comparison with field experiments. Ivings et al. (2007) note that the experiments referred to by Chang and Hanna (2004) tended to be relevant to air quality modelling including passive tracer tests. Therefore, the SPM criteria suggested by Ivings et al. (2007) for dense gases have been adopted in this section as a measure of DRIFT’s passive modelling capability.

107. SPM results for the concentration and cloud width predictions are given in Table 9. Meaningful values cannot be derived for the Hanford trials so results are only presented for the Prairie Grass experiments. The slight overprediction of concentration in three of the trials is reflected in the negative value of MRB and a value of MG less than one. The values of these SPM are, however, inside what would be considered acceptable for a dense gas dispersion model. MRSE and VG also fall within the acceptance criteria as does FAC2. Cloud widths were only available for two of the trials (Prairie Grass 9 and Prairie Grass 10) and the SPM show that DRIFT tended to underpredict this quantity. MRB and MG are outside the acceptance criteria whilst MRSE, VG and FAC2 are considered acceptable. Figure 21 is a plot of the relationship between MG and VG for the Prairie Grass experiments.

Table 9 SPM results for the Prairie Grass passive experiments

SPM Concentration Cloud width Acceptability range MRB -0.12 0.64 -0.4< MRB < 0.4 MG 0.87 1.93 0.67 < MG < 1.5 MRSE 0.43 0.41 MRSE < 2.3 VG 1.72 1.56 VG < 3.3 FAC2 0.61 0.70 0.5 < FAC2

Figure 21 Graphical depiction of the SPM results for the Prairie Grass passive experiments

45

7 CONCLUSIONS

108. This report describes the evaluation of DRIFT version 3.6.4 in accordance with the Model Evaluation Protocol of Ivings et al. (2007). The protocol sets out a method of scientific assessment, verification and validation. DRIFT version 3.6.4 represents a major revision of the model to address a number of shortcomings of the original model and to add some new features. The instantaneous and steady continuous release models provide a fairly straightforward but nevertheless also fairly intricate generalization of the original DRIFT model to allow clouds to become buoyant. The models for transient and unsteady releases also add some new ideas and assumptions, but a number of details may benefit from further explanation in the specification and documentation.

109. The basis of the validation was a set of statistical performance measures derived from measured and predicted gas concentrations and cloud widths. These were obtained for a series of dense gas releases and a set of passive tracer releases. The test cases are split into groups depending on the averaging time used in processing the experimental data and according to the absence or presence of obstacles. Five statistical performance measures (SPM) were calculated for each grouping.

110. The assumptions made in modelling each test case result in some uncertainty in the DRIFT predictions. This can be demonstrated in the variation in predictions that can be obtained using, for example, different source terms. It is worth noting that the experimental values of concentration are not direct measurements but an approximation (via several steps of processing) of a temporally and spatially varying flow. The nature of large scale dense gas dispersion experiments in the field means that they cannot be well controlled. Therefore, several nominally identical releases would each give different results. In this validation, single DRIFT runs which effectively represent an ensemble average have been compared with single releases for each trial.

111. With the above in mind, the DRIFT predictions generally compared well with the experimental data. SPM values within recommended levels were obtained for most of the comparison groups. For the trials which involved spills onto water and unobstructed dispersion there is a high level of uncertainty surrounding the specification of the source term and DRIFT tended to overpredict concentration. To some extent, the SPM will mask cases where overpredictions and underpredictions cancel each other out and result in what appears to be good model performance. On the whole, DRIFT tended to underpredict cloud width though the SPM values were within acceptance criteria. Cloud widths were not available for all the experimental datasets so represent a smaller set of comparisons then for the concentration data. Furthermore, the determination of cloud width for the field trials is not an exact process and in many of the cases gives a long time average representation of the cloud.

112. In summary, the advantages of DRIFT are as follows:

• The model runs very quickly. • It can output, on request, the evolution of a very large number of variables, allowing easy

qualitative understanding of what it is predicting.

• Documentation is available which covers model specification, user guide and validation. • The model is based on a large amount of scientific research through the 1980s and early 90s.

46

113. Important aspects of the model to be aware of are:

• The model is complex. In attempting to get a physically well-founded understanding of the behaviour of heavy clouds into the form of an integral model (whose solutions can be found very rapidly on a computer) the original DRIFT was a more complex model than most of its contemporaries. The extended scope of DRIFT 3.6.4 follows the same aims and it is more complex still.

• During the course of this review, it became apparent that care needs to be taken in relation to some

output variables and how these relate to the model itself. For example, some output relates to the cloud before any post-processing calculations have taken place and therefore gives rise to what appear, at first sight, to be erroneous results.

• Pool evaporation results may be imported from the GASP model and are treated only as “time

varying” by default. This results in a more limited range of output from the user interface.

• In common with other similar models, DRIFT only accounts for flat terrain, though modest variations in terrain can have a significant effect on dense gas dispersion.

• Additionally, this review identified that the finite duration and time varying models would benefit

from further explanation in the documentation.

114. Overall, this evaluation exercise has shown DRIFT to be fit for purpose. The findings of this report have been used to inform guidelines on the use of DRIFT to ensure that the model is used correctly and to give sensible and appropriate advice.

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8 REFERENCES

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Britter, R. E. and McQuaid, J., (1988). Workbook on the dispersion of dense gases, HSE. Brown, T. C., Cederwall, R. T., Chan, S. T., Ermak, D. L., Koopman, R. P., Lamson, K. C., McClure, J. W. and Morris, L. K. (1990). Falcon series data report: 1987 LNG vapor barrier verification field trials, Gas Research Institute, Report No. GRI-89/0138.

Carissimo, B., Jagger, S. F. and Daish, N. C. (2001). The SMEDIS database and validation exercise, Int J Environ Pollut, Vol 16, No 1-6, pp 614 – 629. Chang J C and Hanna S R, (2004). Air quality model performance evaluation, Meteorol Atmosp Phys, Vol 87, pp 167 – 196.

Coldrick, S., Lea, C. J. and Ivings, M. J. (2009). Validation Database for Evaluating Vapor Dispersion Models for Safety Analysis of LNG Facilities, The Fire Protection Research Foundation.

Colenbrander, G. W., Evans, A. and Puttock, J. S. (1984a). Spill tests of LNG and refrigerated liquid propane on the sea, Maplin Sands 1980: Dispersion Data Digest; Trial 27, Shell Research Ltd, Thornton Research Centre, Report TNER.84.028, May 1984.

Colenbrander, G. W., Evans, A. and Puttock, J. S. (1984b). Spill tests of LNG and refrigerated liquid propane on the sea, Maplin Sands 1980: Dispersion Data Digest; Trial 34, Shell Research Ltd, Thornton Research Centre, Report TNER.84.030, May 1984.

Colenbrander, G. W., Evans, A. and Puttock, J. S. (1984c). Spill tests of LNG and refrigerated liquid propane on the sea, Maplin Sands 1980: Dispersion Data Digest; Trial 35, Shell Research Ltd, Thornton Research Centre, Report TNER.84.031, May 1984.

Daish, N. C., Britter, R. E., Linden, P. F., Jagger, S. F. and Carissimo, B. (2000). SMEDIS: scientific model evaluation of dense gas dispersion models, Int J Environ Pollut, Vol 14, No 1 – 6, pp 39 – 51.

Goldwire, H. C. Jr, Rodean, H. C., Cederwall, R. T., Kansa, E. J., Koopman, R. P., McClure, J. W., McRae, T. G., Morris, L. K., Kamppinen, L., Kiefer, R. D., Urtiew, P. A. and Lind, C. D, (1983). Coyote series data report: LLNL/NWC 1981 LNG spill tests. Dispersion, vapor burn and rapid-phase transition, Vols 1 & 2, UCID-19953, Lawrence Livermore National Laboratory.

Hanna, S. R., Chang, J. C. and Strimaitis, D. G., (1993). Hazardous gas model evaluation with field observations, Atmos Environ, Vol 27 A, No 15, pp 2265 – 2285. Hanna, S. R., Hansen, O. R. and Dharmavaram, S. (2004). FLACS CFD air quality model performance evaluation with Lit Fox, MUST, Prairie Grass and EMU observations, Atmospheric Environ, Vol 38, pp 4675 – 4687.

Hanna, S. R., Strimaitis, D. G. and Chang, J. C. (1991). Hazard response modelling uncertainty (a quantitative method), Volume II: Evaluation of commonly-used hazardous gas dispersion models, Sigma Research Corporation, Final report, Volume II, April 1989 – April 1991.

48

Hansen, O. R., Gavelli, F., Ichard, M. and Davis, S. G., (2010). Validation of FLACS against experimental data sets from the model evaluation database for LNG vapor dispersion, Journal of Loss Prevention in the Process Industries 23, pp 857-877.

Havens, J. and Spicer, T. (2005). LNG vapor cloud exclusion zones for spills into impoundments, Process Safety Progress, Vol 24, Iss 3, pp 181 – 186.

Havens, J. Spicer, T. and Sheppard, W. (2007). Wind tunnel studies of LNG vapor dispersion from impoundments, AIChE National Spring Meeting, Houston.

Havens, J. and Spicer, T. (2006). Vapor dispersion and thermal hazard modelling, Final topical report to Gas Technology Institute under sub-contract K100029184, October 2006.

Ivings, M. J., Jagger, S. F., Lea, C. J. and Webber, D. M. (2007). Evaluating vapor dispersion models for safety analysis of LNG facilities, The Fire Protection Research Foundation, May 9 2007.

Jones, S. J., Mercer, A., Tickle, G. A., Webber, D. M. and Wren, T. (1993). Initial verification and validation of DRIFT UKAEA Report SRD/HSE R580.

Jones, S. J., Tickle, G. A. and Webber, D. M. (1994a). Further Validation of DRIFT, UKAEA report AEA/CS/FLADIS/Ia/1994.

Kohout, A. J., (2011). Evaluation of fire dynamics simulator for liquefied natural gas vapor dispersion hazards, Department of Fire Protection Engineering, Graduate School of the University of Maryland.

Koopman, R. P., Baker, J., Cederwall, R. T., Goldwire, H. C. Jr, Hogan, W. J., Kamppinen, L. M., Kiefer, R. D., McClure, J. W., McCrae, T. G., Morgan, D. L., Morris, L. K., Spann, M. W. Jr and Lind, C. D. (1982a). Burro series data report LLNL/NWC 1980 LNG spill tests, UCID-19075, Vols 1 & 2, Lawrence Livermore National Laboratory.

Koopman, R. P., Cederwall, R. T., Ermak, D. L., Goldwire, H. C. Jr, Hogan, W. J., McClure, J. W., McRae, T. G., Morgan, D. L., Rodean, H. C. and Shinn, J. H. (1982b). Analysis of Burro series 40 m3 LNG spill experiments, J Hazard Mater, Vol 6, pp 43 – 83.

Koopman, R. P. (2004) Comments in Federal Energy Regulatory Commission, Notice of availability of staff’s responses to comments on the consequence assessment methods for incidents involving releases from liquefied natural gas carriers, Docket No. AD04-6-000.

Kukkonen, J., Kulmala, M., Nikmo, J., Vesala, T., Webber, D. M. and Wren, T. (1993). Aerosol Cloud Dispersion and the Suitability of the Homogeneous Equilibrium Approximation, UKAEA Report AEA/CS/HSE R1003/R (1993).

Kukkonen, J., Kulmala, M., Nikmo, J., Vesala, T., Webber, D. M. and Wren, T. (1994). The Homogeneous Equilibrium approximation in models of aerosol cloud dispersion, Atmos Environ Vol 28, pp 2763-2776. McQuaid, J. and Roebuck, B. (1985). Large scale field trials on dense vapour dispersion, Commission of the European communities indirect action programme “Safety of thermal water reactors”, 1979-83, Final report on contracts 029SRUK and 036SRUK with the Health and Safety Executive. McQuaid, J. (Editor) (1987). Heavy gas dispersion trials at Thorney Island - 2, Proceedings of a symposium held at the University of Sheffield, Great Britain, September 1986, J Hazard Mater, Vol 16, pp 1-502.

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MEG (1994a). Model Evaluation Group, Model Evaluation Protocol, European Communities Directorate General XII Science Research and Development.

MEG (1994b). Model Evaluation Group, Guidelines for Model Developers, European Communities Directorate General XII Science Research and Development.

Nielsen, M., Chatwin, P., C., Jorgensen, H. E., Mole, N., Munro, R. J. and Ott, S. (2002). Concentration fluctuations in gas releases by industrial accidents, Technical Report Risø-R-1329(EN), Risø National Laboratory.

Nielsen, M. and Ott, S. (1996). A collection of data from dense gas experiments, Riso-R-845 (EN), Risø National Laboratory, Denmark.

Nickola, P. W., Ramsdell, Jr. J. V. and Ludwick, J. D. (1970). Detailed time-histories of concentrations resulting from puff and short-period releases of an inert radioactive gas: a volume of atmospheric diffusion data, BNWL-1272 UC-53, Batelle Pacific Northwest Laboratories, Richland, WA 99352. Pasquill, F. (1983). Atmospheric Diffusion. 2nd edition, Ellis Horwood.

Puttock, J. S. (1987). The development and use of the HEGABOX/HEGADAS dispersion models for hazard analysis. Proceedings of the international conference on vapour cloud modelling.

Puttock, J. S., Blackmore, D. R. and Colenbrander, G. W. (1982). Field experiments on dense gas dispersion, J Hazard Mater, Vol 6, pp 13–41.

Tickle, G. A. (2001). Thermodynamic Modelling of Anhydrous HF / Moist Air / Immiscible Component Mixtures and Validation against Experimental Data AEA Technology Report AEAT/NOIL/27328006/002.

Tickle, G. A. and Carlisle J. E. (2012). DRIFT Version 3: Mathematical Model, ESR Technology Limited.

Tickle, G. A., Carlisle, J. E. and Ketchell, N. (2011a). Comparison of DRIFT Version 3 Predictions with DRIFT Version 2 and Experimental Data, ESR/D1000846/STR01/Issue 2.

Tickle, G. A., Godaliyadde, D. and Carlisle, J. E. (2011b). Comparison of Predictions from the Gas Dispersion Model DRIFT (Version 3) against URAHFREP Data, ESR Technology report ESR/D1000976/001/Issue 4, to be published as an HSE Research Report.

Turner J. S. (1973). Buoyancy Effects in Fluids, Cambridge University Press, ISBN 0 521 08623.

Walton, E., (2011). DRIFT COM Interface Guide, ESR/D1000846/SUG02/Issue 5.

Webber, D. M. and Jones, S. J. (1989). A User’s Guide to G*A*S*P on Microcomputers, UKAEA report SRD/HSE R521.

Webber, D. M., Jones, S. J., Martin, D., Tickle, G. A. and Wren, T. (1994). Complex Features in Dense Gas Dispersion Modelling, UKAEA Report AEA/CS/FLADIS/1994 (volumes I and II). Webber, D. M., Jones, S. J., Tickle, G. A. and Wren, T. (1992). A model of a dispersing gas cloud, and the computer implementation DRIFT - II Steady Continuous Releases, UKAEA Report SRD/HSE R587. Witlox, H. W. M., Harper, M. and Pitblado, R. (2013). Validation of PHAST Dispersion Model as Required for USA LNG Siting Applications, The Italian Association of Chemical Engineering, Chemical engineering transactions, Vol. 31.

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9 APPENDIX 1: DRIFT MODEL EVALUATION REPORT

51

Model Evaluation Report

Model Evaluation Report Contents

ii

Contents

Key to summary information .............................................................................................................. iv

0. Evaluation information .................................................................................................................... 1

0.1 Protocol .................................................................................................................................. 1

0.2 Evaluator ................................................................................................................................ 1

0.3 Date ........................................................................................................................................ 1

0.4 Documentation ....................................................................................................................... 1

1. General model description ............................................................................................................... 6

1.1 Name, version number and release date ................................................................................. 6

1.2 Short description of model ..................................................................................................... 6

1.3 Model type ............................................................................................................................. 6

1.4 Route of model into evaluation project .................................................................................. 7

1.5 History of model .................................................................................................................... 7

1.6 Quality assurance standards adopted ..................................................................................... 8

1.7 Relationship with other models.............................................................................................. 8

1.8 Current model usage .............................................................................................................. 9

1.9 Hardware and software requirements .................................................................................... 9

1.10 Availability and costs ......................................................................................................... 10

2. Scientific basis of model ................................................................................................................ 11

2.0 Model type ........................................................................................................................... 11

2.1 Specification of the source ................................................................................................... 11

2.2 Specification of the environment ......................................................................................... 13

2.3 Model physics and formulation............................................................................................ 16

2.4 Solution technique................................................................................................................ 23

2.5 Results or output available from model ............................................................................... 24

2.6 Sources of model uncertainty ............................................................................................... 26

2.7 Limits of applicability .......................................................................................................... 27

2.8 Special features .................................................................................................................... 28

2.9 Planned scientific developments .......................................................................................... 28

3. User-oriented aspects of model ...................................................................................................... 29

3.1 User-oriented documentation and help ................................................................................ 29

3.2 Installation procedures ......................................................................................................... 29

3.3 Description of the user interface .......................................................................................... 30

3.4 Internal databases ................................................................................................................. 30

3.5 Guidance in selecting model options ................................................................................... 31

3.6 Assistance in the inputting of data ....................................................................................... 32

3.7 Error messages and checks on use of model beyond its scope ............................................ 32

3.8 Computational aspects ......................................................................................................... 33

3.9 Clarity and flexibility of output results ................................................................................ 33

3.10 Suitability to users and usage ............................................................................................. 34

3.11 Possible improvements ...................................................................................................... 35

3.12 Planned user-oriented developments ................................................................................. 35

4. Verification performed ................................................................................................................... 36

4.1 Summary of verification ...................................................................................................... 36

4.2 Comments ............................................................................................................................ 37

5. Evaluation against MEP qualitative assessment criteria ................................................................ 38

5.1 Scientific criteria .................................................................................................................. 38

5.2 Output criteria ...................................................................................................................... 39

Model Evaluation Report Contents

iii

6. Validation performed and evaluation against MEP quantitative assessment criteria .................... 40

6.1 Validation already performed .............................................................................................. 40

6.2 Evaluation against MEP quantitative assessment criteria .................................................... 41

6.3 Conclusions .......................................................................................................................... 55

7. Conclusions .................................................................................................................................... 57

General model description ......................................................................................................... 57

Scientific basis of model ............................................................................................................ 57

Limits of applicability ................................................................................................................ 58

User-oriented aspects of model .................................................................................................. 58

Verification performed ............................................................................................................... 58

Evaluation against MEP qualitative assessment criteria ............................................................ 59

Validation performed and evaluation against MEP quantitative assessment criteria ................ 59

Advantages and disadvantages of model ................................................................................... 60

Suitability of protocol for assessment of model......................................................................... 61


DRIFT 3.6.4 Version 11 (October 2014) iv

Key to summary information

In Sections 1-3 summary information is provided on many aspects of the model. This is organised

as follows:

(a) Where there is a mutually exclusive choice of options, the relevant choice is denoted by a circle

containing a dot , while the remaining choices are accompanied by an empty circle .

(b) A square box containing a tick denotes that a feature is present or an option applies to the

model; if the box contains a cross , this emphasises that the feature is not present or that the

option does not apply.

(c) A character between square brackets can convey one of the following meanings:

[U] denotes that the user can specify the details of the feature;

[M] denotes that the model specifies the details of the feature;

[X] denotes that the feature is absent or not considered;

[] denotes that the feature is present

[?] denotes that the status of the feature is uncertain;

[n] denotes that the item has the value n.

(d) Any parts which are greyed out are not relevant to the model in question.

(e) Comments may be added in any section. They should be brief.

In Section 2 – “Scientific basis”

A simple categorisation of dense gas dispersion models is included in the protocol, consisting of

four main types:

(a) Simplified empirical screening models or screening tools based on either a generic interpretation

of a problem or specific experience in a restricted domain.

(b) One-dimensional integral models in which the development of the flow is in one spatial

dimension. Variations of, say, the concentration field in the other two dimensions are

accommodated by the assumption of self-similarity in the concentration field.

(c) Shallow-layer models or two-dimensional integral models in which the development of the flow

is in two spatial dimensions.

(d) Fully three-dimensional models allowing for the development of the flow field in three spatial

dimensions and time. This would include computational fluid dynamics (CFD) and the attendant

models for turbulence.

Model Evaluation Report Evaluation information

1

0. Evaluation information

0.1 Protocol This scientific assessment was carried out using the LNG Model Evaluation Protocol (2007)

derived from the SMEDIS Model Evaluation Protocol, Version 2.0 (7 December 2000), and the

Model Evaluation Report template Version 1.1.

0.2 Evaluator The scientific assessment was carried out by:

D M Webber, Health and Safety Laboratory (General Aspects)

S Coldrick, Health and Safety Laboratory (New validation studies)

0.3 Date The date of this scientific assessment is November 2013.

0.4 Documentation

0.4.1 References

The following references were used in this scientific assessment.

0.4.1.1 Supplied documents

Primary model specification: G. A. Tickle and J. E. Carlisle, (2012). DRIFT Version 3: Mathematical Model, ESR Technology Limited. G. A. Tickle and J. E. Carlisle, (2008). Extension of the Dense Gas Dispersion Model DRIFT to Include Buoyant Lift-Off and Buoyant Rise, HSE research report RR629. Further specification: A. J. Byrne, S. J. Jones, S. C. Rutherford, G. A. Tickle and D. M. Webber, (1992). Description of ambient atmospheric conditions for the computer code DRIFT, UKAEA Report SRD/HSE R553. D. M. Webber, S. J. Jones, G. A. Tickle, and T. Wren, (1992a). A model of a dispersing gas cloud, and the computer implementation DRIFT- I Near instantaneous releases, UKAEA Report SRD/HSE R586.


2

D. M. Webber, S. J. Jones, G. A. Tickle, and T. Wren, (1992b). A model of a dispersing gas cloud, and the computer implementation DRIFT- II Steady Continuous Releases, UKAEA Report SRD/HSE R587. D. M. Webber and T. Wren, (1993). A differential phase equilibrium model for clouds, plumes and jets UKAEA Report SRD/HSE R552. User manual: DRIFT Version 3 User Guide Issue 5, ESR Technology Limited, 2011. E. Walton, (2011). DRIFT COM Interface Guide Issue 4, ESR Technology Limited report ESR/D1000846/SUG02. Verification, validation, and background material: P. W. M. Brighton, S. J. Jones, D. Martin, D. M. Webber, and T. Wren, (1992).The effects of natural and man-made obstacles on heavy gas dispersion - Summary of report. UKAEA Report SRD/HSE R583. S. J. Jones, G. A. Tickle, and D. M. Webber, (1994a). A user’s guide to D*R*I*F*T, internal UKAEA publication. S. J. Jones, G. A. Tickle, and D. M. Webber, (1994b). Further Validation of DRIFT, UKAEA report AEA/CS/FLADIS/Ia/1994. S. J. Jones, G. A. Tickle, D. M. Webber, and T. Wren, (1994). A user’s guide to the DRIFT suite of codes (internal UKAEA publication). S. J. Jones, D. Martin, D. M. Webber, and T. Wren (1992). The effects of natural and man-made obstacles on heavy gas dispersion. Part II: Dense gas dispersion over complex terrain, UKAEA Report SRD/HSE R582. S. J. Jones, A. Mercer, G. A. Tickle, D. M. Webber and T. Wren, (1993). Initial verification and validation of DRIFT, UKAEA Report SRD/HSE R580. J. Kukkonen, M. Kulmala, J. Nikmo, T. Vesala, D. M. Webber and T. Wren, (1993). Aerosol Cloud Dispersion and the Suitability of the Homogeneous Equilibrium Approximation, UKAEA Report AEA/CS/HSE R1003/R. J. Kukkonen, M. Kulmala, J. Nikmo, T. Vesala, D. M. Webber, and T. Wren, (1994). The Homogeneous Equilibrium approximation in models of aerosol cloud dispersion, Atmospheric Environment 28 pp 2763-2776. J. Kukkonen, M. Kulmala, J. Nikmo, T. Vesala, D. M. Webber, and T. Wren, (1994). Comparison of models

for aerosol vaporisation in the dispersion of heavy clouds. in: Gryning, S.-E. and Millan, M.M. (ed.), Air Pollution Modelling and its Application X. NATO, Challenges of Modern Society, Volume 18. Plenum Press, New York and London, pp. 431 - 438. A. J. Prince, P. W. M. Brighton and D. M. Webber, (1985). Thorney Island heavy gas dispersion trials - determination of path and area of cloud from photographs, UKAEA Report SRD R 318. G. A. Tickle, J. E. Carlisle and N. Ketchell, (2011b). Comparison DRIFT Version 3 Predictions with DRIFT Version 2 and Experimental Data, ESR/D1000846/STR01/Issue 2. G. A. Tickle, D. Godaliyadde and J. Carlisle (2011c). Comparison of Predictions from the Gas Dispersion Model DRIFT (Version 3) against URAHFREP Data, ESR Technology report ESR/D1000976/001/Issue 4, to be published as an HSE Research Report. D. M. Webber, (1983). The physics of heavy gas cloud dispersal, UKAEA Report SRD R 243.


3

D. M. Webber, S. J. Jones, D. Martin, G. A. Tickle, and T. Wren, (1994). Complex Features in Dense Gas Dispersion Modelling, UKAEA Report AEA/CS/FLADIS/1994 (volumes I and II).

D. M. Webber, G. A. Tickle, T. Wren, and J. Kukkonen, (1991). Mathematical modelling of two-phase release phenomena in hazard analysis, UKAEA Report SRD/CEC/22939/01. D. M. Webber and C. J. Wheatley, (1987). An integral, dynamic turbulence model for heavy gas cloud dispersal close to the source, UKAEA Report SRD R 437.

Further validation/development done with the CEC URAHFREP project (on HF) C. C. Kemp and M. S. Newland, (2000). HF Thermodynamics Tests: Data Report, AEA Technology report AEAT/R/NS/0028. S. A. Ramsdale and G. A. Tickle, (2001). Review of Lift-off Models for Ground Based Buoyant Clouds, AEA Technology Report AEAT-4262. G. A. Tickle, (2001). Thermodynamic Modelling of Anhydrous HF / Moist Air / Immiscible Component Mixtures and Validation against Experimental Data, AEA Technology Report AEAT/NOIL/27328006/002. G. A. Tickle, (2001). Integral Modelling of the Dilution and Lift-off of Ground Based Buoyant Plumes and Comparison with Wind Tunnel Data, AEA Technology Report AEAT/NOIL/27328006/001. G. A. Tickle, (2001). Model Predictions Compared with URAHFREP Campaign 2 Field Trial Data, AEA Technology Report AEAT/NOIL/27328006/003. G. A. Tickle, S. A. Ramsdale, C. C. Kemp and M. S. Newland, (2001). Understanding The Dispersion Of Industrial Releases Of Anhydrous Hydrogen Fluoride And The Associated Risks To The Environment And People (URAHFREP): Final Summary Report Work Package 5- HF Thermodynamic Data & Work Package 7- Model Development and Validation AEA Technology plc contract report. URAHFREP: PL 971152: Contract ENV4-CT97-0630.

0.4.1.2 Other references P. W. M. Brighton, A. J. Prince and D. M. Webber, (1985). Determination of cloud area and path from visual and concentration records, J. Hazardous Materials 11 155-178. P. W. M. Brighton, A. J. Byrne, R. P. Cleaver, P. Courtiade, B. Crabol, R. D. Fitzpatrick, A. Girard, S. J. Jones, V. L'Homme, A. Mercer, D. Nedelka, C. Proux, and D. M. Webber, (1994). Comparison of heavy gas dispersion models for instantaneous releases, J Hazardous Materials 36 193-208. R. E. Britter, and J. McQuaid, (1988). Workbook on the dispersion of dense gases, HSE. N. J. Duijm and D. M. Webber, (1993). Dispersion in the presence of buildings, TNO Report 93-155. N. J. Duijm and D. M. Webber, (1994). Dispersion in the presence of buildings, J. Loss Prev Process Industries 7 118-123. S. R. Hanna, J. C. Chang and D. G. Strimaitis, (1993). Hazardous gas model evaluation with field observations, Atmospheric Environment, Vol 27 A, No 15, pp 2265 – 2285. M. J. Ivings, S. F. Jagger, C. J. Lea and D. M, Webber, (2007). Evaluating vapor dispersion models for safety analysis of LNG facilities, The Fire Protection Research Foundation, May 9 2007. S. J. Jones and D. M. Webber (1993). The interaction of a dense gas plume with a fence, Trans IChemE 71B 15-20. M. Nielsen, P. C.Chatwin, H. E. Jorgensen, Mole, N., R. J. Munro, and S. Ott, (2002). Concentration fluctuations in gas releases by industrial accidents, Technical Report Risø-R-1329(EN), Risø National Laboratory.


4

Turner J. S. (1973). Buoyancy Effects in Fluids, Cambridge University Press, ISBN 0 521 08623 D. M. Webber, (1984). Gravity spreading in dense gas dispersion models, in Atmospheric dispersion of heavy gases and small particles 397-406, ed. G. Ooms & H. Tennekes, Springer-Verlag. D. M. Webber and P. W. M. Brighton, (1986). Inviscid similarity solutions for slumping from a cylindrical tank, J. Fluids Eng. 108 238-240. D. M. Webber and S. J. Jones, (1989). A user’s guide to G*A*S*P on microcomputers, UKAEA report SRD/HSE R521. D. M. Webber, S. J. Jones, and D. Martin, (1992). Recent advances in gas cloud dispersion modelling

Plenary invited lecture at the Symposium "Chemical Protection '92", Tampere, Finland, May 1992. Proceedings edited by K Nieminen and E Pääkkönen, published by the Research Centre of the Finnish Defence Forces. ISBN 951-25-0587-8. D. M. Webber and S. J. Jones (1992). Dispersion Modelling, Invited lecture at the Conference "The Safe Handling Of Pressure Liquefied Gases - Consequence Analysis and Prevention" London. D. M. Webber, S. J. Jones, and D. Martin, (1995). Modelling the Effects of Obstacles on the Dispersion of Hazardous Materials, in "International Conference and Workshop on Modeling and Mitigating the Consequences of Accidental Releases of Hazardous Materials" AIChE 1995 ISBN 0-8169-0660-2 pp 379-404. D. M. Webber, A. Mercer, and S. J. Jones (1994). Hydrogen fluoride source terms and dispersion, J. Loss Prev Process Industries 7 94-105. D. M. Webber and C. J. Wheatley (1987). The effect of initial potential energy on the dilution of a heavy gas cloud, J. Hazardous Materials 16 357-380. C. J. Wheatley, (1986a). Dispersion of a passive puff released at the ground into the diabatic atmospheric boundary layer, UKAEA Report SRD/HSE R445. C. J. Wheatley, (1986b). A theory of heterogeneous equilibrium between vapour and liquid phases of binary systems and formulae for the partial pressures of HF and H2O vapour, UKAEA Report SRD R357. C. J. Wheatley and D. M. Webber, (1984). Aspects of the dispersion of denser-than-air vapours relevant to gas cloud explosions, CEC Publication EUR 9592en. C. J. Wheatley and D. M. Webber, (1985). Aspects of the dispersion of denser-than-air vapours relevant to gas cloud explosions in "Safety of thermal water reactors" 191-197 ed. E.Skupinski, B.Tolley, and J.Vilain EUR 9903.

0.4.2 Comments on documentation supplied

The separation into sections 0.4.1.1 and 0.4.1.2. above is somewhat moot: the “supplied documents”

are largely UKAEA publications which when they were written were available from HMSO, but in

fact they were almost all already in this reviewer’s possession for reasons made clear under the

model history. The “other references” are available from other sources or are published in journals

and conference proceedings. Copies of almost all of these were also already in this reviewer’s

possession for the same reasons.


5

0.4.3 Major omissions and uncertainties arising

A large quantity of output variables is available from the graphical user interface and via the COM

interface. There is no single source containing a description of each variable.

Model Evaluation Report General model description

6

1. General model description

1.1 Name, version number and release date

Name: DRIFT

Version number: 3.6.4

Release date: 2012

Notes:

1.2 Short description of model

DRIFT is a model for dispersing clouds. The original model, completed in the early 1990s, was for

heavy or passive clouds released instantaneously or in a steady continuous source. It validated well

against a range of experimental data. The current incarnation extends the scope in a number of

ways. It embraces lighter-than-air clouds (previously, the model would halt upon detection of this

condition), and not only instantaneous and steady continuous releases but also steady releases of

finite duration, and other transient releases. The extension to buoyant plumes necessitates a more

complete description of the atmosphere than was present in the original DRIFT. This is provided

in terms of the various length scales and regions of the atmosphere which may affect the behaviour

of buoyant plumes as well as ground-based heavy clouds.

The instantaneous, and steady continuous, release models are easily recognisable from the original

DRIFT but now allow clouds to transition between dense, passive and buoyant behaviour in a

seamlessly continuous way. Structurally the models are the same as the original DRIFT, and the

extra details in the model which provide the generalisation to allow buoyant clouds are probably as

simple as they can be, commensurate with providing a reasonable model.

1.3 Model type

Screening tool Integral model Shallow layer model CFD model

1-D 2-D 3-D


7

1.4 Route of model into evaluation project

1.4.1 Model supplier

Developer Licensee Other

Contact details:

Name: Dr Graham Tickle

Address: ESR Technology Ltd, Whittle House, 410 Birchwood Park, Warrington,

Cheshire, WA3 6FW, UK.

Telephone: +44 (0)1925 843400

Fax: +44 (0)1925 843500

E-mail/Web: [email protected] http://www.esrtechnology.com/

1.4.2 Model developer

As above

Note: DRIFT was originally developed in the early 1990s by UKAEA for HSE (see below).

This reviewer was one of its authors, as was Dr Tickle. Dr Tickle, who has done subsequent

work on the code, and ESR Technology are the current person and organisation, respectively,

who are responsible for the code.

1.5 History of model DRIFT was developed by the Safety and Reliability Directorate (SRD) of the United Kingdom

Atomic Energy Authority (UKAEA) on behalf of the Health and Safety Executive (HSE) in the late

1980s and early 1990s. By the time of privatisation of parts of UKAEA into “AEA Technology”

in 1996 (who became responsible for the code), the original version of the code was complete, and

it had acquired one or two enhancements (for example the ability to handle obstacles) as a result of

research done as part of various CEC-sponsored projects. Later, part of AEA Technology became

ESR Technology, and that company is currently responsible for the code.

Recently, ESR Technology reissued the code incorporating a number of modifications allowing a

wider range of scenarios to be modelled. These modifications are summarised in the document

“DRIFT Version 3 Mathematical Model” (Tickle and Carlisle, 2012) including, for example, the

previously mentioned buoyant lift-off and rise and extension to multi-component materials.

1.5.1 Model ancestors

DRIFT version 2.31 (Jones et al. 1994a, Webber et al. 1992 a,b) is an immediate ancestor and the

codes DENZ (for instantaneous releases) and CRUNCH (for steady continuous releases) may be



8

considered “forebears”. Both had been developed at SRD for HSE and DRIFT’s specification was

defined so that it would handle those two geometries in a similar way, but also do a lot more, and,

in the light of expanding empirical knowledge of gas clouds, do it more accurately.

1.5.2 Features inherited

The model equations have been maintained and extended to cover buoyant lift-off, the integration

of a momentum jet model, finite duration and time-varying releases and multi-component

thermodynamics.

1.6 Quality assurance standards adopted

1.6.1 Model development

MEG guidelines Other

DRIFT was developed very much along Model Evaluation Group (MEG) guidelines. S. J.

Jones, one of the DRIFT development team, was also a member of the MEG, and was thus

actively involved in development of the MEG guidelines.

1.6.2 Software development

National International Organisation ISO 9000 Other

DRIFT’s development predated AEA Technology’s certification to ISO 9000. Software

verification (as defined by the MEG), however, was considered paramount during its

development.

1.7 Relationship with other models

1.7.1 Status of dispersion model being evaluated

Self-contained Can be used as one part of suite

Inextricably bound to other models General-purpose, specific application

Other

1.7.2 N/A


9

1.7.3 Interfacing with other models

The pool spread and vaporisation source term code GASP has an option to generate a DRIFT input

file, from which DRIFT can then be used straightforwardly to generate dispersion predictions.

DRIFT is also able to directly import GASP files.

1.8 Current model usage

1.8.1 Type of user

Background Engineer Consultant Regulator Academic Other

Type of experience Dispersion Fluid dynamics Thermodynamics Numerical methods Programming Consequence modelling Risk analysis Other

Length of experience Hours Days Weeks Months Years

Current users tend to have “significant” experience.

1.8.2 Model distribution

Location Outside model developer Country of origin Continent of origin Worldwide Other Industry Consultancies Universities Regulatory authorities Other

Numbers <5 5-10 10-50 50-100 >100

1.9 Hardware and software requirements

1.9.1 List of requirements

Computer platforms PC Workstation Parallel machine Other

Operating system DOS Windows UNIX VMS Other

Additional software Required Optional Not required Compiler Graphics package GIS Other


10

DRIFT can be used as a standalone package or be called to interact with Microsoft Excel through

the COM interface.

1.10 Availability and costs

Proprietary Shareware Public domain Other

Licence Perpetual licence/buy outright Not available Other

By negotiation with ESR Technology. (The Regulator’s objective in commissioning the

development of the code was that it should be made available and that its distribution should

support its maintenance and further development.)


DRIFT 3.6.4 Version 11 (October 2014) 11

2. Scientific basis of model

2.0 Model type

The present model may be classified as

empirical integral shallow 3D

2.1 Specification of the source

2.1.1 Primary origin for source

2.1.1.1 Types of release conditions available directly to dispersion model

Release models available Liquid jet Liquid pool Catastrophic release Other

The model is a dispersion model and starts from a gas or aerosol cloud produced from an

instantaneous, steady continuous, finite duration or time varying source. The current version of

DRIFT also incorporates momentum jet equations to model situations such as a pressurised

release from an orifice.

2.1.1.2 Source geometry handled by the dispersion model

Pool plan view Circular Long trench Other

Liquid containment

No dike Dike High collar bund Other

2.1.2 Fluid dynamic properties of source

2.1.2.1 Instantaneous releases

Instantaneous releases

[U] Spatial dimensions [U] Symmetry [U] Velocity

[M] Volume [M] Density [U] Mass

Model Evaluation Report Scientific basis of model

12

[X] Multiple sources [U] Elevation [U] Other

[U] Entrained air

Low-momentum area sources are considered to be at ground level. The elevation can only be

specified for momentum jets.

2.1.2.2 Continuous releases

Continuous releases

[U] Spatial dimensions [U] Orientation [M] Symmetry [U] Velocity

[M] Volume flow rate [M] Density [U] Mass flow rate

[X] Multiple sources [U] Elevation [U] Other

[U] Entrained air

[M] Model can recalculate source dimensions

2.1.2.3 Time-varying releases

Time-varying releases

[U] Spatial dimensions [U] Orientation [U] Symmetry

[M] Volume flow rate [M] Density [U] Mass flow rate

[X] Multiple sources [U] Time variation [U] Other

[U] Entrained air

[M] Model can recalculate source dimensions

2.1.2.4 Other aspects of release types

Guidance provided on choice of dispersion source type

Instantaneous Continuous Time-varying Other

2.1.3 Thermodynamic properties of source

Temperature [] Isothermal Other


13

2.1.4 Material nature of source

Pre-defined substances

LNG Methane Other (~80)

Mixtures

[U] True mixtures [] Effective single-component mixtures [] Passive tracer [] Other

Dependence of physical properties

[M] Temperature [X] Pressure [M] Composition [X] Other

2.2 Specification of the environment

2.2.1 Frame of reference

2.2.1.1 Coordinate system

Cartesian Cylindrical polar Spherical polar Other

2.2.1.2 N/A

2.2.2 Atmosphere

2.2.2.1 Mean wind field

Mean wind parameterised

[M] Vertical profile [X] Horizontal field [X] Time-varying

[M] Stratification [X] Other

Vertical velocity profiles used

Logarithmic Other

Velocity at reference height Friction velocity specified

Mean wind modelled


14

[] Vertical profile [] Horizontal field [] Time variation Other

Zero wind allowed

2.2.2.2 Turbulence

Turbulence parameterised Turbulence modelled

2.2.2.3 Stratification

Stability ranges

neutral stable unstable

Stratification parameterised

Vertical density profile Horizontal density field Time-varying

Pasquill-Gifford stability categories used Monin-Obukhov length used

Monin-Obukhov Pasquill-Gifford conversion

Stratification modelled

[] Vertical density profile [] Time-varying Other

If a Pasquill-Gifford scheme is used it is converted internally to Monin-Obukhov. The Holtslag

method may also be used. This is also is converted internally to Monin-Obukhov.

2.2.2.4 Use of meteorological data

Meteorological data used

Temperature Humidity Cloud cover

Date/time Latitude/longitude Other

(Depending on selected option.)

2.2.3 Terrain

2.2.3.1 Terrain types available

Non-flat terrain

[X] Single slope [X] Slope segments (#) [X] General 2-D [X] Other


15

2.2.3.2 Wind orientation for non-flat terrain

Downslope Upslope General [] Other

N/A

2.2.3.3 Surface characteristics

Roughness length

Pre-defined surface types User-defined values

[X] Temperature [X] Other

2.2.4 Obstacles

2.2.4.1 Obstacle types available

Obstacle types available

2-D fence Cylindrical building/tank Cuboidal building

General shape 1-sided canyon 2-sided canyon Other

2.2.4.2 Obstacle distribution

Distribution of obstacles

Max number (10 buildings and 10 fences)

[U] Positions [U] Orientations [X] Other

2.2.4.3 Obstacle characteristics

Dimensions

[U] Horizontal [U] Vertical [X] Other

Structural characteristics

[X] Porous [X] Other


16

2.3 Model physics and formulation

2.3.1 Fundamental equations, initial conditions and boundary conditions

2.3.1.1 Equations used/set up

Volume Mass Momentum Width/radius

Energy Enthalpy Temperature

Concentration Species concentration

Contaminant mass fraction Species mass fraction

Turbulent kinetic energy k Turbulent dissipation Other

For instantaneous releases, the primary variables relating to cloud mass, volume and content are

the number of moles of contaminant gas, contaminant liquid, dry air, water vapour, and water

liquid in the cloud. For continuous releases the fluxes of those quantities are the primary

variables.

2.3.1.2 Dependent variables1

H W R

Uad u v w

E h T

c {ci} m {mi}

k Other

2.3.1.3 Independent variables (about 7)

spatial time Other

2.3.1.4a Model-type-dependent features of equation formulation (“screening tool”)

Physical quantities for which correlations are available

Concentration Other 1 H = depth; W = width; R = radius; Uad = advection velocity; = density; (u, v, w) = velocity components; E = energy;

h = enthalpy; T = temperature; c = concentration; ci = concentration of species i; m = contaminant mass fraction;

mi = mass fraction of species i, k=turbulent kinetic energy, = turbulent dissipation


17

Main quantities used to form dimensionless groupings

Length scale Velocity scale Ambient density Other

Main independent variables

Downwind distance Wind speed Release size/rate Other

Physical quantities fixed (or given limited values) for purposes of correlation

Wind speed Atmospheric stability Surface roughness Other

N/A

2.3.1.4b Model-type-dependent features of equation formulation (“integral”)

Use of similarity profiles in setting up equations

Dependent variables

Concentration Velocity Temperature Other

Profile shapes used

Uniform Gaussian Other

Profiles evolve smoothly from the heavy gas region into the passive dispersion region.

2.3.1.4c Model-type-dependent features of equation formulation (“shallow layer”)

Shape factors for depth variation

[] Constant [] Other

N/A

2.3.1.4d Model-type-dependent features of equation formulation (“CFD”)

N/A

2.3.1.5 Turbulence modelling

Turbulence models available

k- Isotropic buoyancy-modified k- Anisotropic buoyancy-modified k-

Algebraic stress Reynolds stress Other

N/A


18

2.3.1.6 Initial conditions

Initial conditions specified

[U] Source [U] Atmosphere [X] Terrain [U] Obstacles [X] Other

2.3.1.7 Boundary conditions

Boundary conditions specified

[] Source [] Atmosphere [] Terrain [] Obstacles

[] Cloud boundary Other

2.3.1.8 Domain

The domain on which the model runs

Overall extent Restrictions

2.3.2 Dispersion - advection

Advection velocity derived from wind profile

Average over cloud height Wind speed at fraction of cloud height Other

Acceleration of stationary cloud from rest calculated

Advection modelled directly

The advection speed is asymptotic to the wind speed at the centroid height.

2.3.3 Dispersion - gravity spreading

Gravity spreading parameterised

Gravity spreading locations

Plume sides Puff edge Cloud boundary Other

Gravity spreading characterisation

Constant Froude number Other

Upstream/lateral spreading of vapour blanket (continuous releases)

Upstream spreading Lateral spreading Other

Gravity spreading modelled directly


19

Lateral spreading is a combination of gravitational and dispersive effects with a smooth

transition from the régime where one dominates to the régime where the other dominates.

2.3.4 Dispersion - dilution

2.3.4.1 Dispersion modelling in the DENSE GAS régime

Turbulent diffusion Top entrainment Edge entrainment

Gaussian or similar parameterisation Other

(The “self-similar” parameterisation admits entrainment velocities.)

2.3.4.2 Dispersion modelling in the PASSIVE régime

Turbulent diffusion Top entrainment Edge entrainment

Gaussian or similar parameterisation Other

(The concepts are all related by the model. See e.g. Wheatley (1986a)).

2.3.5 Dispersion - concentration fluctuations

Concentration fluctuations considered

Indirectly.

2.3.5.1 Fluctuation calculations

Derived from data [] Modelled directly Other

2.3.6 Dispersion - concentration profiles

Concentration profiles considered

2.3.6.1 Types of concentration profiles used for dense gas dispersion region

Vertical profile

Uniform Gaussian Exponential decay Predicted Other

Lateral profile

Uniform Gaussian Uniform core with erf edges Predicted Other

Radial profile

Uniform Gaussian Uniform core with erf edges Predicted Other


20

2.3.6.2 Types of concentration profiles used for passive dispersion region

Concentration profiles applied

Uniform Gaussian Exponential decay Other

2.3.7 Thermodynamics

Thermodynamics considered

2.3.7.1 Sources of heat gain/loss for cloud

[M] Ambient air [M] Ground: forced convection [M] Ground: free convection

[U] Insolation [M] Phase changes [M] Other

2.3.7.2 Relations used for thermodynamic properties

Perfect gas law Antoine correlation Other

2.3.8 N/A

2.3.9 N/A

2.3.10 Transition to passive dispersion

2.3.10.1 Criteria for transition to passive dispersion

Richardson number small Density difference small

Rate of lateral spreading small Implicit Other

There is no discrete transition: the model moves smoothly from the primarily gravitational

régime to the primarily passive régime in a way which is guided by the Richardson number.

2.3.10.2 Treatment of passive dispersion

Dispersion parameters (’s) used Other


21

Dispersion parameter dependencies

Downwind distance Roughness length Atmospheric stability Other

2.3.11 Complex effects: aerosols

Aerosols considered

2.3.11.0 Sources of mass loss from two-phase clouds

Rain-out (two-phase) Other

2.3.11.1 Type of model

Homogeneous equilibrium Explicit droplet Other

2.3.11.2 Effects incorporated in aerosol model

Mass transfer between phases Heat transfer between phases

Interaction with atmospheric water Other

2.3.11.3 Cloud variables affected by aerosol

Temperature Density Velocity Concentration Other

2.3.11.4 Rainout and other effects considered

Mass loss

Rainout Obstacle impact Other

Droplet behaviour

Coalescence Break-up Other

2.3.12 Complex effects: terrain

Terrain considered


22

2.3.12.1 Cloud variables affected by terrain

Depth Width Velocity Concentration Other

N/A

2.3.12.2 Physical processes modified in formulation of terrain effects

Advection Entrainment Gravity spreading Other

N/A

2.3.12.3 Modification of ambient flow by terrain

Mean flow Turbulence Other

N/A

2.3.13 Complex effects: obstacles

Obstacles considered

2.3.13.1 Level of detail

Net effect Local details Other

2.3.13.2 Cloud variables affected directly by obstacles

Depth Width Velocity Concentration Other

Puff time-of-arrival

2.3.13.3 Physical processes modified in formulation of obstacles

Advection Entrainment Gravity spreading Other

2.3.13.4 Modification of ambient flow by obstacles

Mean flow Turbulence Other


23

2.4 Solution technique

2.4.1 Equation types

2.4.1.1 Type of main equations

Algebraic Ordinary Differential Partial Differential Other

2.4.2 Analytical solution methods

Significant analytical solution methods used

Methods developed as part of model Existing methods used Other

2.4.3 Numerical solution methods

Significant numerical solution methods used

Methods developed as part of model Existing methods used Other

DRIFT employs the DDRIV3 solver from CMLIB.

2.4.3.1 Computational mesh

Structured Unstructured Adaptive Multi-block Other

[] Mesh size/arrangement Advice provided

Variable step ODE solver.

2.4.3.2 Discretisation methods

Temporal discretisation of governing equations

Explicit Implicit Other

Spatial discretisation of governing equations

Finite difference Finite volume Finite element Other

Flux differencing applicable

1st order 2nd or higher order bounded


24

A variable step ODE solver is used solving a mixed set of both first order differential equations

and purely algebraic equations. Temporal steps are used for instantaneous releases; spatial steps

are used for steady continuous releases.

2.4.3.3 Solution of discrete system

Solution methods used

Segregated solver Coupled solver

Convergence

Convergence criteria Features to enhance convergence Other

Accuracy

Desired accuracy specified Other

2.5 Results or output available from model

2.5.1 Concentration-related output for steady situations

2.5.1.1 Plume centreline

Plume centreline trajectory

2.5.1.2 Pointwise concentration output

Pointwise concentration distributions

Centreline Longitudinal Lateral Vertical All points Other

Pointwise concentration output can be obtained using the COM interface to interrogate the cloud.

2.5.1.3 Derived concentration data

Concentration information - derived

Contours Flammable inventory

Lateral distance to given concentration

Longitudinal distance to given concentration Other


25

2.5.1.4 Concentration fluctuations

Concentration fluctuations

Averaging time PDF Other

Plume meander is accounted for in post processing by applying an averaging time correlation to the

concentration profiles following the approach of Nielsen et al. (2002).

2.5.2 Concentration-related output for time-varying situations

2.5.2.1 Cloud position

Position of cloud

Centroid Time-of-arrival Other

2.5.2.2 Pointwise concentration output

Concentration time history at a point

Coordinate origin for distributions

Cloud centroid Fixed origin Other

Distributions relative to origin

Centreline Longitudinal Lateral Vertical All points Other

2.5.2.3 Derived concentration data

Concentration information - derived

Contours Flammable inventory

Dose Toxic load Lateral distance to given dose

Maximum concentrations

Other

2.5.2.4 Concentration fluctuations

Concentration fluctuations

Averaging time PDF Other


26

2.5.3 Other information available

2.5.3.1 Temperature

Mean temperature Fluctuations

2.5.3.2 Further variables

Other variables available in output

Radius/width Height/depth Density

Velocity magnitude Velocity vector Pressure

Turbulent k.e. Turbulent dissipation Other

2.6 Sources of model uncertainty

2.6.1 Stochastic processes

Stochastic processes are always a source of uncertainty and dispersion is a stochastic process. The

uncertainty is reduced as much as possibly by predicting averaged concentrations.

2.6.2 Modelling assumptions

There may be some uncertainty in very stable atmospheric conditions where there are rather little

data for validation.

The models for transient and unsteady releases add some new ideas and assumptions and some

uncertainty exists in the specification of these models. However, in relation to other modelling

assumptions, the influence of these uncertainties is likely to be small.

2.6.3 Numerical method

No uncertainty. The solver is a commercial package designed using advanced concepts of

numerical mathematics to control errors.

2.6.4 Sensitivity to input

Any sensitivity to input is expected to reflect real sensitivity to initial conditions.


27

2.7 Limits of applicability

2.7.1 Sources

2.7.1.1 Primary origin

The source must be specified as a heavy or passive gas, aerosol cloud or momentum jet.

It may be instantaneous, continuous, finite duration, or time varying.

Low momentum area sources must be ground based.

2.7.1.2 Release type

Pools and jet sources are routinely considered with the program.

The pool model GASP produces an input file for DRIFT which converts what is a non-

instantaneous, non-steady release into a form which DRIFT can cope with explicitly. GASP

files can be imported directly.

2.7.1.3 Thermodynamic properties

Gas or aerosol cloud consisting of a mixture of contaminant gas, contaminant liquid, “dry

air”, water vapour and water liquid.

Any appropriate temperature.

Thermal effects of vaporisation and condensation are included.

For the cases of ammonia and hydrogen fluoride heat of solution is modelled; for hydrogen

fluoride oligomerisation is modelled.

2.7.1.4 N/A

2.7.2 Environments

2.7.2.1 Atmosphere

Wind speed and atmospheric stability are accounted for.

Primary parameters are friction velocity, roughness length, and Monin-Obukhov length.

The last can be input direct or estimated internally from Pasquill or Holtslag schemes.

2.7.2.2 Terrain

Flat

2.7.2.3 Obstacles

Multiple buildings and fences are admitted.


28

2.7.3 Targets/output

2.7.3.1 General situations

A large amount of output is available in graphical or tabular form.

2.7.3.2 Steady situations

See 2.7.3.1

2.7.3.3 Time-dependent situations

See 2.7.3.1

2.8 Special features

2.8.1 Capabilities

The ability to handle fence and building obstacles is perhaps unusual in an integral model. These

sub-models were produced and validated as a result of extensive Coordinating European Council

(CEC) research programmes, which demonstrated how they could be included quite generally in

almost any integral model.

2.8.2 Formulation

The variety of optional output variables is unusually large. This makes it fairly straightforward to

get an overall idea of the dispersion predictions – much more so, for example, than when simply

predicting concentrations at given points.

2.8.3 Mathematical aspects

Mathematically the model is a set of coupled ordinary differential, and algebraic, equations. These

have been the subject of study by numerical mathematicians for many decades, and sophisticated

methods exist for their solution which control errors rather precisely (in a way which popular,

easily-recodable methods like the simple 4th

order Runge Kutta and the 1st order Euler method do

not).

2.9 Planned scientific developments Discussions have taken place regarding high pressure CO2 releases, fire source terms and

introducing reactive substances to allow the model to interface to STAWaRS (a model of the

spreading and vaporisation of liquid pools resulting from spillages of water reactive chemicals).



3. User-oriented aspects of model

3.1 User-oriented documentation and help

3.1.1 Written documentation

User Manual Other

3.1.2 On-screen help and documentation

Context-sensitive help User Manual online Other

3.1.3 User support

Telephone support E-mail Training courses Other

3.2 Installation procedures

3.2.1 Medium

Diskette CD-ROM Internet download Other

3.2.2 Procedure

Copy files manually Installation program/script Other

3.2.3 User-friendliness

Description of procedure Help available during installation

Model Evaluation Report User-oriented aspects of model

30

3.3 Description of the user interface

3.3.1 General properties

Interactive Batch

Graphical Textual

3.3.2 Provision of input

Edit files directly Guided input Enter data on graphical forms

Edit existing files Edit default input Other

3.3.3 Information when model running

Numerical values Error/warning messages Status of calculation Other

3.3.4 Examining output

Graphical display of output

Integral graphical display facilities Separate graphical display program

Interactive selection Other

Examining numerical values

Integral numerical display facilities Separate numerical display facility

Output files Other

Numerical output can be examined in the graphical user interface or extracted to a spreadsheet

using the COM interface.

3.4 Internal databases

Internal databases available


31

3.4.1 Databases available

Material properties Scenarios Other

3.4.2 Access by user

Access from model Access outside model Other

3.4.3 Modification

General users Administrators only No users Other

3.5 Guidance in selecting model options

3.5.1 Main choices required

Primary origin details Source configuration

Substance released Properties of substance released

Atmospheric conditions Terrain Obstacles

Boundary conditions Initial conditions Computational domain

Computational mesh Discretisation Convergence criterion/a

Output required Other

3.5.2 Guidance in choices available

Sources User documentation Within interface Pre-existing input files Other

Type Worked examples Explicit advice Pre-set lists of values Defaults Other


32

3.6 Assistance in the inputting of data

3.6.1 Facilities available

Checks Valid range Valid type Entry has been made Other

Filing system Choose input file location Choose input file name

Special case: mesh and surface generation

Import file formats Built-in facility

Automatic User-defined Other

N/A

3.7 Error messages and checks on use of model beyond its scope

3.7.1 Facilities to trap inappropriate use

Facilities available

Checks on intermediate results

Warning messages given Other action taken

The program terminates if conditions are encountered which it cannot explicitly cope with.

3.7.2 Error/warning Messages

Occurrence During input During model run During output examination Other

Type Self-explanatory Look up online Look up in documentation Other


33

3.8 Computational aspects

3.8.1 Programming languages

Simulation engine Fortran 77 Fortran 90 C C++ Pascal Other

3.8.2 User programming

User-defined subroutines permitted

Required Optional

Boundary/initial conditions Properties Other

Main source code editable

All Specific parts only Other

No user programming of the model itself is possible. However, the user can interact with the

model through the COM interface and gain access to an increased range of output.

3.8.3 Execution times for specified problems

Seconds Minutes Hours Days

3.9 Clarity and flexibility of output results

3.9.1 Summary of model output

3.9.2 Facilities for display of results

Graphical formats Line (“x-y”) plots* Contour plots Vector plots Display on GIS Animated display Virtual reality Other

Numerical formats Tabulated output User interrogation Other

Hard copy facilities available directly from program Numerical output Graphical output Other

Additional software may be required


34

Necessary Optional

Required for Line (“x-y”) plots Contour plots Vector plots Display on GIS Animated display Virtual reality All Other

3.10 Suitability to users and usage

3.10.1 Suitability with respect to type of user

Background Engineer Consultant Regulator Academic Other

Type of experience Dispersion Fluid dynamics Thermodynamics Numerical methods Statistics Programming Consequence modelling Risk analysis

Length of experience Hours Days Weeks Months Years

3.10.2 Suitability with respect to type of usage

Intensity Specific incidents Hazard assessment Risk assessment Other


35

3.11 Possible improvements

Written documentation On-line help and documentation

Installation

User interface Selection/entry of input data

Information and checks while running

Output results

The finite duration and time varying models would benefit from further explanation in the

documentation.

3.12 Planned user-oriented developments

No user-oriented developments are planned.



4. Verification performed

4.1 Summary of verification

4.1.1 Parts of implementation verified

By using a commercial ODE solver, only cursory verification of the basic numerical method was

required, as this had done by the authors of the package. Thus it was considered that verification of

the specific solutions of DRIFT would be sufficient to ensure that the equations themselves had

been programmed correctly.

4.1.2 Verification undertaken

Verification of DRIFT version 2.31 is discussed in detail by Jones et al (1993). The method used

was essentially:

Examination of graphs of a large number of variables plotted against one another to check that

the results are “qualitatively sensible”;

Deeper follow-up tests where results were unexpected.

The latter included comparison of results with approximate analytic solutions – for example:

The expected increase of the top area of an instantaneously released cloud linearly with time;

The down-wind advection velocity;

The asymptotic power law decrease of concentration;

The integrated thermodynamic balance in the cloud;

Behaviour of height and width in continuous releases with momentum.

Verification of DRIFT version 3 is discussed in Tickle et al (2011a,b). The first of these documents

is a comparison of DRIFT version 3 against version 2.31 and several experimental datasets. This is

presented as a verification check that the models broadly agree where expected. The second

document covers two aspects; the lift-off of buoyant puffs in low wind and thermodynamic

modelling of HF-iso-butane mixtures. DRIFT’s buoyant puff rise model is based upon that of

Turner (1973) and comparison with an analytic solution to Turner’s model is used as a check of

DRIFT’s computer implementation. Tickle et al (2011b) demonstrate that DRIFT predictions for

HF mixtures are in agreement with a thermodynamic model for HF mixtures.

It is also noted that the instantaneous and continuous release modules share a lot of common code

so that its verification in one scenario carries over to the other.

Model Evaluation Report Verification performed

37

4.1.3 Quality assurance

Originally, DRIFT was developed with paramount regard to the principles expounded by the Model

Evaluation Group. DRIFT 3 was developed under ISO 9001 and TickIT software quality assurance.

4.2 Comments It is believed that sufficient verification was carried out to give confidence that the equations have

been programmed correctly.



5. Evaluation against MEP qualitative

assessment criteria

5.1 Scientific criteria

Key details of the model available for scientific assessment

Model based on accepted/published science.

Model accepts a credible source term

Model accounts for the effects of wind speed

Model accounts for the effects of surface roughness on dispersion

Model accounts for the effects of atmospheric stability on dispersion

Model accounts for passive dispersion

Model accounts for gravity-driven spreading

Model accounts for the effects of buoyancy on dilution

Numerical methods are based on accepted/published good practice

Model Evaluation Report Qualitative assessment

39

5.2 Output criteria

Model produces output suitable for assessment against MEP statistical performance measures



6. Validation performed and evaluation against

MEP quantitative assessment criteria

6.1 Validation already performed

Validation of DRIFT 2.31 is discussed in detail by Jones et al. (1993) and by Jones et al. (1994a).

A more detailed validation of the concept of homogeneous equilibrium of aerosol clouds is given by

Kukkonen et al. (1993, 1994) and later validation for hydrogen fluoride clouds is discussed by

Tickle (2001).

6.1.1 Validation exercises

Instantaneous releases

Jones et al. (1993) compare DRIFT extensively with the Thorney Island trials (isothermal

instantaneous releases). Jones et al. (1993) also present comparison with other models including

DRIFT’s predecessor DENZ and with the Britter-McQuaid Workbook (Britter and McQuaid,

1988).

Continuous releases

Jones et al. (1993) compare DRIFT with the continuous release field trials conducted in Lathen by

TÜV. Both momentum free and jet releases were included. These trials also provide data on

concentrations as a cloud passes a transverse fence downwind of the source and were used to

validate the fence model previously described – see Webber et al. (1994). Jones et al. (1993) also

compare DRIFT with the Lyme Bay chlorine trials from 1927, though the data are limited, and with

the Nevada Desert “goldfish” trials with HF. It is worth noting that none of the free parameters in

DRIFT needed be set differently to fit continuous and instantaneous releases.

Tickle et al. (2011a) present comparisons of DRIFT version 3 against DRIFT version 2 and a range

of experiments covering dense and passive releases including two-phase ammonia and hydrogen

fluoride.

(References to the experiments mentioned here are given in the validation documents cited.)

Model Evaluation Report Validation performed

41

6.2 Evaluation against MEP quantitative assessment criteria

6.2.1 Validation cases modelled

The validation cases were provided by the database proposed by Ivings et al. (2007). The database

holds sufficient information on a range of test configurations (datasets for field and wind tunnel

trials including release conditions, meteorological data, etc.) to permit model set-up and simulation.

The database also contains test results in the form of tabulated concentration and temperature data

against which model output can be compared. A total of 33 individual trials are included in the

database covering dispersion over water and land, with and without obstacles. The database is not

limited to LNG and includes dispersion of heavy gases such as freon 12 and sulphur hexafluoride

(SF6). A number of wind tunnel tests are also included in addition to the field trials. The wind

tunnel studies provide increased environmental control over their full scale counterparts and are

therefore result in useful validation datasets. However, wind tunnels cannot reproduce the

atmospheric conditions and heat transfer effects that govern full scale releases, so are limited to

isothermal releases.

DRIFT is intended for modelling releases over flat terrain and with simple obstructions such as

fences and buildings. Many of the test cases can be simplified to fit in with these capabilities

without introducing excessive approximations and assumptions. However, not all the test cases in

the database are applicable to the model evaluation. Table 1 lists the specific validation cases

modelled.

Table 1 Specific test cases modelled

Trial Field (F)

or Wind

tunnel

(WT)

Obstructed

(O) or

unobstructed

(U)

Trial/Case number

and/or

description

Maplin

Sands, 1980

F U

U

U

27 dispersion over sea



Burro, 1980 F U

U

U

U

3

7

8

9

Coyote, 1981 F U

U

U

3

5

6

Falcon, 1987 F O

O

O

1 with vapour barrier fence



Thorney

Island 1982-4

F U

U

45 continuous release

47 continuous release

CHRC, 2006 WT U

O

O

A without obstacles

B with storage tank & dike

C with dike

BA-Hamburg WT U

O

O

O

Unobstructed

Upwind fence

Downwind fence

Circular fence

BA-TNO WT U FLS – 3-D mapping


42

The field trials are primarily releases of LNG (Maplin Sands, Burro, Coyote and Falcon), other than

the Thorney Island trials which are releases of Freon 12. The latter set of trials includes releases in

stable atmospheric conditions, whereas the LNG field trials data are largely restricted to neutral or

unstable conditions. All of these field trials releases are over unobstructed terrain, with the

exception of the Falcon trials in which a large fence surrounded the LNG source. The wind tunnel

tests comprise recent work undertaken at the Chemical Hazards Research Center at the University

of Arkansas as well as two series of tests carried out as part of European Commission-funded

projects, referred to as BA-Hamburg and BA-TNO.

Excluded cases

A number of the test cases in the database are outside the scope of DRIFT and no attempt was made

to model these cases or approximate them into a form that could be modelled. Four of the BA-

Hamburg wind tunnel trials are releases onto sloping terrain. These releases were carried out in

conditions of zero windspeed which implies a certain amount of upwind spreading. This is not

treated in DRIFT (see Section 7 “Disadvantages”). Two BA-TNO datasets are not included due to

the nature of the arrangement of the concentration sensors.

Notes on the modelling approach

For continuous releases of LNG onto water, it can be assumed that the pool spreads out to a given

diameter and reaches a steady state. The evaporation rate can be equated to the spill rate to

determine the pool radius for input into DRIFT. This method relies wholly on accurate

determination of the evaporation rate which is specific to each situation, (and indeed on the validity

of the popular assumption that the equilibrium of spill rate and vaporisation rate is a stable one). In

addition to this basic method of estimating the pool diameter, the program GASP (Gas

Accumulation over Spreading Pools) version 4.2.3 (Webber and Jones, 1989) was used to model the

source for the unbunded spills onto water. GASP describes the spreading of evaporating or boiling

liquid pools on land or water and GASP runs can be imported into DRIFT. By default, GASP runs

are imported as “time varying” releases. Alternatively, GASP runs can be exported as steady

continuous releases by creating a legacy .DIN DRIFT input file which gives the average

vaporisation rate and pool radius. These can be imported into DRIFT 3.6.4 as a legacy input file.

For this review, all GASP runs were imported using the legacy input file method.

All the LNG spills were modelled as pure methane. Although LNG is typically 95% methane, the

main effects of the impurities are generally considered to be (a) in the late time period when the

methane has boiled off preferentially, and (b) throughout the boiling process in suppressing film

boiling which is much more readily achievable with pure methane. Accordingly the GASP runs did

not include film boiling.

DRIFT version 3.6.4 permits a certain amount of upwind plume spread from low momentum area

sources through “initial dilution over the source” (Tickle and Carlisle, 2011). This feature is active

by default and was used for all the low momentum area source runs.

All the wind tunnel trials were modelled at wind tunnel scale and were treated as isothermal

releases consisting of only contaminant gas and dry air. The wind tunnel releases differ from the

field trials in that they are truly continuous releases with little temporal concentration variation.


43

As indicated in Section 1.9, DRIFT files can be created and run using the GUI or from Microsoft

Excel using the “COM” interface. All the runs for this review were set up manually using the GUI

and run so that any error messages arising during running could be examined. An Excel spreadsheet

application was then written to batch run all the files and extract the required output. The two

outputs required for entry into the model validation database are the concentration and cloud width

(σy) at specific downstream distances and for specific averaging times. Whilst tabulated output is

available from the GUI, it is not possible to directly obtain values at specific downstream distances,

hence the necessity to use the COM interface.

The treatment of time averaging in DRIFT 3.6.4 is applied in post processing and does not affect all

the output variables so some care is required in obtaining output. For the finite duration and steady

state releases, concentrations were obtained using “maximumConcAtDistanceAndReceiverHeight”

and cloud widths, σy, using “slice.OutputValue(ContinuousOutputVariable_SigmaY_Meander).”

These variables are subject to the effect of averaging time. The time varying releases, the default

when importing GASP results, had to be treated slightly differently due to the way in which DRIFT

constructs the concentration profiles. These releases are the Maplin Sands, Burro and Coyote trials

involving spills onto water. Concentrations were again extracted using

“maximumConcAtDistanceAndReceiverHeight”. However, contact with DRIFT’s developers

indicated that σy as extracted previously does not account for the effects of longitudinal diffusion.

The suggested alternative was to obtain the maximum half width to Exp(-0.5) times the centreline

concentration. This returns σy for a Gaussian profile and is a good approximation in the dense

regime. Cloud width data were not available for all the water spills, so in practice it was only

necessary to use this alternative for the Burro and Coyote trials.

Validation case descriptions

Maplin Sands

The Maplin Sands trials were conducted by Shell Research limited in 1980 and consisted of 34

spills of liquefied gases onto the sea (see Puttock et al., 1982 and Colenbrander et al., 1984 a,b,c)

The aim of the trials was the study of combustion and dispersion of flammable gases. Both

continuous and instantaneous releases of propane and LNG were carried out. The spill point was

350 m offshore and liquid was delivered along a pipeline from the gas handling plant. A 300 m

diameter dike was constructed around the spill point to retain water so that trials could be conducted

at low tide. The water level behind the dike varied by 0.75 m. At the spill point a 150 mm diameter

pipe directed LNG vertically downward onto the water surface. For the three trials modelled, there

was no splash plate fitted under the pipe so the jet of LNG was free to penetrate into the water,

resulting in increased mixing. No measurements were made of the pool diameter and there is some

uncertainty over the evaporation rate given the increased mixing of the LNG and water. The two

methods discussed previously were used to derive the vapour source. The first assuming the

evaporation rate is equal to the spill rate (hence leading to a source diameter), the second was by

running GASP and importing the results. Due to the level of uncertainty in calculating an

evaporation rate, the GASP-DRIFT predictions were used in the database.

The Maplin Sands concentration data are listed in the database for a “short” 3 second averaging

time and this was applied to the DRIFT predictions. The results are shown in Figure 1. The Maplin


44

sands trials were carried out in neutral conditions apart from trial 27 which was carried out in

slightly unstable conditions. Closer agreement was obtained for this trial.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 100 200 300 400 500 600 700

Ma

xim

um

arc

wis

e c

on

cen

tra

tio

n (

%)


Maplin Sands 27

Measured

Predicted

a

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 50 100 150 200

Ma

xim

um

arc

wis

e c

on

cen

tra

tio

n (

%)


Maplin Sands 34

Measured

Predicted

b

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0 100 200 300 400 500

Ma

xim

um

arc

wis

e c

on

cen

tra

tio

n (

%)


Maplin Sands 35

Measured

Predicted

c

Figure 1 Measured and predicted concentration for Maplin Sands

Burro and Coyote

The Burro series of experiments were performed at the Naval Weapons Center (NWC), China Lake,

California in the summer of 1980 (see Koopman et al., 1982a,b). There were eight LNG spills of

between 25 m3 and 40 m

3 onto water. The Coyote series (see Goldwire et al., 1983) followed a

similar format and were intended for the study of vapour burn and Rapid Phase Transition (RPT)

explosions that had been observed in the Burro trails. For both the Burro and Coyote trials, a 25 cm

diameter line ran from the spill valve to a water test basin where it terminated 1.5 m above the

surface of the water basin. A splash plate was fitted below the outlet to limit penetration of the LNG

into the water. The water test basin had an average diameter of 58 m and a water depth of

approximately 1 m. The water level was approximately 1.5 m below the surrounding ground level.

The terrain downwind of the spill pond sloped upward at about 7 degrees for 80 m before levelling

out to an approximately 1-degree slope. No attempt was made to account for the level of the water

test basin and the terrain was assumed to be flat. This is valid for distances greater than 80 m where

the majority of the sensor arcs were positioned.


45

As with Maplin Sands, two estimates were made of the source area. A splash plate was fitted, so it

could be expected that this would decrease mixing and hence the evaporation flux. Both means of

calculating the evaporation rate resulted in overprediction of concentrations. The GASP-DRIFT

predictions were entered in the database.

The concentration measurements for both the Burro and Coyote trials are presented in the database

for two averaging times whilst cloud widths are only available for the longer averaging time.

DRIFT runs were carried out for the two averaging times and the results are shown in Figure 2. In

DRIFT version 3.6.4, the plume meander model used to determine the time averaged concentration

is only applied in the passive phase. The two averaging times resulted in almost identical

concentration predictions. Burro trials 7 and 9 were carried out in neutral conditions whilst the

conditions for trials 3 and 8 were slightly unstable and slightly stable respectively. Relatively good

agreement was obtained for the Burro 7 and for the Coyote trials 3 and 5 in slightly unstable

conditions. The Coyote 6 trial was carried out in neutral conditions and DRIFT overpredicted

concentration.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

0 50 100 150

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Burro 3

Measured 1 s

Predicted 1 s

Measured 100 s

Predicted 100 s

a

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 100 200 300 400 500

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Burro 7

Measured 1 s

Predicted 1 s

Measured 140 s

Predicted 140 s

b

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

0 200 400 600 800 1000

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Burro 8

Measured 1 s

Predicted 1 s

Measured 80 s

Predicted 80 s

c

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

0 200 400 600 800 1000

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Burro 9

Measured 1 s

Predicted 1 s

Measured 50 s

Predicted 50 s

d


46

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 50 100 150 200 250 300 350

Ma

xim

um

arc

wis

e c

on

cen

tra

tio

n (

%)


Coyote 3

Measured 1 s

Predicted 1 s

Measured 50 s

Predicted 50 s

e

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 100 200 300 400 500

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Coyote 5

Measured 1 s

Predicted 1 s

Measured 90 s

Predicted 90 s

f

0.0

5.0

10.0

15.0

20.0

25.0

0 100 200 300 400 500

Ma

xim

um

arc

wis

e c

on

cen

trati

on

(%

)


Coyote 6

Measured 1 s

Predicted 1 s

Measured 70 s

Predicted 70 s

g

Figure 2 Measured and predicted concentration for Burro and Coyote

Falcon

The Falcon trials were a series of large scale LNG spill tests carried out by the Lawrence Livermore

National Laboratory (LLNL) (see Brown et al. 1990). The trials were carried out at Frenchman Flat,

an extremely flat playa (salt flat) with little vegetation. The trials had the purpose of evaluating the

effectiveness of vapour fences as a mitigation technique for accidental releases as well as providing

a dataset for model validation purposes. The spills were onto a specially designed water pond

equipped with a circulating system to maximise evaporation. LNG was supplied to the spill area

along pipes by means of nitrogen drive gas. The spill pipes terminated in a multi exit “spider” to

provide a uniform distribution of LNG at the spill pond. The spider consisted of four arms of 11.6 m

length, each fitted with a capping level with the pond water surface to direct the LNG horizontally.

The spill pond was 40 m by 60 m and filled to a depth of approximately 0.76 m. An 8.7 m high

vapour fence structure 44 m by 88 m surrounded the spill pond, with the spill pond located at the

downwind end. Immediately upwind of the spill pond was a 13.3 m high and 17.1 m wide

“billboard” structure intended to generate turbulence typical of a storage tank within the vapour

fence.

The process of dispersion from within the fenced area is not straightforward due to the effects

introduced by turbulence in the wake of the upwind “billboard”. Whilst it may be possible to


47

estimate the vaporization rate from the pond, the rate at which vapour leaves the fenced enclosure is

altogether less certain. The simplest approach to modelling the Falcon trials with DRIFT is to

assume the entire fenced area forms the source. The vapour then leaves this fenced area at a rate

equivalent to that spilled. Alternatively, the source may be set as the spill pond and the fence model

in DRIFT used to account for the downwind section of the vapour barrier fence. Both methods were

tried and resulted in almost equal concentration and cloud width predictions. The values produced

by the former method were entered in the database.

DRIFT predictions were made for the two averaging times presented in the database and the results

are shown in Figure 3. As with the Burro and Coyote trials, both averaging times resulted in almost

identical concentration predictions. The Falcon trials were carried out in neutral/slightly stable

conditions apart from trial 1 which was in stable conditions. Relatively good agreement was

obtained for Falcon 3.

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

0 50 100 150 200 250 300

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Falcon 1

Measured 1 s

Predicted 1 s

Measured 100 s

Predicted 100 s

a

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

0 50 100 150 200 250 300

Ma

xim

um

arc

wis

e c

on

cen

trati

on

(%

)


Falcon 3

Measured 1 s

Predicted 1 s

Measured 150 s

Predicted 150 s

b

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 50 100 150 200 250 300

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Falcon 4

Measured 1 s

Predicted 1 s

Measured 250 s

Predicted 250 s

c

Figure 3 Measured and predicted concentration for the Falcon trials

Thorney Island

The Heavy Gas Dispersion Trials at Thorney Island were set up by the Health and Safety Executive

for the study of the dispersion of fixed volume releases of heavy gas. The programme was

subsequently extended to include continuous release trials 45, 46 and 47. The test area was largely

clear for a length of 2 km and a width of 500 m and flat to within 1 in 100. Gas was ducted below

ground from a 2000 m3 container to the release position. The release position consisted of a vertical


48

duct emerging at ground level with a 2 m diameter cap situated 0.5 m above the ground. This

arrangement provides for a well defined vapour area source for input directly into DRIFT. The gas

used in the Thorney Island trials was a mixture of Freon 12 and nitrogen. This was approximated in

DRIFT as pure inert gas with its molecular weight set to reflect that of the mixture.

The release durations for the Thorney Island trials were typically several minutes, however the

concentration data are presented for a 30 second averaging time. The results are shown in Figure 4.

Relatively good agreement was obtained for these trials and this may be due in part to the fact that

DRIFT’s parameters were originally set using these trials. DRIFT’s tendency to underpredict

concentration in the near field was reduced if the runs were carried out with dilution over source

switched off.

0.0

5.0

10.0

15.0

20.0

25.0

0 100 200 300 400 500

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Thorney Island 45

Measured

Predicted

a

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

0 100 200 300 400 500

Maxim

um

arc

wis

e c

on

cen

trati

on

(%

)


Thorney Island 47

Measured

Predicted

b

Figure 4 Measured and predicted concentration for Thorney Island

CHRC

The Chemical Hazards Research Center at the University of Arkansas carried out wind tunnel

experiments of the dispersion of CO2 over rough surfaces, with and without obstacles (Havens and

Spicer, 2005, 2006, Havens et al., 2007). The experiments were at a scale of 150:1 and consisted of

the following three cases:

Case A: Low momentum area source CO2 release without obstacles.

Case B: Low momentum area source CO2 release with tank and dike.

Case C: Low momentum area source CO2 release with dike only.

The wind tunnel was an ultra low speed boundary layer wind tunnel able to simulate the constant

stress layer of the atmospheric boundary layer. Airflow from the driving fans passed through a

72080 ft working area in which the floor was covered with smooth rubber matting on which the

roughness elements were mounted. A 150:1 model of the tank and dike was installed on the floor

surface. The dike was square with an inner dimension of 63 cm and a wall height of 3.7 cm and the

tank model was 31 cm in diameter with a spherical dome top and an overall height of 28.3 cm. The

tank was located in the centre of the dike on a mesh screen through which the gas flowed. The gas

was 33.4 standard litres per minute (slpm) CO2 with 0.5 slpm propane used as a tracer.


49

Case A consists of dispersion from a well defined area source. For this case, DRIFT was initiated

using a vapour source of half width equal to that of the mesh screen.

Case B is more problematic as the storage tank sits in the centre of the source surrounded by the

dike. The tank was modelled in DRIFT as a building set downwind from the source. A fence was

positioned downwind from the tank. This approximation assumes that the entire cloud is modified

by both the tank and the fence. In reality, the presence of the tank causes an initial birfurcation of

the cloud at the source.

The dike in case C was modelled as a fence positioned downwind from the area source.

The results are shown in Figure 5. Relatively poor agreement in the near field was obtained for

cases A and C. In these cases, switching off dilution over source resulted in increased near field

predictions.

CHRC A

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 0.5 1 1.5 2 2.5 3 3.5 4


Ma

xim

um

arc

wis

e c

on

ce

ntr

ati

on

(%

)

MeasuredPredicted

a

CHRC B

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 0.5 1 1.5 2 2.5 3 3.5 4


Ma

xim

um

arc

wis

e c

on

ce

ntr

ati

on

(%

)

MeasuredPredicted

b

CHRC C

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 1 2 3 4 5 6


Ma

xim

um

arc

wis

e c

on

ce

ntr

ati

on

(%

)

MeasuredPredicted

c

Figure 5 measured and predicted concentration for CHRC wind tunnel trials


50

BA-Hamburg

The BA-Hamburg trials were conducted in an open circuit wind tunnel at the Meteorological

Institute at the University of Hamburg (UH) (see Nielsen and Ott, 1996). The trials comprised

experiments on a wide range of different geometries in an open circuit wind tunnel using a floor

level area gas source of sulphur hexafluoride (SF6). The source consisted of an array of holes

covering a 7 cm diameter circular area flush with the tunnel floor. There was a circular cap

approximately 9 cm in diameter and 5 cm above the source. Five different arrangements of

obstacles are included in the database, of which four were modelled with DRIFT. These are an

unobstructed reference case, an upwind fence, a downwind fence and a circular fence surrounding

the source. Both the upwind and downwind fences are semicircular. There are 2 releases for each

case, differing in sensor location or fence height/diameter, requiring a total of 8 runs of DRIFT.

The unobstructed reference cases were modelled as dispersion from the area source. For the

downwind fence cases, the fence was assumed to be straight. The fence model in DRIFT acts upon

the cloud and therefore upwind fences have no effect, resulting in the two upwind fence cases being

effectively unobstructed. The cases with a circular fence surrounding the release point reduce to a

problem similar to the Falcon trials. Again, this can be considered as an arrangement of fences or as

an area source of the same diameter as the fence, the latter assuming that the entire fenced area

surrounding the source fills with gas. As with the Falcon trials, this approach was found to give

better predictions and these results were entered into the database. The results are shown in

Figure 6. Generally good agreement was obtained for these wind tunnel trials other than for the first

unobstructed case. As with the unobstructed CHRC A trial, the near field concentration was

underpredicted.

BA-Hamburg unobstructed

0.0

1.0

2.0

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BA-Hamburg unobstructed (2)

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BA-Hamburg upwind fence

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BA-Hamburg upwind fence (2)

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51

c d BA-Hamburg downwind fence

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BA-Hamburg downwind fence (2)

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BA-Hamburg circular fence (2)

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Figure 6 Measured and predicted concentration for BA-Hamburg wind tunnel trials

BA-TNO

The BA-TNO wind tunnel trials used SF6 gas released from a floor level source at a scale of 78:1.

The source consisted of a 107 mm diameter orifice covered in a 50% porosity gauze to give a low

vertical momentum release. The single TNO-FLS experiment used in this review was a continuous

release with an unobstructed 3D measurement field. The results are shown in Figure 7. Near field

concentrations were underpredicted as with the CHRC A and unobstructed BA-Hamburg trials.


52

BA-TNO FLS

0.0

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Figure 7 Measured and predicted concentration for BA-TNO wind tunnel trials

6.2.2 Model performance for key statistical evaluation parameters

The Statistical Performance Measures used in the evaluation database are those recommended by

Ivings et al. (2007) and reproduced in Table 2.

Table 2 SPM definitions

SPM Definition

Mean Relative Bias

mp

pm

CC

CCMRB

2

1

Mean Relative Square Error

2

2

4

1mp

mp

CC

CCMRSE

FAC2: the fraction of

predictions which are within a

factor of two of the

measurements

0.25.0

m

p

C

C

Geometric mean bias

p

me

C

CMG logexp

Geometric variance 2

logexp

p

me

C

CVG

The angle brackets used in the formulas denote an average over all the measured/predicted pairs of

concentration entered in the database. These include both the field and wind tunnel trials. As noted

previously, the wind tunnel trials have been modelled at wind tunnel scale as this is possible within

DRIFT and removes any uncertainty over the effects of scaling the data. Modelling the wind tunnel

trials at both scales would result in an undue bias to those datasets.

SPM have been calculated for two parameters: the maximum arc-wise concentration at various

downwind distances and the plume width at those distances. In the database, it is recognised that not

all models are capable of accounting for obstacles. Therefore, the SPM are split into two basic


53

groupings: dispersion in the presence and absence of obstacles. Additionally, the data in the

database are presented for differing averaging times depending on the specific dataset. SPM are

computed separately for nominally “short” and “long” averaging times. These correspond to peak

instantaneous concentration (averaging times typically in the order of seconds) and a maximum

average concentration over a longer time period (typically minutes).

Values of SPM corresponding to an acceptable model have been put forward by Ivings et al (2007)

and have been adopted in this review. The proposed acceptance criteria are as follows:

A mean bias within 50% of the mean, corresponding to: –0.4<MRB<0.4 and

0.67<MG<1.5.

A scatter of a factor of three of the mean, corresponding to: MRSE<2.3 and VG<3.3.

The fraction of model predictions within a factor of two of observations to be at least 50%.

6.2.3 Evaluation against quantitative assessment criteria

Tables 3 and 4 present the values of SPM as computed for the pairs of predicted concentrations and

measurements.

Table 3 SPM for maximum arc-wise concentration for short time averages

SPM Value for unobstructed

cases (Group 1)

Value for obstructed cases

(Group 2)

Acceptability range

MRB -0.11 -0.53 -0.4< MRB < 0.4

MG 0.86 0.56 0.67 < MG < 1.5

MRSE 0.36 0.53 MRSE < 2.3

VG 1.64 1.97 VG < 3.3

FAC2 0.79 0.56 0.5 < FAC2

Table 4 SPM for maximum arc-wise concentration for long time averages


cases (Group 1)


(Group 2)

Acceptability range

MRB -0.08 0.06 -0.4< MRB < 0.4

MG 0.92 1.06 0.67 < MG < 1.5

MRSE 0.85 0.48 MRSE < 2.3

VG 3.13 1.86 VG < 3.3

FAC2 0.30 0.59 0.5 < FAC2

The SPM in the first column in Table 3 are generated from the Maplin Sands, Burro and Coyote

trials. These are unobstructed spills and the concentration comparisons have been made against the

short averaging time (corresponding to 3 seconds for Maplin Sands and 1 second for Burro and

Coyote trials). The resulting SPM values fall with the acceptance criteria outlined previously. Some

further information can be gained from the values of the values of MRB and MG. As MRB is

negative and MG is less than 1, there is a tendency to overpredict the concentration. Inspection of

the graphs of concentration versus downwind distance show this is true for the Burro and Coyote

trials, but not for the Maplin Sands trials where the concentration was generally underpredicted.


54

However, it can be noted that the Maplin Sands trials are a smaller dataset and therefore have lower

influence on the overall result than the Burro and Coyote trials. In all three cases, the spills are onto

water which carries an additional element of uncertainty in the specification of the source term.

Since GASP has been used to model the source for the spills onto water, the effectiveness of two

models is being evaluated.

The second column in Table 3 contains SPM generated from the Falcon trials exclusively. The

value of MRB is outside the acceptable limit, and indicates that DRIFT is overpredicting the

concentration. When taken in conjunction with the plots of concentration versus distance, it is

evident that the near-field values, particularly for Falcon trials 1 and 4 are overpredicted by as much

as a factor of 4. The comparison of DRIFT with the Falcon trials needs to be viewed with some

caution due to the complexity of the source. The two methods used to approximate the source

(which both produced similar results) will tend to overestimate the vapour generation rate and hence

predicted concentrations.

The first column in Table 4 lists SPM for datasets which present unobstructed concentration

measurements for a long time average. These include the Burro, Coyote and Thorney Island

continuous release trials in addition to the CHRC and Hamburg wind tunnel trials. The negative

value of MRB and MG less one indicate that, overall, DRIFT is overpredicting concentration. This

is the case for the Burro and Coyote trials, whereas the concentrations for the wind tunnel trials

were generally underpredicted. As a result, the value of FAC2 falls outside the acceptable range.

The second column in Table 4 relates to the obstructed Falcon trials as well as the CHRC and

Hamburg wind tunnel trials. The values of MRB and MG indicate that DRIFT is underpredicting

concentration, however all SPM are within the acceptable range. It is worth noting that in the

Hamburg upwind fence cases (included in the obstructed cases), the obstacles are not included in

the calculation, as the wake of a fence does not influence the calculation. The Falcon trials and

Hamburg circular fence cases were modelled without representing the fences and therefore there is

greater uncertainty in the source configuration. However, particularly good agreement was obtained

for the Hamburg downwind fence cases where the obstacles are more readily defined.

Table 5 lists SPM computed for cloud width predictions. In the database, cloud widths were only

calculated where concentration values were available for the long time average datasets.

Furthermore, cloud widths were not calculated for those datasets where the cloud shape was not

well defined (for example had bifurcated) or had veered excessively from the sensor array (see

Coldrick et al., 2009). The SPM for cloud width therefore represent a smaller set of paired

comparisons than for the concentration measurements. The values of SPM fall well within the

acceptance criteria. Values of MG less than one and negative values of MRB show that DRIFT

tends to slightly overpredict the cloud width for the obstructed cases. Conversely, the cloud widths

for the obstructed cases are slightly underpredicted.

Table 5 SPM for cloud width


cases (Group 1)


(Group 2)

Acceptability range

MRB 0.04 -0.19 -0.4< MRB < 0.4

MG 1.04 0.82 0.67 < MG < 1.5

MRSE 0.06 0.12 MRSE < 2.3

VG 1.07 1.14 VG < 3.3

FAC2 1.00 0.92 0.5 < FAC2


55

Figure 8 is a graphical depiction of the results of Tables 3, 4 and 5 along the lines of Hanna et al

(1993). The vertical lines of MG = 0.5 and MG = 2 represent factor of two overpredictions and

underpredictions about the mean. The lines VG = exp(ln MG)2 define the minimum possible values

of VG for a given value of MG.

Figure 8 Graphical depiction of MG and VG

6.3 Conclusions

Quantitative evaluation of DRIFT was performed by comparing DRIFT predictions against

experimental data contained in a database of dense gas dispersion experiments. The basis of the

quantitative evaluation was a set of statistical performance measures derived from measured and

predicted gas concentrations and cloud widths. The test cases are split into groups depending on the

averaging time used in processing the experimental data and according to the absence or presence

of obstacles. Five statistical performance measures (SPM) were calculated for each grouping.

Certain test cases in the database were omitted as they cannot be modelled using DRIFT. A number

of the test cases involved spills onto water and therefore required the use of an additional model to

estimate the source term.

The assumptions made in modelling each test case result in some uncertainty in the DRIFT

predictions. This can be demonstrated in the variation in predictions that can be obtained using, for

example, different source terms. In addition to this, the DRIFT predictions have associated with

them a confidence interval indicative of their inherent uncertainty. It is tempting to assume that the


56

experimental datasets are correct as they contain measurements. However, the experimental values

of concentration are not direct measurements but an approximation (via several steps of processing)

of a temporally and spatially varying flow. The nature of large scale dense gas dispersion

experiments in the field means that they cannot be well controlled. Therefore, several nominally

identical releases would each give different results. In this evaluation, single DRIFT runs have been

compared with single releases.

With the above in mind, the DRIFT predictions generally compared well with the experimental

data. SPM values within recommended levels were obtained for most of the comparison groups. For

the trials which involved spills onto water there is a high level of uncertainty surrounding the

specification of the source term and DRIFT tended to overpredict concentration. To some extent,

the SPM may mask cases where overpredictions and underpredictions cancel each other out and

result in what appears to be good model performance. On the whole, DRIFT tended to underpredict

cloud width though the SPM values were within acceptance criteria. Cloud widths were not

available for all the experimental datasets so represent a smaller set of comparisons that the

concentration data. Furthermore, the determination of cloud width for the field trials is not an exact

process and in many of the cases gives a long time average representation of the cloud.



7. Conclusions

General model description DRIFT is an integral model for instantaneous and steady continuous releases of a heavy or passive

gas or aerosol. It models clouds of up to five components: contaminant gas, contaminant liquid,

“dry air”, water vapour, and water liquid in homogeneous equilibrium. Its thermodynamic module

accounts for heat of vaporisation and (in the case of ammonia and hydrogen fluoride) heat of

solution.

The basic model is derived in terms of a set of coupled differential equations (in time for

instantaneous releases, and in downstream distance for steady continuous releases).

In the instantaneous case, the independent variables are cross-wind and down-wind radii (the cloud

has an elliptical plan), down-wind advection momentum, temperature and the number of moles of

the five components in the cloud. Cloud volume and height are derived quantities.

In the steady continuous release model, cloud width, temperature, and down-wind advection

momentum are independent variables, together with the fluxes of the various components.

A finite duration model is available based upon the same equations as the steady continuous model

but allowing for longitudinal diffusion. This has been extended to give a time varying model which

essentially involves running a number of instances of the finite duration model and re-composing

the results in post processing.

Sub-models are included for entrainment rates and heat transfer.

The model admits obstacles in the form of a number of buildings and a number of fences across the

wind.

Scientific basis of model The model equations are based broadly on the assumption of self-similar profiles which allow the

formulation of equations for “integral properties” of the cloud. The profiles assumed evolve

smoothly from the dense gas phase into the passive regime, and can assume the forms of “top hat”,

Gaussian, and exponential in various limits.

Entrainment is modelled as a function of a bulk Richardson number (which is the most appropriate

dimensionless measure of whether the cloud is heavy or passive).

Some of the sub-models (for example entrainment and its dependence on Richardson number) were

formulated in a way which was guided by best practice in earlier models, and tuned in the process

of validation. Others (for example the obstacles) were formulated as a result of research sponsored

by HSE and the CEC, in which collaborators produced data that enabled validation to be done.

Model Evaluation Report Conclusions

58

The models for transient and unsteady release models add some new ideas and assumptions, but a

number of details are not entirely clear. This arises as much from a lack of explanation in the

specification and documentation as it does from any shortcomings in the model itself.

Limits of applicability

Source: The source must be specified as a heavy or passive gas, aerosol cloud or momentum jet. It

may be instantaneous, continuous, finite duration, or time varying. Low momentum area sources

can only be specified at ground level.

Environment: The model permits a certain amount of upwind spreading in low wind conditions, but

cannot operate in nil wind. Some care is therefore required in low wind conditions. The model only

accounts for flat terrain.

Targets/output: In the original version of DRIFT, the treatment of time averaging was somewhat

ad-hoc. In the passive regime, fitting to particular dispersion experiments meant that the predicted

concentration represented an average over typically 10 minutes. In the dense gas regime, fitting to

concentrations averaged over a shorter period (in the order of seconds) resulted in a different

prediction. Given the use of the model, this approach was not deemed to be inappropriate. DRIFT

version 3 incorporates a time averaging function which is applied in post-processing to determine

the concentration profiles over a user specified averaging time.

User-oriented aspects of model DRIFT 3.6.4 can be operated in a number of ways. DRIFT files can be created and run using the

GUI, or the model can be run entirely from the COM interface via a Microsoft Excel spreadsheet to

which results can be output. A third option (and the one used for this review) is that DRIFT files set

up using the GUI can be run in batch using the COM interface. This allows output not otherwise

available from the GUI to be extracted.

DRIFT runs are straightforward to set up using the GUI as there is context sensitive help and the

various input options are covered in the user manual. A large amount of output is available from the

GUI in terms of plots of flammable or toxic limits and tabulated data. Some care is needed when

examining the tabulated output as certain output variables are not subject to the effect of the user

input cloud meander averaging time (a note of this is made in the user manual). This is also relevant

to some of the output obtained using the COM interface. For example, “cloud” output variables are

treated slightly differently to “slice” output variables. This is perhaps more relevant in the context

of this review than for risk assessment purposes where there is less of a requirement for non-

standard output. In particular, the format of the model evaluation database uses concentration and

cloud width output at fixed downstream distances and this is not directly available from the DRIFT

GUI.

Verification performed This is discussed in detail by Jones et al. (1993) and by Tickle et al. (2011b,c). The method used

was essentially:


59

Examination of graphs of a large number of variables plotted against one another to check

that the results are “qualitatively sensible”;

Deeper follow-up tests where results were unexpected.

The latter included comparison of results with approximate analytic solutions – for example:

The expected increase of the top area of an instantaneously released cloud linearly with

time;

The down-wind advection velocity;

The asymptotic power law decrease of concentration;

The integrated thermodynamic balance in the cloud;

Behaviour of height and width in continuous releases with momentum.

It is also noted that the instantaneous and continuous release modules share a lot of common code

so that the model’s verification in one scenario carries over to the other.

Evaluation against MEP qualitative assessment criteria All details of the model are available for assessment. Any problems are probably more to do with

the volume of literature available, rather than a lack of it. It must be noted however that much of

this was produced as UKAEA reports, then available through Her Majesty’s Stationery Office.

Owing to privatisation of the relevant part of UKAEA into AEA Technology in 1996, and

subsequent sale to ESR Technology, those reports may be harder to find today.

All aspects of the model were based on extensive scientific research. Some of these have a

structure in common with earlier models, but none of these was simply taken on trust.

All the most important influences on heavy and passive gas dispersion are taken into account.

The numerical procedures are commercial off-the-shelf libraries written by experts in numerical

mathematics.

Validation performed and evaluation against MEP quantitative

assessment criteria

Evaluation of DRIFT against MEP quantitative assessment criteria was performed by running the

model against a database of dense gas dispersion experiments. The database contains a range of

dispersion experiments over land and sea, the former being with and without obstructions such as

fences. A number of wind tunnel trials are included in the database in addition to the large scale

field trials. The large scale releases are of liquefied natural gas (LNG) or freon 12/nitrogen whilst

the wind tunnel releases are of sulphur hexafluoride or carbon dioxide. The comparison parameters

used in the evaluation were values of concentration and cloud width at various distances downwind

from the source. The basis of the quantitative evaluation was a set of statistical performance

measures derived from measured and predicted gas concentrations and cloud widths.


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DRIFT predictions were generally in good agreement with the experimental data and resulted in

SPM values within recommended levels for most of the comparison groups. A number of the test

cases involved LNG spills onto water and these required the use of a source model to estimate the

vapour generation rate for input into DRIFT. For these cases, DRIFT tended to overpredict the

concentration, which is heavily dependent on the specification of the source. However, particularly

good SPM results were obtained for cloud width predictions.

Advantages and disadvantages of model

Advantages

The model runs very quickly.

It can output, on request, the evolution of a very large number of variables, allowing easy

qualitative understanding of what it is predicting.

Documentation is available which covers model specification, user guide and validation.

The model is based on a large amount of scientific research through the 1980s and early 90s.

Disadvantages

The model is complicated. In attempting to get a physically well-founded understanding of

the behaviour of heavy clouds into the form of an integral model (whose solutions can be

found very rapidly on a computer) the original DRIFT 1 was a more complicated model than

most of its contemporaries. The extended scope of DRIFT 3 follows the same aims and it

is more complicated still.

During the course of this review, it became apparent that care needs to be taken in relation to

the output variables from the COM interface and how these relate to the model itself. For

example, some output relates to the cloud before any post-processing calculations have

taken place and therefore gives rise to what appear, at first sight, to be erroneous results.

Pool evaporation results imported from GASP are treated only as "time varying" by default

and this results in a more limited range of output from the GUI.

The model only accounts for flat terrain.

The finite duration and time varying models would benefit from further explanation in the

documentation.


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Suitability of protocol for assessment of model The main limit of the protocol for assessment of models is quite general: it guides the assessor of a

particular model by questions of a very generic nature. Even this reviewer as one of the authors of

this version of the form finds this less than perfect.

Secondly, DRIFT, in particular, is a model which treats dispersion of a wide array of gases and

aerosols on the same basis, with one or two more specific thermodynamic options for the reaction

with water of ammonia and hydrogen fluoride only. Reviewing it specifically for LNG (as is the

brief here) is therefore not entirely appropriate. For example, much of the validation was done with

other substances, but should nevertheless give confidence in predictions for LNG clouds.

Published by the Health & Safety Executive 02/17


RR1100

www.hse.gov.uk

The Health and Safety Executive (HSE) uses gas dispersion modelling in its assessment of the hazards and risks posed by toxic and flammable substances stored at major hazards sites. To update its dispersion modelling capability, HSE recently commissioned ESR Technology to develop a new version of the gas dispersion model DRIFT (Dispersion of Releases Involving Flammables or Toxics). The new version of the model, DRIFT Version 3 (DRIFT 3), includes a significant number of modelling enhancements over the version of DRIFT previously used within HSE (DRIFT 2.31). These include the extension of the model to treat buoyant plumes and time varying releases. Prior to DRIFT 3 being adopted for use by HSE, it must undergo thorough evaluation and assessment.

This report describes the evaluation of DRIFT version 3.6.4 in accordance with a Model Evaluation Protocol originally developed for the evaluation of liquefied natural gas (LNG) vapour dispersion models. The protocol sets out a method of scientific assessment, verification and validation for heavy gas dispersion models where the results are recorded in a model evaluation report (MER). Overall, the evaluation exercise found DRIFT version 3.6.4 to be fit for purpose. This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

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