FURTHER VALIDATION OF FIRE DYNAMICS SIMULATOR …...International Journal on Engineering...

International Journal on Engineering Performance-Based Fire Codes, Volume 9, Number 1, p.7-30, 2007

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FURTHER VALIDATION OF FIRE DYNAMICS SIMULATOR USING SMOKE MANAGEMENT STUDIES P. Coyle and V. Novozhilov The Institute for Fire Safety Engineering Research and Technology, Faculty of Engineering University of Ulster, United Kingdom (Received 7 July 2006; Accepted 13 December 2006) ABSTRACT Further validation of Fire Dynamics Simulator (FDS) developed by NIST (USA) is performed using four smoke filling scenarios reported in the literature. Careful comparison is made to experimental data available for those scenarios. Performance of the code was found to vary considerably with complexity of scenario (e.g. geometry). While giving very reasonable results for a number of cases, average deviation from experimental values in smoke-filling rates and temperature predictions were above limits claimed by developers. The study emphasizes need for further development and extensive validation of CFD codes used by fire engineering practitioners. 1. INTRODUCTION Fire Dynamics Simulator is an increasingly popular CFD model choice for fire engineers and academia researchers [e.g. 1,2]. The code has been developed at the National Institute of Standards and Technology, USA. The underlying motivation for the present study is assessment of accuracy of currently available computer models for the purpose of their integration into Engineering Performance-Based Fire Codes (EPBFC). The importance of ongoing validation and standardization work in fire-related CFD is well recognised [3]. The most interesting property of the FDS code is that it uses Large Eddy Simulation (LES) approach, as opposed to many other (primarily commercial) codes used by fire safety consultants. The latter use Reynolds-averaged (RANS) governing equations. Use of LES is still a hot topic in CFD community, and many important fundamental issues related to this approach are not completely resolved. As examples, one could point out to importance of different filtering procedures, influence of mesh refinement on solution, etc. Since this innovative technology is penetrating into practical design, it is important that practitioners in the area are kept informed of possible limitations and shortcomings of the methods. It should be kept in mind that many of those using modern CFD software may not have sufficient background in CFD or combustion fundamentals, therefore they need be warned of potential misuse of computational tools.

The objective of the present study is to validate the FDS code further using a number of well-documented scenarios for comparison. Fire Dynamics Simulator (FDS) Version 4.0.5 [4] is used for the study. 2. RESULTS AND DISCUSSION This section is segmented into four sub-sections, namely scenarios A, B, C and D, which correspond respectively to each selected physical experiment under investigation. For comparison with the experiments, some technical features of FDS 4.0.5 should be noted. The first one refers to smoke layer thickness calculation in FDS. Relatively simple zone models compute this quantity directly, along with the average temperature of the upper and lower layers. In a computational fluid dynamics (CFD) model like FDS, there are generally no distinct zones, but rather a continuous profile of temperature. Nevertheless, the methods can be developed to estimate layer height and average temperatures from a continuous vertical profile of temperature. The method employed by FDS is as follows. Consider a continuous function T(z) defining temperature T as a function of height above the floor z, where z = 0 is the floor and z = H is the ceiling. Define Tu as the upper layer temperature, Tl as the lower layer temperature, and zint as the

International Journal on Engineering Performance-Based Fire Codes

8

interface height. Conservation of energy dictates that and conservation of mass (assuming an ideal gas) dictates that Solving for zint gives: Letting Tl be the temperature in the lowest grid cell and using Simpson’s Rule to perform the numerical integration of I1 and I2, Tu can be defined as the average upper layer temperature via Another note is on boundary conditions in FDS, relevant for the cases considered in the present paper. These are imposed in the following manner [4]. By default, a partial-slip condition is employed at solid walls. This means that the velocity at the wall is a fraction of its value in the grid cell adjacent to the wall. This is a very crude analogue of a wall function, which is required if boundary layer cannot be resolved. For the present study, the details of boundary layers are not important, so that the above solid boundary condition is deemed to be satisfactory. Fire with the fixed HRR (Heat Release Rate) is also treated as a boundary condition, effectively modelling combustion as injection of pyrolysed fuel through the solid surface with subsequent burning according to fast chemistry combustion model [4]. At external boundaries (vents) a pressure-like condition is prescribed either for natural ventilation, or a forced flow extraction [4]. In the latter case, the volumetric extraction rate is prescribed. 2.1 Scenario A − Smoke Filling in a Room

Environment In this first scenario the smoke-filling rate was analysed within a single room environment. In this

strategy the smoke layer was just let descend to the floor without any additional smoke venting means. This is considered as a starting point for the investigation, since if the smoke filling rate cannot be accurately predicted here, then there would be no justification for analysing more complex strategies. Hagglund et al. [5] conducted several experiments in a room having a floor area of 5.62 m x 5.62 m with a height of 6.15 m (Fig. 1). The room was closed except for a 0.25 m high by 0.35 m wide leakage opening near the floor. The fire source was placed 0.2 m above the floor and incorporated a kerosene pan of 0.5 x 0.5 m, with a total steady state heat release rate of 186 kW. The experimental boundaries, i.e. the walls, floor and the ceiling of the experiment were made up of concrete. Karlsson and Quintiree [6] used this experiment to validate a simple mathematical expression, developed by Zukoski and known as the Zukoski Smoke Filling Model. The results of this model show an overestimation of the smoke-filling rate. This was due to the delay time in the experiment for the HRR to reach steady state, in addition to the time taken for the plume to reach the ceiling. Another important factor was the ignorance of heat losses to the boundaries of the experiment. FDS should not have these aforementioned shortcomings as this model tracks flows and heat losses by conduction, convection, and radiation to all surrounding boundaries over time. The input/output parameters for Scenario A are summarised in Table 1. Fig. 2 depicts the averaged layer height predicted by FDS for nine various runs, which were subject to change in order to highlight sensitive input parameters. Overall, good agreement was observed between the FDS V 4.0.5 predictions and the physical experiment conducted by Hagglund. It can be seen from Fig. 2 that the smoke took approximately 4 s to reach the ceiling before starting its rapid descent. This is demonstrated by the straight line at 6.15 m height at the top left of the chart. For the sensitivity analysis of this scenario, a smaller HRR of 168 kW was intentionally chosen during selected runs, primarily to investigate how sensitive FDS was to this HRR fluctuation.


9

Fig. 1: Plan of experimental set-up for Scenario A

Fig. 2: Smoke layer time history plot. Scenario A

2

AREA 31.4 M

CONRETE BOUINDARY WALL

INLET VENT

KEROSENE FIRE SOURCE186 (kW)

SMOKE FILLING IN ROOM ENVIRONMENT

0.000.501.001.502.002.503.003.504.004.505.005.506.006.50

0.00 25.00 50.00 75.00 100.00 125.00 150.00 175.00 200.00 225.00 250.00 275.00

TIME (s)

HEI

GH

T (m

)

FDS LAYER HEIGHT RUN 1 (200X200X205)(168kW) EXPERIMENT BY HAGGLUND et al. (1985)FDS LAYER HEIGHT RUN 2 (140X140X140)(168kW) FDS LAYER HEIGHT RUN 4 (140X140X140)(186kW)FDS LAYER HEIGHT RUN 5 (100X100X100)(168kW) FDS LAYER HEIGHT RUN 6 (100X100X100)(186kW)FDS RUN 7 LAYER HEIGHT (100X100X100)(186)(Soot Yield 2%) FDS RUN 8 LAYER HEIGHT (50X50X50)(186kW)FDS RUN 3 (140x140x140) DT 0.005 FDS LAYER HEIGHT RUN 3A (100X100X100)(DT 0.01)

FIREINLET

GRID

ROOM BOUNDARY

HEIGHT DEVIATION 16%

TIME 55s, HEIGHT

LAYER HEIGHT DEVIATION

FIRE

LAYER HEIGHT DEVIATION


10

Con

verg

e sp

eed

[s-1

]

1.64

0.39

0.6

0.25

0.39

0.24

0.14

0.14

0.15

Fire

res

olve

. (D

x ) / (∂

x)

1.45

2.04

2.18

3.05

2.18

2.85

3.05

3.05

6.12

Out

put

Q* [-

] (0

.1<Q

*<2.

5)

0.65

0.65

0.72

0.72

0.72

0.65

0.72

0.72

0.72

Sim

ulat

ion

time

(s)

250

250

150

150

250

250

250

250

200

Smag

orin

sky

cons

tant

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

Soot

yie

ld

(%)

0.04

2

0.04

2

0.04

2

0.04

2

0.04

2

0.04

2

0.04

2

0.02

0.04

2

Initi

al

time

step

.

0.12

8

0.09

0.00

5

0.01

0.09

0.06

4

0.06

4

0.06

4

0.03

2

HR

RPU

A

(kW

/m2 )

672

672

744

744

744

672

744

744

744

Fire

ou

tput

(k

W)

168

168

186

186

186

168

186

186

186

Fire

are

a (m

2 )

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0.25

Fire

sour

ce

Ker

osen

e C

_14

H_3

0,

MW

_FU

EL=1

98.0

N

U_O

2=21

.5

NU

_CO

2=14

.0

NU

_H2O

=15.

0 EP

UM

O2=

1270

0.

CO

_YIE

LD=0

.012

SOO

T_Y

IELD

=0.0

42

SFPE

Han

dboo

k

Smok

e fil

ling

in r

oom

env

iron

men

t

Tot

al c

ells

2352

0

7200

0

7200

0

1749

60

7200

0

1749

60

1749

60

1749

60

1458

000

Inpu

t

Gri

d ce

ll si

ze

(XY

Z) (

mm

)

200x

200x

200

140x

140x

140

140X

140X

140

100x

100x

100

140x

140x

140

100x

100x

100

100x

100x

100

100x

100x

100

50x5

0x50

Scen

ario

A

Run

No.

Run

1

Run

2

Run

3

Run

3A

Run

4

Run

5

Run

6

Run

7

Run

8

Tab

le 1

: Sum

mar

y of

inpu

t / o

utpu

t par

amet

ers.

Scen

ario

A


11

For the run (1), i.e. the first simulation of this scenario, it can be clearly seen that after approximately eight seconds FDS began to dramatically over predict the smoke filling rate within the enclosure. This behaviour was not expected while applying a lower HRR than that in the experiment. Twelve seconds later FDS predicted a layer interface height of 2.75 m, while the physical experiment gave a height of 4.6 m, a difference of 1.85 m. Thereafter, the models smoke filling predictions began to slow down, and started gradually to move closer to the experimental result. In the physical experiment this slowing up of the layer interface was attributed to the fact that the rate of air entrainment from the lower layer decreases as the smoke layer descends towards the floor. For the run (2), the only change was in the grid cell size from 0.2 m to 0.14 m. The smoke filling rate reflected this change remarkably, indicating that grid size is a sensitive input parameter. Run (3) was the final simulation (rerun), and will be discussed later. Run (4) incorporated the correct HRR of 186 kW and a grid cell size of 0.14 m. This run gave the same trend as the run (2), but the filling rate was a little higher initially due to the higher HRR. Run (5) reverted back to a HRR of 168 kW, but decreased the grid cell size further, from 0.14 m to 0.10 m. This was carried out in a bid to improve the filling rate from the ceiling to a height of 2.5 m. However, this run gave a closer prediction from the ceiling to approximately 3.0 m, but resulted in a larger error thereafter. Run (6) went back to the correct HRR of 186 kW and applied the same grid cell size of 0.1 m. The result gave a slight improvement, but still held an overall height deviation of 16% from 5.50 m to 2.75 m. Run (7) confirmed that soot yield did not play any role in layer height prediction, when it was halved from 4% to 2%. It confirms that FDS uses average temperatures from a continuous vertical temperature profile to predict the layer height. Run (8), incorporated a grid cell size of 0.05 m. It took a considerable amount of computational time, approximately 72 hours. It was a disappointing result, as it goes against the models main underlining assumption, which is, “the accuracy of the results is a function of the fidelity of the numerical solution, which is mainly dependent on the size of the computational grid”, as stated in the FDS technical reference guide [4]. This run from the chart shows greater over predictions in the layer height from 4.52 m to 2.0 m, compared to all other runs with the exclusion of the run (1). Thereafter it begins to follow the trend of the physical experiment slightly better than the runs (5), (6) and

(7). This run also behaved slightly better than all other runs in predicting long-term (asymptotic) behavior of the layer. Other runs tended not to go below 0.5 m in all simulations. Run (3) was the penultimate run of scenario A. This run reverted back to a 0.14 m grid cell size, similar to run (4) as it showed good promise in predicting the physical experiment. The time step was reduced to 0.005 s in this simulation in a bid to highlight its effect compared to the grid size effect. The reduced time step of 0.005 produced the trend similar to the run (4). The final simulation within this scenario, the run (3A) simulated a 0.1 m grid cell size and increased the time step to 0.01 s. It can be seen from the layer height chart, Fig. 2, that the run (3A) was comparable to the runs (6) and (7). This indicates little effect of changing time in this scenario. In summary, the results demonstrated that the model does not necessarily give better predictions with decrease in the grid cell size. This can be seen upon comparison of the Run (4) (grid cell size of 140 mm) with the runs (6) and (7) (finer grid cell sizes of 100 mm). The latter two runs do not really give any better predictions in comparison with the run (4). For the majority of the runs, FDS V 4.0.5 has given smoke filling rate predictions within 20% of the experimental curve. This is in line with the claim of its developers. Temperature comparisons were not attempted in the scenario A. 2.2 Scenario B − Smoke Filling in a Hanger

Environment In this scenario the smoke filling rate, the effects of natural vents and the performance of mechanical extraction were analysed. Having evidence from the first scenario that FDS can predict smoke filling in a single room enclosure quite well, our initial task in this scenario is to see how well the model can predict smoke filling when applied to a larger enclosure. Then FDS is given a task of simulating natural smoke venting, followed by mechanical extraction at various volume fluxes [m3s-1] within the same enclosure. Yamana and Tanaka [7] performed a series of full-scale experiments using the BRI fire test facility to investigate the smoke filling behaviours in large-scale spaces under various smoke control conditions. These experiment (Part 2 of the their study) also served as the validation exercise for the simple analytical theories that presented in Part 1 [8]. As a result, the predictive capabilities of the


12

simple theories were proved to be fairly good. In this section, this standard filling equation, developed by Yamana and Tanaka, is indicated by the black doted lines on all layer height charts (Figs. 6-7, 9-10) for comparative purpose. The experiment facility was the BRI full-scale fire test laboratory, of the Ministry of Construction, Japan, which was used for this series of

experiments; a schematic of the facility is given in Fig. 3. The facility consisted of a large space; floor area equating to 720 m2, with maximum dimensions of 30 m by 24 m. The ceiling height was 26.3 m. The experimental conditions selected for this study were (1) Natural Filling, (2) Natural Venting and (3) Mechanical Venting. Table 2 provides some finer details that pertained to each experiment.

Smoke filling set-up Natural smoke venting set-up Mechanical extraction set-up

Tree 2

Fig. 3: Plan of experimental set-up for Scenario B

Tree 1


13

Table 2: Experimental conditions for Scenario B

Filling rate Natural venting

Mechanical extraction 6.0

m3.s-1


m3.s-1


m3.s-1 Initial temp °C 14.0 18.0 13.0 12.0 13.0 Lower opening

time [s] ---- Prior to test Prior to test ---- ----

Lower opening area [m2] ---- 3.23 3.23 ---- ----

Upper Opening Time [s] ---- Prior to test ---- ---- ----

Upper opening [m2] ---- 6.46 ---- ---- ----

Mechanical venting

activation time [s]

---- ---- Prior to test Prior to test 120

Mechanical exhaust rate

[m3.s-1] ---- ---- 6.0 4.5 3.2

The source of fire and smoke consisted of fifteen methanol pans 450 mm square, which were put together to form an almost 1800 mm square fire source, placed centrally on the floor of the facility. A smoke candle was incorporated to generate smoke that would then follow the hot gases being produced by the fire. The burning of the source demonstrated an almost constant rate. The heat release rate was obtained by converting the average mass-burning rate, and this resulted in a 1300 kW fire for all experiments. Two trees equipped with Chromel–Alumel thermocouples, measured the smoke layer temperature during each test. One of the trees near the east wall tree No. 1 had thermocouples placed with 1 m spacing. The other tree No. 2 positioned near the west wall had thermocouples spaced 2 m along its vertical axis, except for proximity of the ceiling where the spacing was reduced to 1 m. Additionally photocells were set near the two-thermocouple trees, which had spacing of 2 m from the ceiling and 4 m from the floor for the measurement of optical density. The smoke layer was also recorded by eye. For observations purposes a scale was placed on the north boundary wall to locate the smoke layer interface boundary. In summary, the smoke layer height was measured using three sources, namely: (a) temperature profile, (b) optical smoke density and (c) by eye. The rate of mechanical smoke extraction was measured using pitot tubes and anemometers mounted in the exhaust duct on the 7th floor. The rate of natural airflow velocities was measured using a hot wire anemometer.

2.2.1 Natural filling rate

Similar to scenario A, the smoke layer was just let descend within the experimental hangar enclosure. This can be seen in Fig. 4 where the smoke layer is very quick to descend at first, and then it begins to slow down after approximately 120 s into the experiment. The measurements recorded by the two thermocouple trees and by the photometer give good comparison with each other. Also the standard filling equation developed by Yamana and Tanaka has given remarkable results. This standard filling equation is shown throughout the remaining scenarios i.e. natural and mechanical, as a guide to the performance of each strategy. Note that a time lag of approximately 60 s (Fig. 4) is present in the experiment, due to the time it took for the hot gas to rise from the fire source, impinge on the ceiling and then spread laterally across it. The run (1) incorporated a grid cell size of 500 mm in the x, y, z directions. This equated to 144000 cells within the hangar. The initial ambient temperature was set at 18 °C. The fire source was mimicked within FDS using a Heat Release Rate Per Unit Area (HRRPUA) of 401 kWm-2. Multiplying this (HRRPUA) by the fire area of 3.24 m2 gave a Heat Release Rate (HRR) of 1300 kW. By comparing the experimental results to the predictions of FDS, it can be concluded that the model did not behave as well as in the room environment. Time and memory constraints prohibited grid refinement below 300 mm. The run (1) gave the best result out of the three simulated runs, although the filling rate was

Condition

Test No.


14

overpredicted until the time of approximately 150 s with a maximum error of 30%, then under predicted the filling rate for the remainder of the simulation with a error of 50%. The smoke filling rate demonstrates erratic movement from a height of 25 m to a height of 22 m. This is due to the turbulence in the developing shallow smoke layer. The grid cell size was reduced to 300 mm in the run (2) (720000 cells overall) with the expectation of better results from the model. However, while the initial filling rate improved somewhat, a larger magnitude of error in the filling rate was evident towards the remainder of the simulation. This result was confusing as the CFD numerical model is generally expected to give better results on finer grids.

Then the grid cell size of 750 mm (46080 cells) and an initial time step of 0.5 s were employed in the run (3). This resulted in an overprediction of the filling rate. The overall results on grid size effect on quality of predictions are summarized in Table 3. The analysis of temperature predictions was made for the selected heights of 24 m, 16 m and 8 m. Generally FDS overpredicted the temperature in all runs. Typical predictions (run 1) are presented in Fig. 5. Temperatures at 24 m in run (1) has large error in the beginning (32% at 100 s), and finish up by getting smaller (8% at 500 s).

Fig. 4: Smoke layer time history plot. Natural filling in a hangar environment. Scenario B Table 3: Correlation of grid size with the quality of predictions for natural filling rate in a hanger

environment

Run Grid Cell Size (XYZ) (mm) Total number of cells Maximum deviation

from test results (%) 1 500 x 500 x 500 144000 50 2 300 x 300 x 300 720000 55 3 750 x 750 x 750 46080 62.5

SMOKE FILLING RATE IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700

TIME (s)

HEI

GH

T (m

)

EXP.THERMOCOUPLE EXP.PHOTOMETER EXP.EYECALCULATED EQUATION FDS LAYER HEIGHT RUN 1 (500X500X500) FDS LAYER HEIGHT RUN 2 (300x300x300)FDS LAYER HEIGHT RUN 3 (750X750X750)

1300 (Kw) Methanol Fire Source

Grid Representation


15

Fig. 5: Temperature history plot. Natural filling in a hangar environment. Scenario B

2.2.2 Natural venting

For this smoke control strategy, known as gravity venting in the UK, FDS gave much improved results in layer height predictions compared to those of the natural filling rate scenario. Gravity venting scenario includes two natural openings (windows) at the 7th floor of the facility. The effect of the natural vents is quite apparent (Fig. 6) in that the predicted layer trend lies above the standard smoke filling equation. It can be seen that FDS and the photometer readings in the experiment are in a good agreement. The standard filling equation and the thermocouples had a marginal error against FDS of approximately (12%). Runs (3) and (4) performed slightly better than run (1) due to better resolution. The final smoke layer height is predicted correctly for this scenario, in contrast to Scenario A, where it was consistently overpredicted. 2.2.3 Mechanical extraction (different

volumetric rates)

The next analysis looks at three different types of mechanical smoke extraction from the hangar facility. In the first run the mechanical extraction rate was documented as 6.0 m3s-1.

The effect of mechanical extraction is seen in Fig. 7 as both the experimental Thermocouple and Eye curves are slightly lifted off the standard filling equation line. Modeling gave contradictory results. Run (1A) produced rather scattered results at initial stage, and predicted strong downward propagation of smoke contradicting with common sense expectations and experimental results. Run (1B) showed better trend, although it overpredicts smoke descend rate initially and slightly underpredicts at later stages. The over prediction error is approximately (12%) at 50 s. The under prediction error is approximately (35%) at 400 s. The temperature plot (Fig. 8, run (1B)) shows an overprediction. The maximum temperature error is approximately (21%) at 100 s. Run (2) incorporated a reduced volume flux extraction rate of 4.5 m3s-1. Grid was changed to 500mm cell size in this simulation, as simulation times where in the order of 35 hours. This change served to check how much speed-up can be achieved, and to what extent the resulting error would increase. Many designers may be tempted to do this in a bid to receive results quicker.

TEMP RUN 1 SMOKE FILLING IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

35

40

45

0 100 200 300 400 500 600 700

TIME (s)

TEM

P D

EG C

TEMP EXP 24 TEMP EXP 16 TEMP EXP 8 FDS TEMP 8 FDS TEMP 16 FDS TEMP 24


16

Fig. 6: Smoke layer time history plot. Natural venting in a hangar environment. Scenario B

Fig. 7: Smoke layer time history plot. Smoke extraction of 6.0 m3s-1 in a hangar environment

NATURAL SMOKE VENTING IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700TIME (s)

HEI

GH

T (m

)

EXP.THERMOCOUPLE EXP.PHOTOMETER STANDARD FILLING EQUATIONRun 1(500x500x500) Run 2(500x500x500)(DT0.05) Run 3(400x400x400)(DT0.1)Run 4 (300x300x300)

Natural vents

1300 (kW) Fire Source Make up vent

SMOKE EXTRACTION 6.0m^3/s IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700TIME (s)

HEI

GH

T (m

)

EXP.THERMOCOUPLE EXP. EYE STANDARD FILLING EQUATION

FDS RUN 1A (500X500X500) DT =0.1 FDS RUN 1B (400X400X400) DT =0.1


17

Fig. 8: Temperature history plot. Smoke extraction of 6.0 m3s-1 in a hangar environment

Peculiar feature of 4.5 m3s-1 scenario (and the next one with 3.2 m3s-1) was that experiments were run with closed vent to investigate how well the mechanical extract system would perform without replacement air. Mechanical or natural extraction methods always need replacement air to function. However for the purposes of this validation study, we follow the experiment exactly, i.e. the vent was closed. It is clear from the layer height chart (Fig. 9) that closing up of the inlet vent did not effect the smoke extraction rate, as the experimental results lie above the standard filling equation. However FDS did not perform really well, a brief look at the layer height chart for smoke extraction using a 4.5 m3s-1 volume flux reveals this. FDS over predicted the filling rate from a height of 26.3 m to a height of 16 m by approximately (8%); thereafter it under predicted the filling rate by approximately (34%).

Temperature predictions for this scenario were slightly improved, with the maximum error being (22%) for the 16 m high thermocouple within the facility. FDS temperature predictions for the other two heights within the enclosure were below (22%). The final simulation of this scenario analyses mechanical smoke extraction with a volume flux of 3.2 m3s-1. This scenario had all the same characteristics of the previous except for a reduced volume flux, which was activated 120 s into the simulation. The filling rate takes a sudden turn at approximately 130 s (Fig. 10), and was over predicted from a height of 26.3 m to a height of 12.5 m, the maximum error up to approximately 40% at 68 s. Thereafter the effect of the extraction was evident, resulting in a final layer height error of 42% at a time of 400 s. Temperature predictions at heights 24 m, 16 m and 8 m were also poor. This error was probably due to the relative coarse grid applied.

TEMP RUN 1B MECH 6.0m^3/s SMOKE EXTRACTION IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

35

40

0 100 200 300 400 500 600 700

TIME (s)

TEM

P D

EG C

EXP TEMP 24 EXP TEMP 16 EXPT TEMP 8 FDS TEMP 8 FDS TEMP 16 FDS TEMP 24


18

Fig. 9: Smoke layer time history plot. Smoke extraction of 4.5 m3s-1 in a hangar environment

Fig. 10: Smoke layer time history plot. Smoke extraction (120 s) of 3.2 m3s-1 in a hangar environment

SMOKE EXTRACTION 4.5m^3/s IN HANGAR ENVIRONMENT

0.00

5.00

10.00

15.00

20.00

25.00

30.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00 700.00

TIME (s)

HEI

GH

T (m

)

FDS RUN 2 (500X500X500)(DT=0.1) EXP THERMOCOUPLE EXPT BY EYE STANDARD FILLING EQUATION

SMOKE EXTRACTION (120s) 3.2m^3/s IN HANGAR ENVIRONMENT

0

5

10

15

20

25

30

0 100 200 300 400 500 600 700

TIME (s)

HEI

GH

T (m

)

EXP THERMOCOUPLE EXP BY EYE STANDARD FILLING EQUATION FDS RUN 3 (500X500X500) DT 0.5


19

2.3 Scenario C − Smoke Filling and Extraction in an Atrium Environment

The next scenario analyses the predictive capability of FDS V 4.0.5 when applied to an atrium type enclosure. Atrium buildings are becoming highly popular in modern built environment. Architects are required to house many different divisions within one building for accessibility and sustainability purposes. In order to supply light and fresh air throughout such building architects like to use the atrium effect. The atria may also be designed for aesthetic reasons. However, smoke spread in such buildings can pose various threats to its occupants if not properly addressed. Due to large voids between the floors smoke can readily move quickly around the building, effecting areas quite remote from the fire source. Our main objective in this scenario is to challenge FDS to determine the most important physical parameters in the development of a smoke layer and the filling process within an atria environment. The experimental data used in the following study was published by Chow in 1994 [9]; the experiments were previously conducted by Hagglund in 1985 [10]. The present analysis incorporated experiments from test 1 of Chow’s paper [9] only. The enclosure under investigation consisted of two rooms (Fig. 11). A larger hall (Atrium) with maximum dimensions of 5.6 m by 5.6 m by 6.1 m high and a smaller fire room constructed adjacent to the atrium. The two rooms share a common doorway with dimensions of 1m wide by 2 m high. Two fires with different HRR are considered in this study. For simplicity the investigation is divided up into series. Series (1) incorporates a fire size of 250 kW, while series (2) has a fire source of 560 kW. Experimental conditions are summarised in Table 4. Numerous computational parameters for each series are reported in [11]. The inlet vent in the lower part of the atrium wall had dimensions of 0.8 m wide by 0.25 high and was open to the atmosphere. The walls, floors and ceilings consisted of concrete. The fire source comprised of kerosene pan situated centrally in the fire room floor. In the experiments, temperatures were measured at heights 200 mm, 700 mm, 1200 mm, 1700 mm, and 2200 mm above the floor in the fire room. Temperature and smoke layer heights were also measured in the atrium space. These were positioned 500 mm, 1500 mm, 2500 mm, 3500 mm, 4500 mm and 5500 mm above the floor. These values were plotted by Chow [9] and compared to three zone models. The results of

CFAST Zone Model are kept in the presentation for comparative reasons. The free NIST software, DXF2FDS, which helps with the set-up of complex geometries within FDS, was employed in this scenario. It must be borne in mind that the rectilinear approach on which FDS V 4.0.5 was developed is preserved after conversion. For instance, a curved roof will actually be modelled as a roof consisting of stair stepped rectangular obstructions. DXF2FDS creates a pretty picture for the geometry under investigation, however this picture is not what is really being modelled. No matter what a modeller specifies in a FDS input code, this is rearranged to suit the grid, which has been specified in that scenario. FDS users must be careful when determining vent area etc., because the requested and the actual can be totally different. 2.3.1 Series 1

As can be seen from the accompanying layer height chart (Fig. 12), FDS failed to predict accurately the smoke-filling rate within the atrium environment. As can be seen from the chart, a number of runs were carried out in a bid to improve the FDS predictions. Looking closely at the experiments trend lines i.e. the smoke meter and the eye recordings, an agreement between the two methods in the smoke decent rate is evident. FDS has to a certain extent mimicked this trend, although the position of the smoke interface was over predicted. The runs 2D, 2EA, 2EB, 2F and 2F2 all show the same trend. The run 2D came closest to the experimental prediction. The error associated with the 2D run layer height during the over prediction phase is approximately (52%) at 40 s, when compared to the eye trend, but is greater when related to the smoke meter trend. Run 2F1 had a Smagorinsky Constant of 0.15, but shows some numerical error when compared to 2F2. Run 2G employed finer mesh, taking approximately 30 hours of computation time. Its results are quite surprising as being the poorest simulation whilst incorporating the finest grid. This goes against the underpinning principle of Computational Fluid Dynamics. This run also shows twice the HRR specified in the input file (250 kW), Fig. 13. All other HRR predictions did not have this problem. The CFAST zone model trend line is incorporated in Fig. 12; it is evident that CFAST and FDS are in agreement from 30 s to 80 s. Note that CFAST ignores the momentum equation and assumes simple two-layer structure.


20

Fig. 11: Plan of experimental set-up for Scenario C

View from the fire room, looking into the atrium View from the atrium looking into the fire room

Atrium

Fire Room


21

Fig. 12: Smoke layer time history plot. Smoke filling, Series 1 in atrium environment

Fig. 13: HRR plot. HHR for the Run 2G. Smoke filling in atrium environment

SMOKE FILLING SERIES 1 IN ATRIUM ENVIRONMENT

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0 20 40 60 80 100 120 140 160 180

TIME (s)

HEI

GH

T (m

)

Expt. By Eye Expt. By Smoke MeterFDS LAYER HEIGHT 2F (100X100X100)(100X100X100) FDS LAYER HEIGHT 2D (200X200X200)(150X150X150)FDS LAYER HEIGHT 2EA (100X100X100)(200X200X200) FDS LAYER HEIGHT 2F1(200X200X225)(200X200X200)(0.15)FDS LAYER HEIGHT 2F2(200X200X180)(155X155X155)(0.15) FDS LAYER HEIGHT 2G (50X50X50)(155X155X155)(SMAG 0.1)FDS LAYER HEIGHT 2EB (100x100x100)(200x200x200)(S2%) CFAST ZONE MODEL

ATRIUM

250 (kW) Fire

eDOOR

INLET VENT

HHR 2G SMOKE FILLING IN ATRIUM ENVIRONMENT

0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0

550.0

600.0

650.0

0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 160.00

TIME (s)

HR

R (k

W)

HRR (50X50X50)(155X155X155) kW


22

Temperature predictions for the run 2D, Fig. 14, show an over prediction when compared to measured values. The maximum error associated with the run 2D at a height of 2500 mm is approximately 33%. This error goes up to 60% for the lower readings within the atrium. Temperature predictions in the runs 2F and 2F2 show the error similar to the case 2D. The run 2G showed the highest temperature errors as HRR was over predicted by FDS for this case. 2.3.2 Series 2

For the Series 2 experiment smoke descended to floor at approximately 70 s, compared to 110 s in series 1. This is a result of the higher HRR in this scenario. The same trend occurred here as in series 1, i.e. FDS over predicted the filling rate (Fig. 15). The FDS smoke filling prediction went only down to 1 m above the floor. CFAST and FDS are once again showing some agreement with each other until approximately 60 s. Apparently, the simple CFAST model performed much better in this case, since it was able to predict smoke descent to the

floor. The overall descent time was also quite well predicted by CFAST. Variation in the time step within FDS in run 2 did not change the result. Maximum layer height errors when compared to experimental eye recordings were approximately 57% at 40 s. FDS did not provide accurate smoke filling predictions within an atrium environment incorporating a fire of 560 kW. Temperatures in the fire room were over predicted. The thermocouple at the height 2200 mm had a maximum error of approximately 46%. The lowest thermocouple within the fire room at the height of 200 mm was also over predicted by FDS and had a maximum error of approximately 77%. Predicted temperatures in the atrium space also demonstrated over predictions. The maximum error resulted from the thermocouple reading at the height of 4500 mm (approximately 40% at 70 s).

Table 4: Experimental conditions for Scenario C

Test No. 1 Fire size/m2 Fire HRR, kW Lower vertical vent area [m2] Ceiling vent area [m2]

Series 1 0.25 250 0.2 0

Series 2 0.56 560 0.2 0

Fig. 14: Temperature history plot. Run 2D, Series 1. Smoke filling in atrium environment

TEMP RUN 2D SERIES 1 SMOKE FILLING IN ATRIUM ENVIRONMENT

0

10

20

30

40

50

60

70

80

0 50 100 150 200 250 300 350TIME (s)

TEM

P D

EG C

EXP TEMP 3500 EXP TEMP 2500 EXP TEMP 1500 EXP TEMP 500 FDS TEMP 3500FDS TEMP 2500 FDS TEMP 1500 FDS TEMP 500


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Fig. 15: Smoke layer time history plot. Smoke filling, Series 2 in atrium environment 2.4 Scenario D − Smoke Movement in a

Corridor Environment The final scenario in this study deals with smoke filling and gas temperatures within a corridor environment. Smoke curtains will be used in this scenario as means of smoke control. This method can be classed as compartmentation. Compart-mentation is probably the oldest and most basic form of fire protection. Compartmentation consists of using partitions, floors, and ceilings of a building to restrict the smoke spread. In the overall fire safety strategy for any building, smoke and hot gases are recognised as the major hazard, and its control must be a primary concern for not only the client, but also for the consulting engineer and equally the architect in charge of the design. This type of smoke protection may form part of the fire safety strategy within, for example, a nursing home. Such premises usually incorporate long corridors, which link almost every part of the building. With the incorporation of smoke curtains at strategic locations, the spread of smoke and gases can be restricted, giving staff ample time to evacuate the building.

Traditionally smoke curtains have been used to contain smoke in the event of fire. The curtain may be a part of either active (where by it drops to a specified height on alarm activation) or the passive fire protection system. In the present study the curtains will act as passive systems. Matsuyama and Wakamtsu [12] carried out a recent study to test room and corridor smoke filling for use in calibration of zone and field models. Two scenarios of their study are used to investigate the accuracy of FDS in predicting smoke movement, smoke filling and gas temperatures. The fire source in the experiment was a polyurethane triangular shaped mattress. The base width was 600 mm, while the height of the triangle was 900 mm. Using three load cells, the mass loss rate was recorded continuously. The HRR was then calculated by multiplying loss rate by the heat of combustion. By calculating the area of the triangle, it was possible to model this experiment within FDS. The HRR in this experiment is not steady, therefore the ramp file was set up to reflect the HRR obtained from the experiment within FDS. The fire in FDS can be ramped by specifying a time and a fractional HRR at that specific time.

SMOKE FILLING SERIES 2 IN ATRIUM ENVIRONMENT

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0 20 40 60 80 100 120 140 160 180

TIME (s)

HEI

GH

T (m

)

CFAST ZONE EXPT BY EYEEXPT SMOKE METER FDS RUN 1 (200X200X200)(155X155X155)FDS RUN 2 (200X200X200)(155X155X155)(0.05)

ATRIUM

560 (kW) Fire

eDOOR

INLET VENT


24

Thermocouple trees were situated within the building to measure the smoke layer height and temperature. The thermocouples used in the experiment were type K (0.3 mm-diameter). Fourteen thermocouples were placed on each tree. Using the N-percent method (N=10%) the smoke layer height and the average temperatures were calculated for all the experimental results. In this scenario the two studies were performed. The setup can be seen from Fig. 16. In the Study (A) smoke curtain (B) (color green) was situated at the west end of the corridor and its role was to prevent smoke from spilling into the lobby room at the end of the corridor. The Study (B) used same conditions as (A), but had a decreased HRR and utilised an additional smoke curtain (color green) situated more or less centrally across the corridor width. The smoke curtain depths were 700 mm below the ceiling for each study. The corridor had an inlet vent situated near the floor on the west wall. Layer height, smoke movement and gas temperature were analysed against experimental data, and also against each other, to show the effects, if any, of the smoke curtain(s). Summary of the computational parameters for both runs is presented in Table 5. From the layer height charts for the runs A and A1 (Figs. 17 and 18), it is evident that FDS has once again over predicted the smoke-filling rate within the fire room. At approximately 60 s the smoke layer begins to develop in the fire room. According to experimental data, smoke layer descends at an almost constant rate. In contrast, FDS predictions show that the smoke layer develops approximately 10 to 15 s earlier and fills the room very quickly down to a height of 1200 mm above the floor (run A). This filling slows down in the runs A1 and B, due to finer grid. The results for the run B are presented in Fig. 19. The error in layer perdition between the experiment and FDS is approximately 42% at 50 s in the run A. This error tends to decrease with the incorporation of the finer grid in the runs A1 and B. After decay of the fire in the experiment, the smoke layer in the fire room descends to 200 mm from the floor at position T0; FDS under predicts this situation by

only filling to approximately 600 mm above the floor, this happened in all the runs A, A1 and B. The corridor smoke layer in the experiment begins to develop at position T2 at about 120 s. It can be seen from the chart that position T5 is delayed by a time of approximately 30 s, this is due to the length of the corridor. FDS over predicts smoke movement within the corridor at a time of 85 s, making a time discrepancy of 35 s. The effect of the smoke curtain is observed to delay the smoke propagation time down the corridor. This observation is through the comparison between the experimental chart results for the cases A and B. In the study A the lobby smoke developed at approximately 150 s (EXP T6 curve), while in the study B it developed at 220 s (EXP T6). This difference is due to introduction of the second smoke curtain in the middle of the corridor. FDS simulations (runs A and A1) predict smoke in the lobby at a time of 150 s, which is correct, but then grossly over predicts the filling time within the lobby by about 50 s. In the run B the lobby smoke developed at approximately 165 s, this is an over prediction of 55 s. As a general tendency, the smoke layer descends right after the fire source has decayed. In the two experiments, the corridor smoke layer has dropped to a level lower than 1 m above the floor. This means that the corridor is unsafe after approximately 180 s at position T4. Comparing this to FDS predictions A, A1 and B demonstrates that even though FDS start off by over predicting filling rate, its merges with the experimental data at position T4 in all the runs. This is a fair prediction by the model from a designer’s perspective. In all the above runs, comparing the experimental temperature to the FDS predicted temperatures it can be concluded that the temperature near the fire source are all in good agreement (13% discrepancy) with the experiment until a maximum temperature observed in the experiment. After the maximum point FDS predictions diverge from the experimental ones (Fig. 20), far field temperatures tend to be over predicted in all the runs.


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[s-1

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Out

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Q* [-

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0.75

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0.53

Sim

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ion

time

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(%)

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RPU

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200x

200

4 x

100x

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4 x

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100x

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Scen

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Tab

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Fig. 17: Smoke layer time history plot. Run A. Smoke movement in corridor environment

Fig. 18: Smoke layer time history plot. Run A1. Smoke movement in corridor environment

LAYER HEIGHT RUN A SMOKE MOVEMENT IN CORRIDOR ENVIRONMENT

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250 300 350 400

TIME (s)

HEI

GH

T (m

)

EXPT1 EXPT2 EXPT3 EXPT4 EXPT5 EXPT6 FDS T0

FDS T1 FDS T2 FDS T3 FDS T4 FDS T5 FDS T6 EXPT0

LAYER HEIGHT RUN A1 SMOKE MOVEMENT IN CORRIDOR ENVIRONMENT

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250 300 350 400

TIME (s)

HEI

GH

T (m

)

EXPT1 EXPT2 EXPT3 EXPT4 EXPT5 EXPT6 EXPT0 FDS TO FDS T1FDS T2 FDS T3 FDS T4 FDS T5 FDS T6


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Fig. 19: Smoke layer time history plot. Run B. Smoke movement in corridor environment

Fig. 20: Smoke layer time history plot. Run A. Smoke movement in corridor environment

LAYER HEIGHT RUN B SMOKE MOVEMENT IN CORRIDOR EXPERIMENT

0

0.5

1

1.5

2

2.5

3

0 50 100 150 200 250 300 350 400

TIME (s)

HEI

GH

T (m

)

EXPT TO EXPT T1 EXPT T2 EXPT T3 EXPT T4 EXP T5 EXPT T6FDS T1 FDS T0 FDS T2 FDS T3 FDS T4 FDS T5 FDS T6

TEMP RUN A SMOKE MOVEMENT IN CORRIDOR ENVIRONMENT

20

30

40

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0 50 100 150 200 250 300 350TIME (s)

TEM

P D

EG. C

EXPT0 EXPT1 EXPT2 EXPT3 EXPT4 EXPT5 FDS T0 FDS T3 FDS T1 FDS T2

FDS T4 FDS T5


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3. CONCLUSIONS Significant, although limited due to a shortage of experimental data number of CFD simulations have been undertaken within this study in an attempt to evaluate smoke control strategies using the FDS V 4.0.5 computational fluid dynamics model. The study investigated the capability of FDS V 4.0.5 to accurately predict smoke control strategies within a variety of selected enclosures. Computational results were compared to full-scale physical experiments involving Natural Smoke Filling, Natural Smoke Venting, Mechanical Smoke Extraction and Smoke Movement. These obviously do not cover all the situations a fire safety engineer may encounter, however, they are all representative of real full-scale fire behaviour. Additionally, to the authors current knowledge, none of the experimental data used in the present paper have been employed earlier in FDS evaluation studies. From the first case of the present study, Scenario A ‘Smoke Filling in a Room Environment’, it can be concluded that FDS V 4.0.5 is capable of predicting the smoke filling rate within the limits specified by the models developers. An important discovery in this scenario was that, the specification of finer grids does not always reap better results. This is evident from the layer height chart representing the room scenario. The chart shows an over prediction of 16% in layer height during initial smoke filling, followed by an under prediction of 20% in the latter part of the simulation. In summary the results are satisfactory for the smoke-filling rate in a room environment. In the second case, Scenario B ‘Smoke Filling and Extraction in a Hangar Environment’ Natural Filling, Natural Venting and Mechanical Extraction strategies were investigated. For this scenario the area of the hangar was 24 m x 30 m with an overall ceiling height greater than 26 m. The initial over prediction for the smoke-filling rate increased to 30%, and the later filling rate had an under prediction error of approximately 50%. This results are beyond the limits highlighted by the models developers, and therefore FDS did not predict accurately the smoke filling rate within the hangar. FDS performed very well to predict Natural Venting scenario and predicted values were well below the error limit of 20%. Mechanical Extraction scenario was considered for three different extraction rates. For the volume extraction rate of 6.0 m3s-1 the layer height predictions performed reasonably well, with an over prediction of 12% and a under prediction of 35%. In the next case (4.5 m3s-1 extraction rate) the inlet vent was

closed and the result showed initially an over prediction of 8%, and an under prediction of 34% thereafter. Finally, a case was run with 3.2 m3s-1 extraction rate, initiated at 120 s and also with no inlet vent. This had an initial over prediction filling rate of 40%, which then ended up under predicting by 42%. The third test, Scenario C ‘Smoke Filling in an Atrium Environment’, was divided into two series. Series 2, which incorporated a larger fire source, did not give satisfactorily results; five runs did have the same trends over predicting the entire atria smoke filling process by 52%. CFAST zone model performed better than FDS in this case, predicting smoke descent down to the floor. This run also highlighted that the finer grid resulted in the poorest performance. HRR did not stay stable in the run 2G, diverging from its prescribed value. Maximum over prediction in the Series 2 was 57%. The final case of this study, Scenario D investigated ‘Smoke Filling and Movement in a Corridor Environment’. FDS over predicts smoke filling by 42%. FDS also over predicted smoke movement in the corridor by approximately 58%. However, the predictions converge back to experimental data by the time when untenable conditions are achieved in the corridor. The principal conclusions of the study are as follows: • Performance of FDS varies significantly

depending on particular smoke movement scenario. Performance seems to deteriorate with increase in geometrical complexity of simulated compartment/building.

• FDS gave an overall average error of 32% in

the smoke-filling rate. • Temperature predictions for FDS throughout

the study were over predicted on average by 35%.

• The above figures suggest that the claim [4]

that FDS can predict temperatures within 5 to 20% of the actual value is incorrect. Identical claim with respect to flow velocities was not investigated in the present study. However, it would be important to verify that claim as well.

• Effects of grid are very important to solution.

The specification of finer grids does not always lead to better results. It has been experienced in the present study that the solution does not always stabilise, as the grid is refined. Moverover, the specification of


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finer grids in some cases may significantly worsen predictions.

This effect (observed also by other investigators) requires careful investigation as it contradicts (at least, at first glance) with general philosophy behind CFD, and LES in particular.

• HRR is not necessarily maintained in

computations, in contrast to what is claimed. An example of this was observed in the run 2G, Scenario C were the predicted HRR was twice as high as it should be.

• In some cases (such as smoke filling in the

atrium) simple zone model can give better prediction than FDS.

• Time step is an important parameter in FDS, as

in any CFD code. Unfortunately, FDS does not allow users to vary this parameter. The authors believe that this flexibility is essential for any credible CFD code, and should be provided by developers.

• Smagorinsky constant did not have significant

effect in the problems considered in the present study.

REFERENCES 1. G.W. Zou and W.K. Chow, “Numerical studies on

fire environment by a field model”, International Journal on Engineering Performance-Based Fire Codes, Vol. 7, No. 1, pp. 18-34 (2005).

2. C.W. Lautenberger, J.L. de Ris, N.A. Dembsey, J.R. Barnett and H.R. Baum, “A simplified model for soot formation and oxidation in CFD simulation of non-premixed hydrocarbon flames”, Fire Safety Journal, Vol. 40, pp. 141-176 (2005).

3. W.K. Mok and W.K. Chow, “Verification and validation in modeling fire by computational fluid dynamics”, International Journal on Architectural Science, Vol. 5, No. 3, pp. 58-67 (2004).

4. K. McGrattan and G. Forney, Fire Dynamics Simulator (Version 4) user’s guide, NIST Special Publication 1019 (2005).

5. B. Hagglund, R. Jansson and K. Nireus, Smoke filling experiments in 6 x 6 x 6 meter enclosure, FOA Report C 20585-D6, National Defence Research Establishment, Sweden (1985).

6. B. Karlsson and J.G. Quintiere, Enclosure fire dynamics, CRC Press, Boca Raton (2000).

7. T. Yamana and T. Tanaka, Smoke control in large scale spaces, Part 2, Building Research Institute, Ministry of Construction, Japan (1985).

8. T. Tanaka and T. Yamana, “Smoke control in large spaces, Part 1: Analytic theories for simple smoke

control problems”, Fire Science and Technology, Vol. 5, No. 1 (1985).

9. W.K. Chow, “A short note on the simulation of the atrium smoke filling process using fire zone models”, Department of Building Services Engineering, Hong Kong Polytechnic University, Hong Kong (1994).

10. B. Hagglund, Comparing fire models with experimental data, FOA Report C20864-24, National Defense Research Establishment, Sundbyberg, Sweden (1985).

11. P. Coyle, An evaluation of smoke control strategies using Fire Dynamics Simulator (FDS), MSc Thesis, University of Ulster, UK (2005).

12. K. Matsuyama and T. Wakamtsu, Department of Architecture, Faculty of Science and Technology, Science University of Tokyo, NISTIR 6588 (2000).

FURTHER VALIDATION OF FIRE DYNAMICS SIMULATOR …...International Journal on Engineering...

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Transcript of FURTHER VALIDATION OF FIRE DYNAMICS SIMULATOR …...International Journal on Engineering...