bryce glenn _0570731_ meng final year project

8/12/2019 Bryce Glenn _0570731_ MEng Final Year Project

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Masters of Mecha

Validation

Technologies for S

wit

In Partial Fulfilmen

Mechani

Presented to theEdi

S

ical Engineering with Manage

f Computational Fluid Dyn

imulation of Fire and Smok

in Large Indoor Spaces

t of the Requirements for the Mas

cal Engineering with Managemen

chool of Engineering of the Uninburgh (United Kingdom) by

Bryce Glenn

(s0570731)

Submitted April 2010

pervisor: Dr Stephen Welch

ent Thesis

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ters Degree

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Masters of Mechanical Engineering with Management

April 22, 2010

I. Personal StatementThe work contained within this thesis is essentially a continuation from previous analysis of

researchers including my dissertation supervisor Dr Stephen Welch and previous masters

students including Frazer MacDonald and Roslyn Clarke. While the thesis draws on

Computational Fluid Dynamics (CFD) predictions made using fire specific CFD software no

prior attempts have been made within the body of work to use a none fire specific, multi-

purpose CFD package to model the problem.

A great deal of background learning had to take place before the thesis project work could

begin fully. Modules in CFD and fire science and fire dynamics were completed as part of the

final year of my degree course. These modules aided greatly in learning the background

theory to the project.

The thesis draws theoretical knowledge from both the previous body of work, and my own

research on the topic. Two modelling techniques have been used in separate simulations

during the project work, the heat source method and the eddy break up combustion method.

While the CFD software used did contain good resources to assist with the setup of the heat

source method, the implementation of the eddy break up combustion method was entirely my

own.

There were a number of problems throughout the project, mainly concerning the operation of

the CFD software and the non convergence of the eddy break up combustion models. A

significant amount of time was invested in researching and solving the convergence issues

with a large number of trial simulations being completed. With the excellent help and advice

of Dr Stephen Welch the convergence issues were addressed and overcome. It is definitely

true that we can often learn more when things dont go to plan the first time than we would

otherwise.

I am pleased with the outcomes to the project believing that the initial aims set out at the

beginning of the project have been met. I also firmly believe that I have learned a great deal

through completing the project both about CFD, fire science as well as an appreciation of fire

safety engineering

I declare that all work carried out within my thesis is my own and any intellectual

contribution derived from other sources has been appropriately referenced.

Signed:

Bryce Glenn

Date: 22/04/2010


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II. SummaryThe complex architecture and designs of modern innovative buildings can often make

application of prescriptive building regulations with respect to fire safety engineering

challenging. Often restrictions are placed on a building which stifles the creative appearance

and functionality of the buildings design. There is a definite requirement that innovative

buildings be designed under a performance based design process rather than a prescriptive

process with respect to fire safety engineering. Computational Fluid Dynamics (CFD) is one

such performance based tool. The abilities, limitations and typical accuracy of CFD

techniques must be understood in order that confidence can be built up in their use.

The most effective approach in verifying the predictive abilities of CFD is to compare

predictions made against experimental data, measured from fire scenarios under well

specified environmental conditions. Data had been made available from a series of fire testsagainst which verification could take place.

The thesis author has modelled a number of the experimental fire scenarios using the multi-

purpose CFD package STAR-CCM+. Two different CFD modelling methods were used; the

eddy break-up combustion method and the heat source method. Comparison would allow an

assessment of the models relative predictive abilities. A number of similar simulations were

conducted with key parameters such as radiation modelling, temporal disczeration order and

mesh refinement altered to assess the influence these parameters have on the simulation

outcomes.

Predictions have been made regarding the gas temperature and the thermally driven velocity

of the fluid flow field within the experimental geometry. These predictions were compared

against the experimental data as well as predictions made by previous authors using fire

specific CFD technology.

Overall the predictive abilities of CFD simulations vary depending on the experimental

scenario modelled. In general the heat source models with radiation solvers activated showed

the best prediction capabilities while the EBU combustion models tended to overestimate the

temperature profiles. In some experimental scenarios the CFD predictions were encouraging,

but in general the predictions tended to overestimate the flow field gas temperatures whencompared to the experimental values particularly on the first floor level.

It was also determined that additional refinement of the simulation meshes would improve

predictive capabilities and tend towards a grid independent solution. Mesh refinement was

not always feasible for the purposes of the thesis due to the long simulation times which were

required.


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III. AcknowledgementsI would like to thank my project supervisor Dr Stephen Welch for his consistent advice,

knowledge input and patience throughout this project.


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V. GlossaryBRE Buildings Research Establishment

CFD Computational Fluid Dynamics

DNS Direct Numerical Simulation

DOM Participating Media Radiation

FDS Fire Dynamics Simulator

HRR Heat Release Rate

IMS Industrial Methylated Spirits

k- A Turbulence model used in RANS codes to solve the unknown relationships between

the Reynolds stresses due to random turbulent fluctuations and the mean flow field

quantities.

LBTF Large Building Test Facility

LES Large Eddy Simulation

RANS Reynolds Averaged Navier Stokes

VI. Chart Legend Key(0.4m) Indicates the base size used in the polyhedral heat source mesh

1st First Order Temporal Discretization

2nd

Second Order Temporal Discretization

EBU Eddy Break-Up Combustion Simulation

HS Heat Source Simulation

No Rad Simulation without Radiation Solvers Activated

Rad Simulation with Radiation Solvers Activated

Ref Mesh Mesh Refined in Run 9 EBU Simulations to give 77 Mesh Resolution around

Fire


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VII. Summary of ResourcesThe work contained within this thesis did not require any physical resources custom for the

thesis or workshop time typically required by Mechanical Engineering project work. The

work did however rely on a large computational effort supplied by the University of

Edinburgh, School of Engineering. Computing resources particularly utilised were the

machines within Teaching Lab F and via remote login through NX Client the Teaching Lab C

Sun Microsystems machines both running Scientific Linux (64 bit) as an operating system.

The thesis also required extensive use of use of the CD-adapco Computational Fluid

Dynamics software STAR-CCM+ version 4.02.007. The CD-adapco licensing terms require

that for each processor core on which a simulation is run a separate licence is required. As

simulations carried out for this thesis utilised extensively the parallel processing capabilities

of STAR-CCM+ it is acknowledged that multiple academic licenses were used

simultaneously.


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VIII. Table of ContentsI. Personal Statement ............................................................................................................. 2

II. Summary ............................................................................................................................. 3III. Acknowledgements ............................................................................................................ 4

IV. Notation .............................................................................................................................. 5

V. Glossary .............................................................................................................................. 6

VI. Chart Legend Key ............................................................................................................... 6

VII.Summary of Resources ....................................................................................................... 7

1. Introduction ...................................................................................................................... 10

2. Project Aims and Objectives ............................................................................................ 12

3. Performance Based Fire Safety Methods (Literature Review) ......................................... 13

3.1. Scale Models ............................................................................................................. 13

3.2. Zone Models .............................................................................................................. 13

3.3. Computational Fluid Dynamics ................................................................................ 14

4. Computational Fluid Dynamics Technology (Literature Review) ................................... 16

4.1. Reynolds Averaged Navier Stokes (RANS) ............................................................. 17

4.2. Large Eddy Simulation (LES) ................................................................................... 18

5. Experimental Geometry (Literature Review) ................................................................... 19

6. Fire Scenarios (Literature Review) ................................................................................... 22

7. Instrumentation Setup (Literature Review) ...................................................................... 24

7.1. Gas Temperature ....................................................................................................... 24

7.2. Gas Velocity .............................................................................................................. 24

8. CFD Fire Modelling Setup ............................................................................................... 25

8.1. Geometry Preparation ............................................................................................... 26

8.2. Geometry Meshing .................................................................................................... 27

8.3. Defining the Heat Release Rates ............................................................................... 34

8.4. Fuel Definition .......................................................................................................... 35

8.5. Industrial Methylated Spirits (IMS) Substitution ...................................................... 35

8.6. Kerosine Substitution ................................................................................................ 36

8.7. Fuel Properties........................................................................................................... 36

8.8. CFD Physics Solver Selections ................................................................................. 37

8.9. Initial and Boundary Conditions ............................................................................... 45


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9. CFD Simulations Parameter Variations ........................................................................... 48

9.1. Data Extraction .......................................................................................................... 48

10. Model Limitations ............................................................................................................ 51

11. Results .............................................................................................................................. 53

11.1. Temperature Analysis ............................................................................................ 53

11.2. Velocity Analysis .................................................................................................. 54

11.3. Internal Opening .................................................................................................... 55

11.4. External Opening ................................................................................................... 56

12. Parameter Variations ........................................................................................................ 57

12.1. Effect of Radiation Models .................................................................................... 57

12.2. Effect of Temporal Discretization ......................................................................... 57

12.3. Effect of Refining the Mesh around the Fire Region ............................................ 57

12.4. Effect of Base Size on the Polyhedral Mesh Heat Source Models ........................ 58

13. Conclusions ...................................................................................................................... 60

14. References ........................................................................................................................ 61

15. Run 8 (2MW IMS) Charts ................................................................................................ 67

16. Run 9 (2MW Kerosine) Charts ......................................................................................... 81

17. Run 11 (5MW Kerosine) Charts ..................................................................................... 107

18. STAR-CCM+ Materials Database .................................................................................. 121

19. Grid Refinement Index (GCI)......................................................................................... 124


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1. IntroductionAcross the world there has been a definite modern trend towards constructing larger buildings

with wide open internal spaces. There has also been a trend towards increasingly creative,

innovative buildings which can often deviate considerably in their shape from the traditional

compartmentalised buildings of the past. Typical examples include airport terminals, sports

arenas, train stations, open-plan office buildings, hotel reception areas, shopping centres and

night clubs. These buildings often contain a large amount of people. It is therefore imperative

that the safety of people inside the building is considered in the event of fire. In particular it is

important that smoke which often contains toxic combustion products such as carbon

monoxide is managed effectively. In the past, design of buildings with respect to fire safety

engineering has often been dictated by prescriptive fire safety codes. The codes tend to be

targeted at conventional building designs based around closed compartment rectangle forms.

The dictatorial nature of these codes can often place restrictions on innovative modernbuildings which deviate from a prescribed form. There is a definite requirement therefore that

creative building forms be designed under a performance based design process rather than a

prescriptive based process with respect to fire safety engineering. A number of technologies

are available which allow design engineers to produce a performance based fire safety

building design; including physical scale models, zone models and Computational Fluid

Dynamics (CFD) based models.

It is important that the abilities, limitations and typical accuracy of each of the performance

based design approaches be understood in order that confidence is built up in their use. Themost effective approach in verifying the design models is to compare predictions made

through their use against experimental data from multiple fire scenarios under well specified

fire and environmental conditions. While there is a general abundance of data from smaller

single compartment fire scenarios there is less data from larger, well instrumented fire

scenarios to act as a benchmark for the validation of fire models. As a result only a limited

amount of work has been undertaken in very large-scale atrium buildings, because of the lack

of data for validation

A project was undertaken in 1999 led by the Fire Research Station (FRS) in collaboration

with a number of European and UK universities and research establishments with the overall

aim of providing increasing confidence in the use of deterministic fire models for their

application to fire hazard assessment in buildings. The project initiated a large-scale

experimental programme to obtain a detailed data set for the validation of fire models. The

comprehensive data set obtained was intended to be used as a benchmark for model

validation purposes by comparisons of model predictions with the data set. [Marshall, N. et

al. (1999)]

The experiments were carried out at the Large Building Test Facility (LBTF) at Cardington

with the experimental data being made available through the Buildings Research

Establishment Trust (BRE) as part of the Edinburgh Centre in Fire Safety Engineering.


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This thesis will therefore attempt to model three of the experimental scenarios using the

multi-purpose commercial CFD package STAR-CCM+ and compare the predictions made

with the experimental values. The results will also be compared with the previous work of

researchers and masters students using fire specific CFD technology.


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2. Project Aims and ObjectivesThis project aims to extend previous work in order to provide practical guidance to fire safety

engineering practitioners in the use of CFD methods as a performance based design tool. The

work contained within this thesis is essentially a continuation of previous analysis from the

following researchers: [Marshall, N. Kumar, S. Goodall, C. and Smith, D.(1999).][Kumar,

S. Welch, S. and Chitty, R. (1999).] and the following masters students: [MacDonald, F.

(2008).] [Clarke, R. (2008).]

The objectives of this study are as follows:

Identify the assumptions and limitations of CFD fire models. Run CFD simulations for selected fire scenarios and do parameter comparisons where

possible.

Compare the predictions with the test data in order to evaluate the performance of themodels for the different scenarios.

Evaluate the predictive capability for different fire phenomena against the large scaleexperimental data.

Identify factors such as reliability and ease of use of the chosen CFD technology. Compare the predictions of alternative CFD technologies, such as RANS and LES

codes in order to determine their relative merits for this application.

While the thesis was based on previous CFD predictions made using fire specific CFDsoftware no previous attempts have been made within the previous body of work to use a

none fire specific, multi-purpose CFD package to model the problem.

While a large degree of freedom was allowed in the modelling direction and setup of the CFD

simulations a number of limitations were adhered to in order that a reasonable comparison

was able to be made between the codes. These are discussed further in Section 10. As far as

was reasonably possible, consistency between the work carried out for this thesis using

STAR-CCM+ and the previous modelling efforts was implemented. It was not always

possible however to be fully consistent due to technological considerations and differences in

the methods used by the CFD packages to define the simulated models.


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3. Performance Based Fire Safety Methods (Literature Review)A number of technologies are available which allow design engineers to evaluate the fire

safety performance of a building design. The three most prominent methods are physical

scale models, zone models and CFD models.

It is important that the abilities, limitations and typical accuracy of each of the performance

based approaches be understood in order that the tools can be utilised successfully and their

use be applied only in situations where the performance will offer a representative view of

the physical behaviour of an actual fire event.

3.1.Scale ModelsScale models involve creating a real fire in a controlled environment and recording physical

measurements such as temperature, smoke velocity and density. Models can either beconducted at full scale or reduced scale (usually 1/3 scale). It must be taken into

consideration however that smaller scale models may not effectively replicate all the flow

field features of an actual fire. Scale models produce tangible raw data which must be further

analysed for productive use. Real life models are often labour intensive and can be expensive

to undertake, particularly at larger scales. Scale models are therefore best suited towards

small buildings and compartments and may not always be feasible for large complex

buildings.

3.2.Zone ModelsZone models are a deterministic set of equations which use a number of control volumes todescribe how the products of a fire behave over time. Zone models typically use the inverted

bathtub assumption whereby the products of combustion rise to fill the ceiling level of the

computed compartments. The models split the compartment into two zones, an upper hot

products layer and a lower clear air layer. The zones are assumed to be homogenous in

nature. The zones are therefore defined by average values of properties such as temperature,

velocity, gas concentrations and density.

The zone models rely on well established empirical relationships to describe the evolution of

the zones over time. These empirical relationships are often derived from experimental data

and may be valid only under a certain set of conditions or initial assumptions by which the

experiments were conducted. Zone models cannot therefore be completely decoupled from

the experimental setup.

Zone models are fast to compute and can often be performed in common spreadsheet

packages. Zone models require a prior knowledge of the flow field in order to determine the

model compartment structure and the fire source data such as heat release rate (HRR) to

determine the rate of smoke production. Zone models often do not work well in flashover

situations where the fire has become oxygen limited or where the flame penetrates the hot

smoke layer.


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Due to averaging of smoke layer properties the data resolution produced is poor and local

effects such as smoke interaction with ceiling height geometry cannot be accounted for. Zone

models also assume that there is a distinct divide between the hot upper products layer and

the lower clear air. This is not always the case as although there will be a sharp gradient in

temperatures between the two layers there will be a mixing region between the zones. It can

also be difficult to define representative compartments where complex building geometry is

involved.

Zone models may not be suitable for use in large atriums where the fire size is proportionally

small. [Rho J.S. and Ryou H.S.( 1999)] The smoke produced by a small fire may not travel

all the way to the ceiling in a large space as the smoke may entrain a significant quantity of

air as it rises. Air entrainment will lower the smoke temperature and therefore the buoyancy

of the smoke. This will invalidate the basic assumption made by all zone models that the

smoke will remain stratified in a single upper hot layer.

3.3.Computational Fluid DynamicsComputational Fluid Dynamics software codes are a mathematically advanced method of

solving fire flow field problems. They are based on fundamental laws of fluid mechanics and

thermodynamics such as the conservation of physical quantities such as mass, momentum,

energy and species concentrations. The air space within the building geometry is divided into

a large number of discrete cells. The properties of the fluid contained within each cell are

numerically homogenous and are influenced by, but not identical to the numerical properties

of the fluid in surrounding cells.

A CFD program consists of a pre-processor, solver and post-processor. The pre-processor

defines the building geometry, fluid and solid region grids, boundary conditions,

computational solvers as well as the selection of fluid and solid material properties.

The solver uses the pre-processed inputs and executes the selected computational models to

simulate the fire problem and generate a numerical representation of the required physical

parameters.

The post-processor uses the data generated from the solver and presents the solution in the

form specified by the user. Measurements of properties such a temperature, velocity, and gas

concentrations can be taken at discrete points or displayed on a scalar or vector field over the

entire geometry.

The accuracy of a CFD simulation is dependent on factors such as grid resolution and grid

quality, initial, boundary conditions, temporal discretization and to what extent the physics

are correctly represented through the selection of solvers. Sub models can be inserted into the

main flow field model dependant on requirements such as combustion, radiation and

turbulence models. Addition of sub models however can often alter the computational effort

which a model requires to run. Addition of radiation models and certain types of combustion

models can add significantly to the computational power required.


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CFD can look at specific flow field phenomena such as thermal, momentum or chemical

properties at any point within the grid geometry. The spatial resolutions which these can be

modelled are dependent on the resolution of the grid with finer grids resolving more of the

flow field phenomena than coarser grids.

CFD models require little prior knowledge of the flow field as the solver will compute flow

field properties from fundamental physics laws of fluid mechanics and thermodynamics. Also

the smoke layer predicted in CFD simulations will be in heterogeneous in nature which can

flow over surfaces through time representing a solution closer to actual conditions.

CFD simulation however can monetarily cost more than zone modelling as it is preformed

with specialist CFD software requiring significantly greater computational resources. Some

types of fire specific CFD software such as the Fire Dynamics Simulator (FDS) package is

freely available for download from the internet. Other significant costs include the

requirement of greater computational resources on which to run the software than zone

models. As the availability of computing power increases the power and achievable accuracy

of CFD technologies will also increase. CFD modelling also requires a greater deal of

expertise to achieve acceptable outcomes than zone models which can further increase costs.

A number of differing CFD codes are currently available for fire safety design simulations

and they generally fall into one of two categories. Fire specific CFD software is intended only

for modelling fire and smoke movement problems. Examples of fire specific codes include

Fire Dynamics Simulator (FDS), SOFIE and JASMINE. General purpose CFD codes can be

used to model a wide range of fluid dynamics problems in a wide range of industries such as

aerospace, automotive, marine and fire engineering. Examples of general purpose CFD codes

include packages such as CFX, Fluent and STAR-CCM+, the package which the thesis

author used to conduct simulations.


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4. Computational Fluid Dynamics Technology (Literature Review)CFD simulations can differ on the modelling method which the solver uses to model fluid

turbulence. Turbulent movement can be described as the internal swirling of the fluid in the

form of fluid eddies. Fluid eddies can occur on very large scales which are visible to the

naked eye down to very small scales, the smallest of which is the Kolmogorov microscale at

which viscous dissipation of the eddies occurs. Turbulence modelling can therefore be treated

in a number of ways depending on the size of the turbulent eddies which are desired to be

modelled. Modelling of turbulence scales down to the Kolmogorov microscale is called

Direct Numerical Simulation (DNS). DNS requires a very fine grid to resolve the small

turbulent eddies. The fine grid required is hugely computationally demanding and is therefore

completely impractical for the modelling of fire engineering problems. To get around the

computational aspects of DNS, turbulence models are used which time average either all of

the turbulence in the flow field or only the smaller eddies in the flow field while directlysimulating the larger eddies. Time averaging of all eddies is called Reynolds Averaged

Navier Stokes (RANS) while computing the larger eddies and time averaging of the smaller

eddies is called Large Eddy Simulation (LES).

Figure 1 Comparison of Eddies Resolved by CFD Turbulence Models [Class Notes].


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Figure 2 Energy Spectrum of Turbulent Eddy Sizes [Class Notes].

4.1.Reynolds Averaged Navier Stokes (RANS)Reynolds Averaged Navier Stokes (RANS) is a turbulence modelling technique whereby all

eddies are in the flow field are time averaged, the solution is therefore determined using an

average representation of the turbulent eddies. The RANS method achieves this by using the

Navier Stokes equations for mass, momentum and energy which are time averaged before

being solved discretely.

RANS can be performed in either steady state conditions where a solution constant in time is

computed or in an unsteady state where the solution varies over time. The time averaging

must be defined in such a manner that the stochastically fluctuating turbulence is removed

while not affecting the time dependent flow phenomena with time scales distinct from those

of turbulence. (Hirsch, C. 2007).

The relationships between the Reynolds stresses in the fluid due to the random turbulent

fluctuations and the mean flow quantities are unknown. Therefore the RANS method requires

a turbulence model to solve these unknown relationships. In practice there are many

turbulence models which attempt to model the unknown relationship. The model used most

widely in fire safety engineering CFD is the k- turbulence model, where k describes the

turbulent kinetic energy and describes the rate of dissipation of the turbulent kinetic energy.

SOFIE and JASMINE are CFD codes which implement the RANS method. The simulations

conducted by the author in STAR-CCM+ use RANS methods with a k-turbulence solver. It

must be noted that due to the general nature of the STAR-CCM+ CFD package it is also

possible to conduct simulations using the LES method.


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4.2.Large Eddy Simulation (LES)Large Eddy Simulation (LES) is a technique whereby the larger eddies in the flow field are

resolved to the smallest size which the underlying computational grid will allow while

smaller sub-grid eddies are time averaged. LES relies on the logic that the larger eddies are

responsible for the majority of energy transfer and momentum in fluid bodies. As the grid

resolution becomes finer the size of eddies which are modelled become smaller, the

proportion of eddies which are resolved increases while the proportion time averaged by the

sub grid model decreases. A finer grid will therefore be more representative of the actual

fluid motion but will be more computationally demanding than coarser grids. LES is

computationally demanding when compared to the RANS turbulence modelling method but

less so than DNS. LES is gaining in popularity due to the wider availability of powerful

computing resources. LES is inherently a time varying representation of the fluid motion and

cannot therefore be used to perform steady state simulations as the RANS method can. The

LES method can also be difficult to implement effectively in the near wall region of the flow.Hybrid RANS-LES systems such as Detached Eddy Simulation have been developed where

the near wall regions are treated in a RANS like manner. The Fire Dynamics Simulator (FDS)

code is based on the LES method.


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5. Experimental Geometry (Literature Review)The following information has been derived from Evaluation of Fire Models for Fire Hazard

Assessment in Buildings: Part 1 Experimental Programme (Marshall et al. 1999).

The large scale experiments on which the CFD studies in this thesis are based were

conducted at the Large Building Test facility (LBTF) at Cardington. The LBTF rig is a steel-

framed eight floor building designed to resemble a modern office development. The

experimental geometry was partitioned by plasterboard within the main LBTF building and is

comprised of an atrium space spanning two floors with an adjacent single storey space

located on the first floor. The dimensions of the rig are shown in Figure 3. The rig contained

both an internal and external opening. The internal opening was situated between the atrium

and first floor compartment which had otherwise no significant external venting. The external

opening was the single significant vent between the atrium and the external atmospherewithin the LBTF and provided the means from which combustion products escaped the

experiment control volume. A one metre soffit was attached at ceiling level to each opening

and would ensure that a satisfactorily deep smoke layer would be established at the ceiling.

Pictures of the internal and external opening can be seen in Figure 4 and Figure 5

respectively.

The atrium has dimensions of 8.8m wide by 10.8m deep by 8.4m high. The first floor

compartment has dimensions of 13.3m wide by 21m deep by 4m high. The width of the

internal opening between the atrium and first floor was set at 8.78m for the 2MW and 5MW

fire scenarios and was reduced to 3m for the 0.4MW fire scenarios. The combined area of the

atrium and first floor compartment provides a smoke reservoir approximately 373m2. It was

required that the rig geometry have a large enough ceiling area so that the effects of smoke

layer cooling could be examined.

The experimental geometry was partitioned within the LBTF using plasterboard and

plasterboard with fire retardant paper. The ceiling of the atrium and first floor compartment

was formed by a composite floor slab consisting of lightweight concrete bounded by a

trapezoidal steel deck and is supported by 300-mm deep steel beams extending the length and

breadth of the ceiling. The locations of the steel beams and supporting columns are shown inFigure 3


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Figure 3 Diagram illustrating the construction of the experimental geometry. [Marshall, N.

(1999)]


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Figure 4 Picture of the internal opening taken from the first floor looking out into the atrium.

[Marshall, N. (1999)]

Figure 5 Picture of the external opening looking into the atrium space [Marshall, N. (1999)].


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6. Fire Scenarios (Literature Review)A total of 13 experimental runs were conducted. The main parameter variations between the

experiments were fire size, fire location, and fuel type. The parameters of each experimental

run are listed in Table 10 and the locations of the fires scenarios A, B and C are shown in

Figure 3Fire A is located in the atrium corner at ground level while fires B and C are located

on the first floor. In total 9 different fires of varying nominal heat release rates and fuel types

were located in the corner of the atrium at fire point A with the remaining four fires at located

at B and C on the first floor.

The experiment used two different types of fuels for the experiment; Industrial Methylated

Spirits (IMS) to produce a clean burning fire and Kerosine to produce a smoky fire. The soot

content of the smoke will affect the fraction of heat which is lost to heat radiation with

smokier fuels loosing a greater percentage of their heat through radiative losses.

The experimental setup also varied the fire design size between experiment runs with fire

sizes of 0.4MW 2MW and 5MW being used. It was important for this study that the fires

chosen had sufficient size to represent a realistically sized design fire. Heat release rates of

some 2MW to 5MW are typical of fires in retail premises, with lower levels being

appropriate for sprinklered buildings. (Marshall et al. 1999).

The atrium had a roof height of 8.4m. A preliminary zone model calculation prior to

experimental setup using ASKFRS (Chitty and Cox, 1988) indicated that the continuous

flame height of the largest fire size of 5MW would be approximately 2.2m while the

intermittent flame height would be approximately 6m. It was predicted that the clear air layer

height below the smoke layer would be 5.4m. It is undesirable within the experiment to have

the flame penetrate the hot smoke layer because this would invalidate key assumptions made

when using the experimental data to validate zone model calculations, i.e. that the fire is

situated in the clear air zone and that the clear air zone remains distinct from the hot smoke

zone. It was felt by (Marshall et al. 1999) that as the intermittent flame height is only slightly

greater than the clear layer height, the flame penetration into the hot layer would be minimal.

The 2MW and 5 MW fires were restricted to the atrium in order to minimise flame

penetration into the hot gas layer above the fire. The 0.4MW fire size experiments were

conducted in both the atrium and on the first floor.

It can be seen from Table 1that each experimental scenario was conducted a minimum of

two times in order to check the degree of repeatability of the experiments as a consequence of

the changes in the environmental conditions.


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Table1 Experiment Fire Scenarios

Run

Number

Nominal

Fire

Size

(MW)

Location Fuel Internal

Ceiling

Height

(m)

Internal

Opening

Height

(m)

Atrium

Opening

Width

(m)

Atrium

Opening

Height

(m)

External

Opening

Width

(m)

1 0.4 C IMS 4.12 3.12 7.33 7.39 3

2* 0.4 C IMS 4.12 3.12 7.33 7.39 3

3 0.4 B IMS 4.12 3.12 7.33 7.39 3

4* 0.4 B IMS 4.12 3.12 7.33 7.39 3

5 0.4 A IMS 8.39 3.12 7.33 7.39 3

6* 0.4 A IMS 8.39 3.12 7.33 7.39 3

7** 0.4 A IMS 8.39 3.12 7.33 7.39 3

8 2 A IMS 8.39 3.12 7.33 7.39 8.78

8a** 2 A IMS 8.39 3.12 7.33 7.39 8.78

9 2 A Kerosine 8.39 3.12 7.33 7.39 8.78

10* 2 A Kerosine 8.39 3.12 7.33 7.39 8.78

11 5 A Kerosine 8.39 3.12 7.33 7.39 8.78

12* 5 A Kerosine 8.39 3.12 7.33 7.39 8.78

* Repeats

** Artificial smoke used

Zone model calculations were performed to determine the size of the tray required for each

fire. The fire properties and tray sizes are displayed in Table 2The actual heat release rates

of the fires were found in advance of the experiments by measuring the oxygen depletion of

the fire combustion products under a 9m hood size calorimeter at BRE facilities separate

from the experimental setup. It is realised therefore that there will be discrepancies in the fire

sizes as measured by calorimetry and the fire sizes during the experiment itself. Load cells

were mounted under the fire trays and were used to determine the mass loss of fuel over the

duration of the tests. Each fire tray was given sufficient fuel to last for about 30 minutes.

Zone model calculations were used to assess the approximate time of the fire to steady state

conditions.

Table 2 Fire PropertiesNominal

Fire

Size

(MW)

Fuel Area

(m2)

Perimeter

of Fire

(m)

Tray Size (m) Flame

Height

(m)

5 Kerosine 2.4 6.2 1.55x1.55x0.15 5.9

2 Kerosine 1.21 4.4 1.1x1.1x0.15 4.2

2 IMS 2.4 6.2 1.55x1.55x0.15 4.2

0.4 IMS 0.56 3 0.75x0.75x0.15 2.2


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7. Instrumentation Setup (Literature Review)A wide range of measurements were taken during the experimental runs. Data types recorded

include; gas temperature, surface temperature, gas velocity, gas composition, optical density,

heat flux, fuel mass loss, and video and thermal image photography. In total over 400 data

streams were recorded. This thesis will focus on comparison of two types of measurements

between the codes and the experimental values, i.e. gas temperature and the gas velocity.

7.1.Gas TemperatureGas temperature was measured using 18 thermocouple columns situated around the

experimental geometry as shown in Figure 20 Each thermocouple column was composed of

between 5 to 20, K-type 200 micron thermocouples hung vertically from the ceiling to

measure the variation of temperature vertically with height. The height of the experimental

measurements and CFD predictions are displayed on the graphs within this thesis as theheight above the atrium floor. Ten thermocouple columns were located across the first floor

with an additional three in the internal opening, two columns were located in the atrium with

three positioned at the external opening. Data from the thermocouple columns was logged

every 10 seconds.

7.2.Gas VelocityVelocity measurements were taken at both the internal and external openings. The

measurements were taken with McCaffrey low-velocity bi-directional pressure transducers

and Pitot static tubes, the instruments measure a pressure difference which can then be

converted to velocity. The devices were again like the thermocouples arranged vertically intocolumns with data being logged at 10 second intervals.

The internal opening contains 4 columns of bi-directional probes with either 9 or 5 probes on

each. The external opening contained 3 columns with the middle column containing 9 bi-

directional probes and the two external columns containing ten Pitot static tubes. The

locations for the velocity columns are shown in Figure 21.


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8. CFD Fire Modelling SetupThe work contained within this thesis is essentially a continuation of the previous work from

the following researchers and masters students. [Marshall, N. Kumar, S. Goodall, C. and

Smith, D. (1999).] [Kumar, S. Welch, S. and Chitty, R. (1999).] [MacDonald, F. (2008).]

[Clarke, R. (2008).]

It was decide to focus modelling efforts on only some of the experimental scenarios. The

experimental runs selected for which simulations would be created and on which

comparisons would be made were as follows:

Run 8, (2MW IMS, Fire position A). Run 9, (2MW Kerosine, Fire position A). Run 11, (5MW Kerosine, Fire position A).

The fire scenarios simulations conducted for this thesis by the author all used STAR-CCM+

version 4.02.007 a commercial non fire specific CFD package running on Scientific Linux

(64 bit) as an operating system. Computing resources were supplied by the University of

Edinburgh, School of Engineering. The simulations utilised extensively the parallel

processing capabilities of STAR-CCM+ in order to bring the simulation time within a

reasonable duration. Simulation of the first 900 seconds of the experiments after the fires had

been lit using two processor cores were typically between 12 hours for coarse mesh

simulations with no radiation solvers, to 4 days for fine mesh simulations with radiation

solvers activated. All simulations used the RANS method of modelling turbulence with a k-turbulence solver.

Comparisons between simulation setups were made by extracting the predicted temperature

and gas velocity data from the simulations at a time of 900 seconds and comparing with

experimental data and predictions made by previous researchers and masters students using

the CFD packages JASMINE, SOFIE and FDS on graphs produced using Microsoft Excel. A

time period of 900 seconds (15 minutes) was chosen previously between Dr Stephen Welch

and previous masters students on the assumption that at 900 seconds the predictions will

have reached steady state conditions. The data supplied for the JASMINE, SOFIE and FDS

predictions was therefore at 900 seconds after ignition.

Two distinct methods were used to model the problem with a number of variations of each

being simulated. The first method which was modelled was a Heat Source (HS) method

whereby a predetermined quantity of heat equivalent to that which is produced by the fire is

released within the simulation. The second method simulated was the Eddy Break Up (EBU)

combustion method whereby a quantity of fuel was injected into the simulation domain and a

basic EBU fire model solved following a stoichiometric combustion equation of the fuel with

oxygen in the air to produce the products of combustion carbon dixoide, water vapour and

heat.


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8.1.Geometry PreparationThe initial modelling step undertaken prior to beginning simulations was to model the

geometry in which the experiments took place. The CAD package used to model the

geometry was Solid Edge Version 20. The dimensions of the experimental facility were

extracted from Figure 3. It must be noted that the geometry modelled was that of the air

contained within the experiment geometry and not that of the containing walls and roof. The

modelled geometry can be seen in Figure 6and is essentially composed of three adjacent

volumes, i.e. the atrium, first floor and a portion of the external air. An external air volume

was modelled to ensure that the air flowed through the external atrium opening in a realistic

manner. This required that the air volume be sufficiently large that hot products could exit

through the air volume and ambient air be drawn into the atrium through the air volume

without undue interaction or recirculation between the gases.

Figure 6 The Modelled Geometry.

The modelled geometry differed between the simulation types and the experiment run

numbers in the treatment of the region around the fire. The experimental runs used different

sizes of fire trays to obtain the correct fire heat release rates (HRR), which are replicated

within the CFD simulations. The treatment of the fire regions between models is seen in

Table 3

The EBU combustion models had a different size of fire tray modelled depending on the

experimental run number, through which the fuel would flow into the simulation domain. The

geometry for the EBU combustion models was exported directly to STAR-CCM+ using the

Parasolid XT file format.


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The heat source models did not have a fire tray modelled. Instead they had a volume in the

shape of a cone with the top cut off as an approximate replication of the fire flame through

which heat would be released into the model. The model geometry was exported to Star

Design a geometric modelling package which accompanies STAR-CCM+ via the Parasolid

XT file format for preparation of the heat source cone. The cones were modelled in Star

Design by first subtracting the shape of the heat source cone from the model geometry and

then remodelling the heat source cone as a separate body. This procedure allowed STAR-

CCM+ to identify the heat source cone as a separate region which allows the separate

treatment of the heat source cone form the rest of the simulated domain. The size of the heat

source cones differed between the experimental run numbers depending on the size of the fire

tray used and the predicted flame height. The top of the cone was cut off in each model for

meshing reasons.

Table 3 Fire Area Dimensions

Heat Source Models

Run Number Fire Area Type Plan Dimensions Cone

Height

Cone Top

Diameter

Run 8 (2MW IMS) Heat Source Cone 1.5m Diameter 4.2m 0.1m Diameter

Run 9 (2MW Kerosine) Heat Source Cone 1.1m Diameter 4.2m 0.1m Diameter

Run 11 (5MW Kerosine) Heat Source Cone 1.5m Diameter 5.9m 0.1m Diameter

EBU Combustion Models

Run Number Fire Area Type Plan Dimensions Tray

Height

Run 8 (2MW IMS) Fuel Tray 1.5m1.5m Square 0.15m

Run 9 (2MW Kerosine) Fuel Tray 1.1m1.1m Square 0.15m

Run 11 (5MW Kerosine) Fuel Tray 1.5m1.5m Square 0.15m

8.2.Geometry MeshingCFD simulations require a mesh of the air volume geometry on which the calculations are

performed. There are a number of different meshing techniques available within STAR-

CCM+ by which the air volume can be meshed. It was required that the mesh constructed be

similar to the mesh used by the previous CFD simulations of JASMINE, SOFIE and FDS to

allow a fair comparison of the predictive abilities of the CFD codes. The mesh generated for

SOFIE was a structured hexahedral mesh containing approximately 68,400 active cells in the

atrium, first floor and air volume. A SOFIE script file containing the mesh definition for the

2MW IMS case was provided on which comparisons between the STAR-CCM+ generated

meshes could be compared with the SOFIE mesh.

Two different meshing techniques were employed for the heat source models and the EBU

combustion models. The EBU combustion models used a hexahedral mesh composed mainly


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of rectangular elements while the heat source model used an unstructured polyhedral mesh

composed of many faced elements.

The SOFIE mesh was a structured rectangular element mesh built by first defining a

structured mesh larger than the experiment geometry then assigning blockages to the mesh toform the geometry walls roof and ground. The SOFIE mesh has different lengths of mesh

elements along the x, y and z dimensions with each mesh spacing remaining constant on a

plane which is normal to the specified dimension. The SOFIE mesh is shown in Figure 7

Figure 7 The mesh used in the SOFIE CFD simulations. [Welch, S.]

Mesh generation in STAR-CCM+ was completed by a different method whereby the

geometry was first imported into STAR-CCM+ and the mesh developed from this. The

hexahedral mesh for the EBU combustion models was developed by fitting a number of

control volumes around the imported geometry and specifying different mesh dimensionswithin the control volumes. The control volumes used were fitted to the atrium, external air

volume, the front of the first floor to the depth of the atrium and the rear of the first floor. An

additional control volume was used to refine the mesh around the fire tray for run 9 (2MW

Kerosine) refined mesh series of simulations. The control volumes used are shown in Figure

8 and the customised anisotropic mesh sizes defined within each control volume shown in

Table 4 The customised values were defined by taking averages of the corresponding SOFIE

mesh sizes within each control volume for each dimension.

The coordinate system used in the definition of the control volumes has its origin located at

ground level at the left back corner of the first floor compartment as if looking through theexternal atrium opening. The coordinate system used was defined as follows:


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X +ve Across rear of compartment. Y +ve Vertically. Z +ve Out of compartment.

The coordinate system can also be seen in Figure 8

Figure 8 Control volumes used in the generation of the hexahedral mesh.

Table 4 EBU Combustion Model Mesh Control Volume Anisotropic Sizes

X Coordinate Y Coordinate Z Coordinate

Atrium 0.426m 0.286m 0.407m

First Floor Front 0.617m 0.272m 0.407m

First Floor Rear 0.617m 0.272m 0.459m

External Air Volume 1.125m 1.125m 1.125m

Fire Tray* 0.169m As atrium 0.159m

* Used only in the Run 9 refined mesh series of simulations.

The mesh also had 2 prism layers applied around all surfaces to help improve the boundary

layer definition between the smoke layer and the roof including the roof beams.


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The cell counts of the generated mesh were as follows:

67916 Cells without the prism layers. 102041 Cells with the prism layers. 102829 Cells with the refined fire tray region added in the Run 9 refined mesh series

of simulations.

It is noticed that the cell count of the mesh without prism layers is within 0.7% of the 68400

active cells contained within the SOFIE mesh. When the 2 prism layers are added the cell

count jumps dramatically to 102041 cells. This can be accounted for by the large number of

cells it takes to mesh around complex geometry such as the roof beams. Such additional cells

are located adjacent to walls and surfaces and not towards the centre of the volumes. It is

therefore assumed that the prism layer will have negligible effect on the flow field within the

centre of the compartments. The mesh generated is shown in Figure 9which looks into the

atrium towards the internal opening with the atrium right hand wall hidden from view. The

fire tray can be seen in the bottom corner of the atrium. The difference in cell size especially

between the air volume inside the experimental setup and the external volume is also

apparent. Figure 10 Shows the mesh on planes around the Run 9, 1.1m fire tray on which the

prism layers are also apparent. Figure 11Shows the additional refinement in cells around the

fire in the Run 9 refined mesh series of simulations.

Figure 9 EBU Combustion model mesh looking towards the internal opening with atrium wall

hidden from view.


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Figure 10 EBU combustion model mesh of the fire area shown on planes around which the

prism layers are apparent.

Figure 11 EBU combustion model mesh showing the refined fire area in the Run 9 refined mesh

series of simulations.

The heat source models use an unstructured polyhedral mesh were the mesh is composed of

elements containing a different number of faces. These types of meshes are generally used

when a complex shape is required to be meshed as they are computationally more demanding

than structured meshes containing the same number of grid cells. They have been used

however in the case of the heat source models as it was required that a mesh be constructed in

STAR-CCM+ between two regions, i.e. the air region composing of the majority of the

simulation domain and the heat source cone region. It is not currently possible to mesh

between different regions within STAR-CCM+ using a hexahedral mesh as was used for the

EBU combustion models so it was required that the polyhedral mesh be used instead. The

polyhedral mesh was generated with a base size of 0.4m through the internal geometry with

the exception of the external air volume which had a base size of 1.2m applied. The base size

with reference to the polyhedral mesh is the target size of any element within the mesh. Themesh also had two prism layers applied over all surfaces if the geometry.


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The mesh generated is shown in Figure 12which looks into the atrium towards the internal

opening with the atrium right hand wall hidden from view. The heat source cone is the yellow

region which can be seen in the bottom corner of the atrium. Figure 13 shows the mesh on

planes throughout the geometry where the difference in cell size between the air volume

inside the experimental setup and the external volume is apparent.

Figure 14 shows the refinement of the mesh on planes around the heat source cones. The

refinement is done automatically by the polyhedral mesh generator when it meets a complex

or curved surface. The prism layers applied over all surfaces are also apparent in the picture.

Figure 12 Heat source combustion model showing the polyhedral mesh, the yellow region in the

corner of the atrium is the heat source cone region.


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Figure 13 Heat source mesh shown on planes, the larger mesh size in the external air volume is

apparent.

Figure 14 The mesh was automatically refined around the heat source cone.

The cell counts of the generated mesh were as follows:


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106,616 Cells in the air region including prism layers. 2,436 Cells in the heat source cone region. 109,052 Cells in totalled in both regions.

Coarser polyhedral meshes were also generated for the heat source models using base sizes of0.8m and 1.6m with cells in the external air volume having base sixes of 2.4m and 4.8m

respectively. The purpose of generating the coarser meshes was to judge the degree of

convergence between the mesh sizes.

8.3.Defining the Heat Release RatesAs previously mentioned the actual heat release rates of the fires were found in advance of

the experiments by measuring the oxygen depletion of the fire combustion products under a

9m hood size calorimeter at BRE facilities separate from the experimental setup. Each fire

tray was given sufficient fuel to last for about 30 minutes. Conditions were replicated as

closely to the experimental conditions as possible with a corner wall scenario being

constructed around the fire to replicate the conditions at fire position A.

The experimental setup measured the amount of oxygen depleted around the fire and from

this determined the heat released by the fire by assuming stoichiometric combustion.

Approximately 13.3MJ of energy is released per kilogram of oxygen consumed in a

hydrocarbon fire. It is realised however that as the heat release rate of the actual experiments

were not measured there will be discrepancies in the fire sizes as measured by calorimetry

and the fire sizes during the experiment itself.

The heat release rates for the simulations were defined by using a constant plateau heat

release rate until the 900 second time step which the fires reached during their burn time.

Time squared (t2) ramp ups were also defined at the start of the runs to simulate the build up

of the fire and to aid convergence of the simulations. The measured and defined heat release

rates are shown on Figures 22, 23 and 24..

The heat release rate t2ramp ups were defined by the following equation until the heat release

rate plateau values were reached:

Where:

is a constant as defined in Table 5


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5

HRR Plateau

Value

(2 ) 0.5 2.077MW

(2 ) 0.5 2.163MW

11 (5 ) 1 5.6MW

The heat release rate plateau for run 8 and 9 are the same as those defined for the SOFIE

Simulations. The value for run 11 where this information was unavailable was defined from

the graph. The values for heat release over time were used directly in the heat source models.

The EBU combustion models required however that an equivalent fuel mass flow rate be

used to obtain the required heat release rate.

8.4.Fuel DefinitionThe EBU combustion model requires the definition of a combustion equation which the

model will solve at the location of the fuel injection into the simulation domain. The reaction

will occur in cells around the fuel tray where the combustion equation is fulfilled by meeting

the correct mixture fraction of fuel and oxygen. In these cells the EBU solver will

numerically deplete the fuel and oxygen in proportions as defined by the combustion

equation and numerically produce the products of combustion along with heat.

STAR-CCM+ uses a multi component gas solver with the EBU combustion solver and

therefore treats each gas separately, i.e. air is treated as O2 and N2with initial mass fractions

defined as 0.233 and 0.767 respectively. The properties which STAR-CCM+ uses for each ofthe gases present within the simulations are derived from the STAR-CCM+ materials

database. An excerpt from the materials database showing the properties of the gases used is

shown in Section 18.

It was found that the materials database did not include properties of either ethanol (in vapour

form), methanol or kerosene. A number of substitutes with similar molar masses and carbon-

hydrogen ratios were instead used with the mass flow rates of the substitute fuel corrected for

difference in heat of combustion (Hc) between the experiment fuel and the substitute fuel.

8.5.Industrial Methylated Spirits (IMS) Substitution

The composition of the Industrial Methylated Spirits (IMS) used in the experiments was 96%

ethanol and 4% methanol. It was assumed for simplicity that the chemical composition of the

fuel was 100% ethanol.

The stoichiometric combustion equation for ethanol (C2H5OH) is as follows:

C2H5OH + 3O2= 2CO2+ 3H2O +Hc

The properties of ethanol are not included in the materials database, therefore a substitute fuel

was derived. It was decided to define a reaction from component molecules which when

summed contained the same number of moles of carbon, hydrogen and oxygen as ethanol.

The reaction would be treated in the same manner by the STAR-CCM+ EBU combustion


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solver as the stoichiometric ethanol combustion equation. It was decided to use the methyl

group (CH3) and methoxy group (CH3O) molecules which would produce the following

substitute combustion equation:

CH3 + CH3O + 3O2= 2CO2+ 3H2O +Hc

8.6.Kerosine SubstitutionKerosine is a fuel which is formed from the fractional distillation of petroleum. As a result it

is a mixture of hydrocarbons with no specific chemical formula. It is often approximated by

the chemical equation C12H23 or C12H26., the properties of kerosene are not listed in the

STAR-CCM+ materials database. The reaction has therefore been defined using dodecane

(C12H26) as a substitute fuel. Dodecane has approximately the same molecular mass and

carbon-hydrogen ratio as the assumed chemical composition of kerosine. The heat of

combustion for the two fuels is also similar as shown in Table 6.

The stoichiometric combustion equation for dodecane is as follows:

C12H26+ 18.5O2 = 12CO2+ 13H2O +Hc

8.7.Fuel PropertiesThe mass flow rate of each substitute fuel is defined using the following equation:

Where:

mfis the mass flow rate of fuel per second. Hcis the heat of combustion of the substitute fuel.

The heat of combustion was determined for each substitute fuel from the following equation:

( ( (

Where:

Hfis the heat of formation of the specified substance. n is the number of molecules of the specified substance.

The properties of the combustion equation reactants and products are found in Table 6.The

properties were derived where possible from the STAR-CCM+ materials database.


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6

Molecular

Weight

[g/mol]

Mass

Fraction

Fuel

Hf

[J/ mol]

Hf

[J/ kg]

Hc

[J/ mol Fuel]

Hc

[J/ kg Fuel]

(25)

46.07 1 -234,848 -5,105,390 1,278,679 27,797,371

3 15.04 0.32667 0 0 756,764 50,450,900

3 31.00 0.67333 0 0 756,764 24,411,726

3 + 3 46.04 1 0 0 1,513,527 32,902,761

N/A 1 N/A N/A N/A 43,434,000

(1226)

170.33 1 -288,823 -1,698,960 7,582,354 44,602,081

/

(2) 32.00 N/A 0 0 N/A N/A

(2)

44.01 N/A -393768 -8,941,600 N/A N/A

(2) 18.02 N/A -241997 13,423,800 N/A N/A

The fuel mass flow rates determined for use in the simulations are shown in Table 7. It

should be noted that the mass flow rates stated are the flow rates which correspond to the heat

release rate plateau values. In the EBU combustion simulations the fuel mass flow rateincorporated the t

2 ramp in the heat release rates at the start of the simulations by defining

the flow rate which corresponded to the t2

ramp ups illustrated on Figures 22, 23 and 24

Table 7 Heat Release Rate Values

Run Number HRR Plateau

Value

Mass Flow Rate

Fuel

Run 8 (2MW IMS) 2.077MW 0.06313kg/s

Run 9 (2MW Kerosine) 2.163MW 0.0474kg/s

Run 11 (5MW Kerosine) 5.6MW 0.1232kg/s

The setups of the simulations are explained in the following sections.

8.8.CFD Physics Solver SelectionsSTAR-CCM+ contains a large range of computational solvers by which to define the setup of

the simulations in order to replicate the physical behaviour of the experiments. The solvers

are in modular form and are activated by the user depending on the type of simulations which

are to be performed and the level to which real world physics are to be modelled. The physics

modelling realism level often has to be traded against the added computational expense

which the additional extra solvers will add.


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The following physics models were used within either the EBU combustion models or heat

source simulations or both. A reference has been included with each solver indicating with

which models it has been used.

Three DimensionalUsed by:The EBU combustion models and the heat source models.

The three dimensional solver defines that the fluid geometry to be modelled is in three

dimensions and selects the three dimensional version of all subsequent solver selections.

STAR-CCM+ can also model two dimensional problems.

StationaryUsed by:The EBU combustion models and the heat source models.

The stationary physics model defines that there are no moving parts within the simulation

such as moving meshes and sliding reference planes. The stationary solver also does not

allow solid body motion due to fluid-body interaction.

Multi Component GasUsed by:The EBU combustion models.

The multi component gas model allows the simulation of a mixture of two or more

different gases in the same phase. In the modelled simulations it allowed the modelling of

combustion of gaseous fuel and oxidizer to generate product gases. The air in the EBU

combustion simulations is defined using the properties in the STAR-CCM+ materials

database for O2 and N2 with the with mass concentration fraction of 0.233 and 0.767

respectively.

GasUsed by:The heat source models.

The gas model allows the simulation of a single gas only. The air in the heat source models is

defined by overall properties of air as listed in the STAR-CCM+ materials database.

Ideal GasUsed by:The EBU combustion models and the heat source models.

The ideal gas model uses the ideal gas equation to express density as a function oftemperature and pressure. The ideal gas model can also allow or remove the dependency on


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pressure. In all simulations completed for the thesis the dependency on pressure was included

within the models.

GravityUsed by:The EBU combustion models and the heat source models.

The Gravity model permits the inclusion of the buoyancy source terms in the momentum

equations when using the segregated flow model. This is important in fire simulations as it

allows the hot products of combustion to rise to ceiling level. When the ideal gas model is

used to define a variable density problem, the buoyancy source term is included in the

momentum equation without modelling. The gravity was set at 9.81m/s2.

Implicit UnsteadyUsed by:The EBU combustion models and the heat source models.

The implicit unsteady model allows the advancement of the simulation through time. In each

time step a number of inner iterations are completed to converge the solution for that step. A

time step of 1 second was used with 20 inner iterations throughout all simulations. The

implicit unsteady solver allows the selection of either a 1st order or 2nd order temporal

discretization options. A number of parameter variation simulations were conducted for the

thesis by changing the temporal discretization between 1st order and 2nd order.

Segregated FlowUsed by:The EBU combustion models and the heat source models.

The Segregated Flow model solves the velocity and pressure components of the flow

equations separately in a segregated, or uncoupled, manner. The linkage between the

momentum and continuity equations is achieved with a predictor-corrector approach.

The segregated solver uses two under-relaxation factors to control the solution update of the

velocity and pressure models. The under-relaxation factors govern the extent to which the old

solution is replaced by the newly computed solution at each iteration. In STAR-CCM+ a high

under-relaxation factor advances the solution faster than a low under-relaxation factor.

Higher factors can make the solution faster to compute but can often cause convergence

problems within the simulations.

Under-relaxation factors of 0.7 and 0.3 were used for the velocity and pressure factors

respectively for the EBU combustion model solvers, while under-relaxation factors of 0.7 and

0.5 were used for the velocity and pressure factors respectively for the heat source model

solvers. These values were used for the majority of simulations, however in a small numberof simulations a STAR-CCM+ error message was encountered midway through the


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simulation indication a floating point error exception which would be replicated when the

simulation was re-ran. The error was fixed by rolling the simulation back to the last good

save point and reducing the pressure relaxation factor by 0.1.

Segregated Fluid EnthalpyUsed by:The EBU combustion models.

The segregated fluid enthalpy model is a form of fluid energy model which solves the total

energy equation with chemical thermal enthalpy as the independent variable. Temperature is

then computed from enthalpy according to the equation of state. The STAR-CCM+ user

manual recommends the use of this model for any simulation involving combustion.

The segregated fluid enthalpy model in the case of multi-component gas simulations uses two

under-relaxation factors to control the solution update for the energy and species models as

was used in the EBU combustion models. The under-relaxation factors govern the extent to

which the old solution is replaced by the newly computed solution at each iteration. As

previously mentioned a high under-relaxation factor advances the solution faster than a low

under-relaxation factor making it faster to compute but again raising the possibility of

convergence problems within the simulations.

An under-relaxation factor of 0.7 was used with the EBU combustion models for both the

segregated energy and species solvers. The STAR-CCM+ user manual states that it is

important that the under-relaxation factors for the species and energy are kept the same inorder to ensure the two solutions remain synchronised.

Segregated Fluid TemperatureUsed by:The heat source models.

The segregated fluid temperature model is a form of fluid energy model which solves the

total energy equation with temperature as the independent variable. Enthalpy is then

computed from temperature according to the equation of state. This model is appropriate for

simulations that do not involve combustion.

The segregated fluid temperature model uses a single under-relaxation factor to control the

solution update for the energy model. The heat source models do not use a multi-component

gas solver so therefore do not require the use of a segregated species solver. An under-

relaxation factor of 0.9 was used in the heat source models for the energy solver.


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Segregated SpeciesUsed by:The EBU combustion models.

The segregated species model solves the species continuity equations for a multi-componentfluid mixture. The equations provide a means of updating the flow field mass fractions which

define the mixture composition.

TurbulentUsed by:The EBU combustion models and the heat source models.

The turbulent solver defines that the flow field is non-laminar and sets up the simulation

accordingly allowing further selection of turbulence modelling methods.

Reynolds Averaged Navier Stokes (RANS)Used by:The EBU combustion models and the heat source models.

RANS is the turbulence modelling method which was used within the simulations. The

RANS technique has been discussed previously.

K-TurbulenceUsed by:The EBU combustion models and the heat source models.

The k-turbulence model is used in RANS codes to solve the unknown relationships between

the Reynolds stresses due to random turbulent fluctuations and the mean flow field quantities.

The k-turbulence model has been discussed previously.

Standard k-Low ReUsed by:The EBU combustion models and the heat source models.

The standard k-low Reynolds number has identical coefficients to the standard k-model

but provides additional damping functions which allow it to be applied in the viscous affected

region near walls. The model is recommended by the STAR-CCM+ user manual for use in

natural convection problems such as convection driven by a combustion model or heat

source. Boundaries within the simulation geometry had a no-slip shear stress specification

applied if they were located within the internal experimental geometry such as the atrium and

first floor roof, ground and walls. The boundaries of the external air volume used for

modelling the air external to the experimental geometry however had a slip shear stress

specification applied as no viscous sub-layer will exist in actuality at these locations.


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All y+ Wall TreatmentUsed by:The EBU combustion models and the heat source models.

The all y+ wall treatment is a model within STAR-CCM+ which applies a set of near wallmodelling assumptions for use with each turbulence model. The wall treatments have been

specialized according to each turbulence model, since assumptions specific to that model

need to be made for the wall boundary conditions concerning the turbulence quantities.

The STAR-CCM+ users manual defines the all y+ wall treatment as a hybrid treatment that

attempts to emulate the high y+ wall treatment for coarse meshes and the low y+ wall

treatment for fine meshes. The high y+ wall treatment implies an empirical wall-function

approach while the low y+ wall treatment is suitable only for low-Reynolds number

turbulence models in which the viscous sub-layer is properly resolved.

ReactingUsed by:The EBU combustion models.

The reacting model allows the components of the multi component gaseous mixture to react

chemically with one another.

Non Pre-Mixed CombustionUsed by:The EBU combustion models.

The non pre-mixed combustion solver defines that a non pre-mixed diffusion flame is to be

modelled as was the case in the experiments rather than a pre-mixed flame.

The EBU Combustion ModelUsed by:The EBU combustion models.

The EBU combustion model tracks individual mean species concentrations on the grid usingtransport equations. Mixing of the fuel and oxidizer is a function of the turbulent mixing time

scale. The reaction rate used by the EBU solver within a grid cell is defined by the mean

species concentrations within that grid cell.

Passive ScalarUsed by:The EBU combustion models and the heat source models.

The passive scalar solver allows the addition of a scalar species to the flow field. The passivescalar is transported around the fluid volume with the flow field but does not interfere with


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flow field velocity or pressure terms. The passive scalar is a dimensionless quantity used

within the simulations to model soot which is injected into the simulation domain through the

fire tray or heat source cone. The passive scalar concentration is used by the radiation solvers.

The quantity of passive scalar released at a given time step is defined using the following

equation:

(Where:

is the dimensionless passive scalar quantity released into the model at a given timestep.

HRR is the defined heat release rates of the simulations at a given time step. Hc (exp) is the heat of combustion of the fuel used within the experiments i.e. IMS and

Kerosine and not the substitute fuels.

Soot Yield is the mass fraction of soot produced per mass unit of fuel burnt. is the density of the fuel in a grid cell, it is determined within STAR-CCM+.

The quantities used within the passive scalar equation are shown in Table 8 It must be noted

that the values used are those of the fuels used within the experiments and not the substitute

fuels.

Soot Yield

[kg/kg]

Hc (exp)

[J/kg FUEL]

0.02 26,502,000

0.1 43,434,000

RadiationUsed by:The EBU combustion models and the heat source models (where specified).

This model enables the STAR-CCM+ radiation modelling capabilities. It allows the further

selection of a radiative transfer model and a radiation spectrum model.

Participating Media Radiation (DOM)Used by:The EBU combustion models and the heat source models (where specified).

The participating media radiation model is a radiative transfer model which defines the

overall solution method for the governing radiative transfer equation. The DOM model

allows the consideration of participating media within the flow field. This radiation model

interacts with the passive scalar previously discussed which represents soot in the air that can

absorb, emit, and scatter thermal radiation.


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Masters o

The DOM model uses an

absorption function defines th

passes through a species or i

function was defined by STA

For temperatures of less than

For temperatures of between

For Temperatures greater tha

Where:

is the absorption coe T is the temperature o

The function has been plotted

Figure 15 Variation of ab

Mechanical Engineering with Management

April 22, 2010

absorption coefficient which varies with

e amount of absorption of thermal radiation

the case of the simulations the passive sca

-CCM+ in the following manner:

23K:

23K and 700K:

700K:

fficient.

species in a grid cell.

and is shown in Figure 15.

sorption coefficient with temperature as defin

temperature. The

per unit length as it

lar. The absorption

d by function.


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Gray Thermal RadiationUsed by:The EBU combustion models and the heat source models (where specified).

The gray thermal radiation model is a radiative spectrum model which defines how theradiation wavelength spectrum is considered within the context of the radiative transfer

model. The Gray Thermal radiation model simulates the diffuse radiation independently of

wavelength. The radiation properties of the media and the surrounding surfaces are therefore

considered the same for all wavelengths.

8.9.Initial and Boundary ConditionsThe simulations required a number of initial and boundary conditions to complete the setup

of the model which are listed and discussed as follows.

The initial ambient air temperature was set at 279.15K or 6C. This temperature was selectedto replicate the ambient air temperature on the days when the experimental runs were

conducted. The pressure outlet boundary temperature was also set at 279.15K. This means

that any air entering the simulation domain through the pressure boundary would have a

temperature of 279.15K. The atmospheric pressure defined in the simulations was

101.325kPa.

The air

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