Investigation of door width towards flame tilting behaviours and combustion species in compartment fire scenarios using large eddy simulation

International Journal of Heat and Mass Transfer 150 (2020) 119373

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International Journal of Heat and Mass Transfer journal homepage: www.elsevier.com/locate/hmt

Investigation of door width towards flame tilting behaviours and combustion species in compartment fire scenarios using large eddy simulation Q. Chen a,1, T.B.Y. Chen a,1, A.C.Y. Yuen a,∗, C. Wang a, Q.N. Chan a, G.H. Yeoh a,b a

ARC Training Centre for Fire Retardant Materials and Safety Technologies, School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia

b

a r t i c l e

i n f o

Article history: Received 20 September 2019 Revised 11 November 2019 Accepted 12 January 2020 Available online 20 January 2020 Keywords: Tilting fire Turbulence effects Detailed chemistry Large eddy simulation Combustion modelling

a b s t r a c t Numerical assessment on a large-scale compartment buoyant fires was performed to investigate the effect of exit door width on the flame tilting behaviour, evolution of the thermal layer and formation of combustion species. The simulations were performed using an in-house large eddy simulation (LES) based fire field model incorporating subgrid-scale (SGS) turbulence, detailed chemical kinetics combustion, soot and radiation models. A comprehensive set of simulation case studies have been carried out with various doorway opening sizes, to investigate the influence of the incoming air entrainment towards the deflection angle of the flame. In addition, the changes in the thermal interface, neutral plane and major chemical species profiles (i.e. CO/CO2 ) are also studied in detail. The fire model was validated against experimental measurements. It was found that increasing the door width elevates the neutral plane height and its correlated layer height of concentrated toxic gases. In addition, there is a critical door width (4/6) that produced the minimum fire tilting angle, deviation from the minimum door width results in an increase in flame tilting angle. It was discovered the centre fire have a range of titling angle between 58° to 75°, with a variation of 25% from 2/6 to 8/6 doorway sizes. This also totally affects the dynamics of the CO2 profiles with the compartment room, in which the concentrated toxic gas layer ascends as the fire is more ventilated. In general, it was discovered that having a large doorway size is mainly contributing to reduce the potential fire hazards in a one-opening compartment room fire scenario. The relation of compartment fire tilting can be critical especially for the design of fire protection systems. © 2020 Elsevier Ltd. All rights reserved.

1. Introduction With the rapid development of population density, the compactness of building occupants and the associated combustible furniture materials significantly increase the fire risks on houses and commercial buildings. According to the most recent annual report from Fire & Rescue New South Wales (FRNSW) Australia, 24.502 fire incidents were reported in the calendar year from 2017 to 2018, 36% of which were bush & grass fire and 22% were structure fire [1]. The top two occurring fires are normally classified as wildland fires and enclosure fires. In the past, fire safety and protection systems were mainly based on prescriptive codes [2] and they were found to be effective for traditional building developments

∗

1

Corresponding author. E-mail address: [email protected] (A.C.Y. Yuen). Qian Chen and Timothy Bo Yuan Chen contributed eqaully to this work.

https://doi.org/10.1016/j.ijheatmasstransfer.2020.119373 0017-9310/© 2020 Elsevier Ltd. All rights reserved.

[3]. Despite their relatively easy implementation, prescriptive codes are inflexible and usually lead to expensive designs. It has been a worldwide trend to develop performance-based codes for engineering designs [4–9]. Furthermore, the rapid adoption of lightweight composite materials in the construction industry has led to dramatic increases in fire risks such as the outbreak of combustible cladding materials [10–12]. These issues have exposed the shortcomings of current fire safety designs and regulation protocols. Therefore, the design of fire safety and protection systems should be specifically designed for buildings based on their architectural structures. Typically for any enclosure compartment rooms with one doorway opening settings, where the fire is located and centre of the room, the fire is titled to the inner portion of the room during to the incoming air entrainment from the surroundings. This tilting behaviour is found to promote the incoming/outcoming flow behaviour across the doorway opening, and subsequently leading to changes to the chemical species generation including key toxic gases such as CO/CO2 .

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Fire tilting is an important fire phenomenon driven by convection effects. As the profile of fire plume changed, a significant increase in heat convection and radiation transfer rate can be expected, and the direct contact with the fire plume itself causes ignition of all surrounding combustible materials leading to rapid flame spreading within the room or even the entire building. One typical example is fire spread on the carpet-renovated compartment, where the direction of the flame tilting will dramatically promote flame propagation. Although the phenomenon was observed during many enclosure fire experiments including compartment [13–15], tunnel cases [16] and mine drift [13], and with some of them reported that tilted flame boosted the flame spread rate [17], none of these enclosure fire experiments focus on the fire deflection. The experimental studies on the fire deflection phenomenon are still limited to open space pool fire, where the tilting effects were driven by the forced convection from entrained air flow [18]. Hu et al. [19] conducted a comprehensive set of experiment focusing on the burning rate and tilting effect of medium size pool fire. The experiment included several different fuel pans up to 70 cm in diameter with cross air flow speeds up to 4.5 m/s. Fei Tang et al. [20] conducted a similar experiment focusing on the fire length and tilting angle with controlled heat release rate up to 21 kW. Precise measurement on fire tilting angles and attachment lengths were studied by Wei Tang et al. [21] with arbitrary pool geometry of a 25 cm by 5 cm fuel slot. They found that there is a linear correlation between the local Richarson number and the flame re-attachment length. Fire tilting experiment with the heat release rate comparable with room fire was conducted by Cecilia and Elizabeth [22,23] with the fuel pool diameter of 2 m. Computational fluid dynamics (CFD) based fire model, also known as fire field model, was initially introduced to fire research by Ku et al. [24], Yang and Chang (for UNDSAFE-I) around 1970s. Early utilization of fire field models to practical scenario can be investigations independently conducted by Simcox et al. [25] on King Cross Station Fire of London Underground, which happened in 18th Nov 1987 with 31 deaths reported, and found out that the trenching effect caused the rapid fire growth. Constrained by the simulation power, all fire field models at this stage represent fire with volumetric hear source. Computational fluid dynamic (CFD) modelling approaches on building fires has become increasingly popular due to the significant advancement of numerical methodologies and computational power [26]. The heat and mass transfer, as well as the conservation of gas species and smoke particulates can be aptly computed by CFD models with quality meshing and a good selection of numerical models. Recently, numerous fire simulations studies based on CFD techniques have been applied. Considerable success were reported from studies ranging from compartment fires [27], wildland fire [28,29],fire forensic investigation [30–32], mechanisim of solid combustibles [33–36], and other fire pheomomenas such as fire whirls [37,38] and soot formation [39]. Fire tilting under crosswind is fire study topic that involves the very fundamental of combustion. When crosswinds hit the fire plume, the fuel-air mixing and as well as heat feedbacks become un-symmetric. Even in recent year, the majority of the researches remain experimental based [40–42] with very convectional fuel pan shape, either square or circular. These experimental study reported significant elevation of heat flux in the downstream, which can be a powerful driven mechanism of fire spreading in actual fire disasters. With the experimental studies still focusing on the fundamentals, no simulation studies can be found in the studies. With all the considerations mentioned above, the present work focuses on comprehensive modelling and numerical validation of fire-induced doorway flows, especially the effect of different doorway dimensions on the flame tilting behaviour and formation of toxic combustion products CO and CO2 . The major objectives are as follows: (i) an-house fire field model was adopted to a

compartment fire scenario with various exit door width, (ii) The performance two SGS turbulent models, namely the Smagorinsky (SM) and Wall Adaptive Local eddy Viscosity (WALE) model by validation with experimental data; and, (iii) investigation of fire tilting correlations to chemical species distributions within the compartment under different door width. 2. Mathematical model In this large eddy simulation (LES) based fire field model, aside from the governing equations for continuity, momentum, energy and other scalar transport properties, sub-modelling components including (i) gas-phase combustion, (ii) radiation heat exchange between fire source, walls and gaseous products, (iii) soot formation and (iv) sub-grid scale (SGS) turbulence models, are included to account for the complex fire phenomena which are non-linear and interactive in nature. 2.1. Combustion model The strained laminar flamelet combustion model for nonpremixed diffusion flames is adopted in this study. The derivation are detailed in previous works [43,44]. The scalar dissipation χ 0 is introduced to depicts the level of flame strain rate or the nonuniformity of mixture fraction from the chemical equilibrium. The beta form of the Probability Density Function (PDF) was utilised to account for the influence of the scalar dissipation and timedependant fluctuations effects for the combustion process. The filtered mass fraction for each species can be simulated as a function of ξ : 1

i = ∫ Yi (ξ , χ0 )P (ξ )dξ Y

(1)

0

With the value of species concentration evaluated, the filtered heat release rate can be derived with χ as independence:

1 2

ω¯ T = ρ¯ χ˜

n  i=1

∂ 2Yi P (ξ )dξ ∂ξ 2

1

h0f,i ∫ 0

(2)

The local peak scalar dissipation χ 0 mentioned in the Pre-PDF can be determined with both GEF and probability density function quantities obtained:

χ˜ 0 =

∫10

χ˜ F (ξ )P (ξ )dξ

(3)

The detailed chemical kinetics for the GRI-Mech 3.0 [45] and CHEMKIN 3.6 [46] was used to generate the flamelet library in function of mixture fraction and its variance, as well as scalar dissipation rate. A total number of 20 flamelet profiles were generated with distinct scalar dissipation rates χ ranging from 0.001 to 76. Fig. 1(a), (b) shows the flamelet profiles of major and selective minor species at two scalar dissipation rates (χ = 0.001 and χ = 5 respectively). 2.2. Turbulence model Based on the concept of Smagorinsky model (SM), another SGS model that specified for three-dimensional framework was developed by Nicoud and Ducros [47] known as Wall-Adapting Local eddy-Viscosity (WALE) model. As what its name suggested, WALE included a near-wall treatment features to capture the transition from laminar to turbulence. The turbulence viscosity in WALE is related to square velocity tensor rather than a linear relationship as SM:



μT = (Cw )  2

S˜i j S˜i j

Sidj Sidj

 52

 32



+ Sidj Sidj

 54

(4)

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Fig. 1. Flamelet profiles for (a) major and (b) minor species for various scalar dissipation rates.

Cw is the WALE model constant as 0.5 suggested by Nicoud and Ducros. WALE detects the turbulent structure by the combination of strain and rotations rate or from single aspect or either tensor, so that the turbulent viscosity term will be null naturally at the wall boundary without the inclusion of a damping function. Stress tensor rate in resolved scale is:

Sidj

 1 1 2 = g˜ + g˜2ji − δi j g˜2kk 2 ij 3 With

g˜2i j

(5)

∂ u ∂ u˜ ∂ u = ∂ x i and strain rate of S˜i j = 12 ( ∂ x i + ∂ x j ). j j i

    ∂ ρ¯ N˜ soot ∂ ρ¯ ui N˜ soot + ∂t ∂ xi     ˜ ∂ ρ¯ u N ∂ μT ∂ N˜ soot th soot =− + + S˜η,soot ∂ xi ∂ xi Sc ∂ xi

(7)

With new source terms are and increase in number density S˜Y,soot and instantaneous production rate of soot particles S˜η,soot ,

S˜Y,soot = δ¯ + γ¯ − ζ¯

(8)

S˜η,soot = α¯ − β¯

(9)

As different algorithm is implemented in Moss Brookes mode, the two terms α¯ and β¯ are now depict the soot particle generation rates of inception and coagulation with different constants as:

2.3. Soot model In this study, the semi empirical model was later proposed by Brooks and Moss [48] was adopted to provide essential description of soot particle inception, coagulation, surface growth and oxidation. The model was developed from the Moss model and replacing soot volume fraction with soot mass fraction, thus the transport equation changed as:

    ∂ ρ¯ Y˜soot ∂ ρ¯ uiY˜soot + ∂t ∂ xi     ˜  ∂ ρ¯ uthYsoot ∂ μT ∂ Y˜soot =− + + S˜Y,soot ∂ xi ∂ xi Sc ∂ xi



α¯ = Cα NA β¯ = Cβ

XC2 H2 P˜ RT˜

24 × RT˜ ρsoot NA



 T α

exp −



T˜

˜2 d1p/2 N

(10)

(11)

Same manner applied to other three term. δ¯ and γ¯ are defined as increment rates on number density by nucleation and surface growth with ζ¯ represents the corresponding reduction rate due to oxidation. These terms can be calculated as:

(6)

δ¯ = M p,soot α¯

Fig. 2. Experimental configurations showing the dimensions of the fuel bed and dimension of the different door widths [13].

(12)

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Q. Chen, T.B.Y. Chen and A.C.Y. Yuen et al. / International Journal of Heat and Mass Transfer 150 (2020) 119373

γ¯ = Cγ

XC2 H2 P˜ RT˜



exp −

ζ¯ = Cζ Coxid ηcoll

Tγ T˜





π N˜



1/3 6Y˜soot 2/3 ρsoot



1 / 3 XC2 H2 P˜ √  ˜ T πN ˜ RT



6Y˜soot

(13)

2/3 (14)

ρsoot

Two dimensionless parameters, oxidation rate scaling number Coxid and the collisional efficiency number ηcoll are suggested as 0.015 and 0.04. The empirical constants applied in the Moss Brookes model have been validated in previous studies [49]:

Cα = 541/s, Cβ = 1 1/s, Cγ = 0.1kgm/kmol · s Cζ = 105.8125kgm/kmol · K 1/2 · s, Tα = 210 0 0K, T γ = 1210 0K 2.4. Radiation model Fig. 3. Computational Domain Generated for Centre Located Fire of Steckler Cases.

The radiative heat transfer is modelled using the filtered radiative transfer equations (FRTE) for non-scattering grey gas solved by the discrete ordinates method (DOM) with the S4 quadrature scheme accordingly to Jamaluddin and Smith [50]. Discrete ordinate approximation applied a discretising to the entire solid angle (that  = 4π ) using finite number of ordinate directions with corresponding weighting factors. The equation is ultimately solved for sets of directions and the integrals over solid angles approximated with Gaussian quadrature and annotated as SN approximations. The SN approximation discretises the positive and negative directions’ cosine values to ξ n , ηn and μn obey that:

ξ +η +μ =1 2 n

2 n

2 n

(15)

A typical S4 quadrature scheme DOM was proposed by Jamaluddin and Smith [51] as:



ξj

∂ I¯j ∂ I¯j ∂ I¯j ¯ σT + ηj + ζj ≈ ka,g − I¯j ∂x ∂y ∂x π ˜4



(16)

With Eb = σ T 4 which was described above. The S4 quadrature would have a maximum of 24 discrete ordinates over solid angles, thus the source term due to radiation on the energy transport equation can be expressed as:

S¯rad ≈ −4k¯ a Eb +

24 

w j k¯ a I¯j

(17)

j=1

The gases absorption coefficient k¯ a for each combustion products were determined in the WSGGM previously. Weighted Sum of grey Gases Model(WSGGM) is introduced by Hottel and Sarofim [52] gas property model. WSGGM considers the total absorptivity and emissivity can be interpolated as the sum of a grey gases emissivity weighted factor depending on temperature. In general, overall absorption coefficient can be represented as the sum of C O2 − H2 O mixture and soot as:

k¯ α = k¯ a.g + k¯ a,soot

(18)

With the ɛT as the total emissivity over a distance L presented as:

εT =

I 

aε,i (1 − e−ki PL )

(21)

i=0

A determined formula was proposed by Kent and Honnery [53] for soot gases’ absorbing coefficient:

k¯ a,soot = 1862 fv T

(22)

3. Experimental configuration and numerical setup Numerical simulations presented in this article is based on a series of full-scale compartment fire tests carried out by Steckler et al. [13]. A total number of 55 experiments were conducted in one compartment on 8 fire locations with 34 different Heat Release Rates (HRR), 8 door widths and 3 different window sizes.the experimental configurations for fire test number 14 were considered in this study. The experimental setup is illustrated in Fig. 2. The room compartment has an inner width and length of 2.8 m and a height of 2.18 m, uniform wall thickness of 0.102 m. A doorway opening 1.83 m high was positioned at one side of the wall to allow ventilation to the room. Different door widths (d) were implemented to the compartment as detailed in Fig. 2. Aspirated thermocouples and bi-directional velocity probes were placed at the doorway centreline to measure the vertical temperature and flow profiles. Moreover, room temperature distribution Table 1 Summary of the mesh systems used in the mesh sensitivity analysis. Mesh number

Mesh size (m)

Total number of cells

R∗ range

1 2 3

0.1 0.04 0.03

43,200 345,600 675,000

1/15 < R∗ <1/5 1/15 < R∗ <1/10 1/15 < R∗ <1/10

With the gas mixture absorption evaluated as:

k¯ α .g = aε,i ki P

(19)

Where α ɛ, i represents the weighting factors for all grey gases considered, ki denotes the absorption coefficient of each gases. P represent the partial pressure of the gases, as only CO2 andH2 O are considered, P = PC O2 + PH2 O . For the distance that greater than 10−4 m, the absorption followed Beer-Lambert’s Law as:

1 k¯ a,g = − ln (1 − εT ) L

(20)

Table 2 List of simulation case studies. Case

Heat release rate (kW)

Door width (m)

(2/6) (3/6) (4/6) (5/6) (6/6) (7/6) (8/6)

62.9 62.9 62.9 62.9 62.9 62.9 62.9

0.24 0.36 0.49 0.62 0.74 0.86 0.99

Q. Chen, T.B.Y. Chen and A.C.Y. Yuen et al. / International Journal of Heat and Mass Transfer 150 (2020) 119373

was measured by another set of thermocouples placed at the corner of the room. Propane was selected as the fuel and the fuel pan was in circular shape with diameter of 0.3 m located in the centre of the compartment. The heat release rate of the burner was 62.9 kW.

5

3.1. Modelling configuration and boundary conditions The three-dimensional computational domain of the simulation comprises of a rectangular box with a size of 2.8 m (W) × 4.2 m (L) × 2.18 m (H). Neutral plane height is one of the most

Fig. 4. Simulation Results of Steckler Location A: Velocity Profiles by SM & WALE (Top Left & Top Right); Temperature Profile by SM & WALE (Bottom Left & Bottom Right).

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important driving factor of this research, and the determination of this height required adequate details of both flow from inlet to outdoor and entrainment from outdoor. Thus additional 50% room length extended region was applied to accurately capture the flow distribution and prevent any un wanted influence from the outlet boundary conditions. The computational domain is shown in Fig. 3. The opening boundary conditions applied

in the present study is suggested by Panpanastasiou et al. [54]. By using this method, the natural entrainment of the ambient fluid was allowed. Three uniform mesh systems were numerically studied to examine the mesh independency of this case scenario. The characteristic length scale (DiNenno et al. [55]) was used to determine the mesh sizes, the details are summarised in Table 1.

Fig. 5. Simulation Results of Steckler with Different Door Width: Velocity & Temperature Profiles of Width 2/6(Top Left & Top Right); Velocity & Temperature Profiles of Width 3/6 (Bottom Left & Bottom Right).

Q. Chen, T.B.Y. Chen and A.C.Y. Yuen et al. / International Journal of Heat and Mass Transfer 150 (2020) 119373

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Fig. 6. 2D temperature contour of cross-section of compartment with door width (a) 2/6, (b) 3/6, (c) 4/6 and (d) 5/6.

Mesh sensitivity analysis was performed to assess the optimise mesh system for this simulation case while retaining numerical accuracy and stability. A series of simulations were performed on 7 different door widths to investigate the effect of the doorway on the fire-induced doorway flow and the subsequent flame tilting behaviour. The full list of simulation case studies are provided in Table 2.

Furthermore, both models slightly underpredicted the location of the neutral plane (i.e. the height where the velocity is 0). Because of the underprediction of the neutral plane, the temperature and velocity profile were also shifted when comparing the experimental data. Nevertheless, for the upper region of the door width, the WALE model performed significantly better than SM. Taking into consideration the validation results, the WALE model was adopted for the rest simulations performed in this study.

4. Results and discussion 4.2. Comparison of fire tilting against different door width 4.1. Validation Before performing full simulations of the enclosure fire, the fire model was first validated on 62.9 kW case with 6/6 door width. Two turbulence models were tested in the validation study, namely, the Smagorinsky (SM) and the Wall Adaptive Local eddy viscosity (WALE) model.The velocity and temperature profiles recorded on the centre of the doorway was used to compare against the numerical predictions. Fig. 4 shows the velocity and temperature comparisons between the SM and WALE turbulence model with experimental results. As can be seen in the figure, both the velocity and temperature profiles for both SM and WALE cases were in good agreement for heights below 0.8 m.

Numerical simulations were performed for 7 different exit door widths to investigate the effect of the door width on the fire tilting behaviour and the height of the neutral plane. The velocity and temperature comparisons between numerical and experimental results taken at the door are shown in Fig. 5. Overall, all 7 cases achieved highly accurate predictions for velocity and temperature profile when compared to experimental results. All simulated neutral planes’ heights are comparable with the actual magnitudes within 5% error, which is the same level of the measurement accuracy of the instruments used by Steckler et al. The flame geometry is represented by the visible flaming temperature range (The cut-off line is approximate 700 K and above).

Fig. 7. 2D temperature contour of cross-section of compartment with door width (a) 6/6, (b) 7/6 and (c)8/6.

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minimum fire deflection which can be a good suggestion for the design of fire doors. To further investigate the effect of changing door width on the flame tilting angle and the height of the neutral plan, the results were extracted from the simulation and plotted against the door width in Fig. 8. The results showed that as the height of the neutral plane increases linearly with each incremental increase in door width. However, the flame tilting angle was found to behave differently to the neutral plane. The titled angle decreased (i.e. more tilted towards the ground) as the door width increases from 0.24 m to 0.49 m. Further increase in door width from 0.49 m onwards causes the tilting angle to increase (i.e. less tilted towards the ground). The 0.49 m door was found to be the critical door width that resulted in the minimum fire tilting angle of 58°, either increasing or decreasing the door width results in a proportional increase in tilting angle. Fig. 8. Plot of (i) flame tilting angle and (ii) neutral plane height against door width size.

The flame tilt angle is taken to be angle between the vertical centreline at the centre of the fuel pan and the flame centreline, which is the approximate representation of the flame main body. The temperature contours are presented in Figs. 6,7. There are two driving mechanisms for the tilting of room fire, one is the air mass flow into the door due to the temperature difference created by fire which is similar to the forced convection, this effect is stronger for larger door opening. On the other hand, smaller door width slows down the mass transfer between the room and external, so that the gases especially oxygen will be run out soon to create a great pressure gradient inside the room, such pressure difference acts similar to buoyany effect to the fire and make to fire to tilt. With the shifting between the major driven mechanism of fire tilting, a critical door width would be expected to produce a

4.3. Comparison of CO/CO2 distribution against different door width In order to understand the influence of the doorway opening width against the amount of major combustion product generations due to the fire, as well as studying the circumstances for fully- and under-ventilated room fires, the centreline profile at the doorway opening were investigated as depicted in Fig. 9. As can be seen, when the fire is provided with sufficient oxygen content, the amount of CO generation significantly reduces (i.e. for cases with door width sizes large or equal to 6/6). Likewise, the CO2 content also considerably reduces alongside with lesser amount of CO. This can be attributed to the fact the gas species content are more dispersed provided with a larger door opening area. Furthermore, fewer chemical products are yielded when the room is more ventilated. By observing the CO/CO2 profiles, it can be demonstrated that they are also influenced by the difference in heights of the neutral plane. Therefore, having a higher vertical level of neutral

Fig. 9. Mass fraction profile of a) CO and b) CO2 at the centreline of the door.

Q. Chen, T.B.Y. Chen and A.C.Y. Yuen et al. / International Journal of Heat and Mass Transfer 150 (2020) 119373

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Fig. 10. 2D Contour of the mass fraction of CO2 for door width (a) 2/6, (b) 3/6, (c) 4/6 and (d) 5/6.

Fig. 11. 2D Contour of the mass fraction of CO2 for door width (a) 6/6, (b) 7/6 and (c) 8/6.

plane will led to a better situation since majority of the toxicity gases that suffocate human occupants will maintain at the ceiling level. Again, the increase in doorway sizes can elevate the neutral plane and cause the chemical species to migrate at a higher level. It is then recommended to increase the total area of the doorway for fire safety concerns. In addition to the doorway centreline CO/CO2 profiles, the contour plots demonstrated the full picture of the changes in major species concentrations and distribution within the compartment room for various sizes of doorway width and fire tiling angles (i.e. Figs. 10 & 11). In Fig. 10(a), it is evident that the amount of CO2 produced in the compartment room is notably higher than that of the doorway with 3/6 sizes or larger. Similar to the hot plume profile, CO2 also is produced from the flame arsing and impinge at the ceiling due to the buoyancy effect. However, the content is richer at the rear portion of the room during the influence of the air entrainment via the doorway. With the increase of doorway area, it can be seen that the incoming/outgoing CO2 transition plane gradually shirts upwards, which corresponding to the temperature profiles. In Fig. 11(a), as the fire is more deflected, it also has a minor effect towards the CO2 transition plane, as it formulates a pathway for the chemical products to travel to the

inner region of the compartment. In general, it was discovered that having a large doorway size is mainly contributing to reduce the potential fire hazards in a one-opening compartment room fire scenario. 5. Conclusion In this article, an in-house fire field model utilising the largeeddy-simulation (LES) framework has been developed specifically to study the fire tilting behaviour. With the application of LES, the temporal and fluctuating phenomena involved for a deflected nonpremixed diffusion flame including turbulence mixing between fuel and air, radiation heat exchange by the hot gas layer, detailed chemical kinetics for combustion and soot formation are all considered in this numerical code. To examine the validity and understand the influence of the tilting fire towards the thermodynamics, chemical formation of species, a comprehensive set of simulation case studies has been performed on a 2.8 m x 2.8 m 2.18 m fire compartment room with a single door opening, where door width ranges from 2/6 to 8/6 ratio to the height. The aim was to investigate the effect of different door opening on fire tilting angle, height of neutral plane and the distribution of toxic gases

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throughout the room. As part of the validation study, two different sub-grid-scale (SGS) turbulent models, Smagoringsky Model (SM) and Wall Adapting Local eddy viscosity (WALE) model were imployed for validation with experimental velocity and temperature profile, and WALE produced a superior outcome on both temperature and velocity. WALE was configured in simulations of seven different door widths for fire tilting behaviour and successfully predicted that the tilting angles range from 58 to 75°. A non-linear relationship was found between the door width and tilting effect as the minimum angle of 58° was observed with the critical door width 4/6, and the tilting angles produced by door width 2/6 and 8/6 are nearly identical. From the neutral plane point of view, the simulations reconstructed the experimental measurement trend line about the elevation of neutral plane with expanding of door width, and further identified that such elevation on neutral plane shifted up the layer of concentrated toxic gases owing the advance of detailed chemical kinetics. Based on the contour plots of the key chemical species, wider door opening was found to be more preferable as the layer of concentrated toxic gases such as CO and CO2 increase to a higher level and the overall amount of asphyxiated gases throughout the compartment also significantly reduced. Nonetheless, in terms of fire spreading, there is a critical door width with minimum fire tilting effect slows down the fire spreading especially for rooms occupied with flammable materials. In short summary, the tilting fire behaviour and its involving dynamics with the surrounded fluid mixture are considered by incorporating detailed chemistry combustion model coupled with LES, radiation and soot formation sub-modelling components. It was discovered that the deflection of the flame will change the thermodynamics, species transport, and interfaces within the compartment room, which can be manipulated by controlling the area of the doorways. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ijheatmasstransfer. 2020.119373. References [1] 2017 - 2018 Annual Report, Fire and Rescue NSW, 2018. [2] AS 1851-2012, in, Standards Australia, 2012. [3] A.C.Y. Yuen, T.B.Y. Chen, W. Yang, C. Wang, A. Li, G.H. Yeoh, Q.N. Chan, M.C. Chan, Natural ventilated smoke control simulation case study using different settings of smoke vents and curtains in a large atrium, Fire 2 (1) (2019) 7. [4] A. Buchanan, The challenges of predicting structural performance in fires, in: Fire Safety Science – Proceedings of the 9th International Symposium, Karlsruche , Germany, 2008, pp. 79–90. [5] R.W. Bukowski, V. Babrauskas, Developing rational, performance-based fire safety requirements in model building codes, Fire and Materials 18 (1994) 173–191. [6] G. Hadjisophocleous, N. Bénichou, Development of performance-based codes, performance criteria and fire safety engineering methods, Int. J. Eng. Perform.-Based Fire Codes 2 (4) (20 0 0) 127–142. [7] B. Meacham, R. Bowen, J. Traw, A. Moore, performance-based building regulation: current situation and future needs, Build. Res. Inf. 33 (2) (2005) 91–106. [8] B.J. Meacham, The evolution of performance-based codes and fire safety design methods, society of fire protections engineers, (1996). [9] S.K. Au, Z.-.H. Wang, S.-.M. Lo, Compartment fire risk analysis by advanced Monte Carlo simulation, Engineering Structures 29 (9) (2007) 2381–2390. [10] T.B.Y. Chen, A.C.Y. Yuen, G.H. Yeoh, W. Yang, Q.N. Chan, Fire risk assessment of combustible exterior cladding using a collective numerical database, Fire 2 (1) (2019) 11. [11] M. Bonner, G. Rein, Flammability and multi-objective performance of building façades: towards optimum design, Int. J. High-Rise Build. 7 (4) (2018) 363–374.

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