numerical simulations of mechanically-ventilated multi

th

24 International Conference on Structural Mechanics in Reactor Technology (SMiRT 24) th 15 International Post-Conference Seminar on “FIRE SAFETY IN NUCLEAR POWER PLANTS AND INSTALLATIONS“

NUMERICAL SIMULATIONS OF MECHANICALLY-VENTILATED MULTI-COMPARTMENT FIRES James R. Stewart and Adrian Kelsey Health and Safety Executive, Buxton, United Kingdom

ABSTRACT Computational Fluid Dynamics (CFD) is increasingly being used in the nuclear industry for fire consequence modelling. Of key importance is the ability of models to adequately capture fire behaviour in confined, mechanically-ventilated environments typical of nuclear installations. This paper assesses the capabilities of the widely-used Fire Dynamics Simulator (FDS) for modelling scenarios of practical interest for the nuclear industry. FDS simulation results are compared to experimental data obtained from the PRISME Integral experimental series of tests conducted by the Institut de Radioprotection et de Sûreté Nucléaire (IRSN). Here the ‘PRISME INTEGRAL-4’ test, conducted in the DIVA facility, a well-sealed mechanically-ventilated multi-compartment configuration at IRSN, is simulated. The scenario comprises a hydrogenated tetra-propylene (HTP) pool fire. This paper serves as an evaluation of FDS and its HVAC (Heating, Ventilation and Air Conditioning) network model focussing on model sensitivity to the choice of combustion modelling approach. The CFD results show that FDS is capable of capturing the main fire-induced effects on the mechanical ventilation system and the fire behaviour in under-ventilated conditions for a well-prescribed fire source. However, the ability of the model to accurately capture species concentrations in underventilated compartment conditions is poor. The choice of combustion modelling approach can have a substantial influence on model predictions of the concentration of combustion products. Crown Copyright © 2017 INTRODUCTION The PRISME (Propagation d’un Incendie pour des Scénarios Multilocaux Elémentaires) project, coordinated by the OECD Nuclear Energy Agency (NEA), is a joint international research project focussing on fires in nuclear power plants. The main objectives of the project are to address knowledge gaps in modelling fire growth and propagation, fire extinction phenomena, and the prediction of damage to and the impact of smoke on safety-critical systems. The project addressed these objectives by performing experiments in a large-scale, well-sealed, mechanically-ventilated multi-compartment enclosure. In addition to the experimental work the members of a working group conducted benchmark simulations of a number of the experiments. Audouin et al. [1] give an overview of the PRISME project and the main experimental findings from each of the PRISME test campaigns. The initial campaigns in the PRISME project characterised single effects related to fires in enclosures. PRISME SOURCE characterised well defined pool fires as sources, PRISME DOOR looked at the transport of smoke and hot gases between compartments through open doors, and PRISME LEAK considered the transport of smoke and hot gases between compartments through leaks and ventilation ducting. The final campaign, PRISME INTEGRAL, consisted of experiments that included combinations of these different effects.

The effects of fire on mechanical ventilation systems are of critical importance for many fire safety applications. The fire-induced pressure rise in connected multi-compartment facilities, common in both nuclear and offshore oil and gas facilities, can overwhelm a mechanical ventilation system. This can lead to a loss of containment of the fire and propagation of smoke and other gaseous combustion products through an HVAC network. Conversely, a mechanical ventilation network can be used as a means of fire control, for example, through the use of dampers to reduce oxygen supply leading to fire extinction. FDS, a CFD model designed to simulate fire-induced flow and heat transfer [2], has been used to simulate experiments from the PRISME project to predict the effects of fire on mechanical ventilation systems. Previous studies have been reported using both FDS 5 and 6 for studying the interaction of fires with mechanical ventilation. A major update in FDS 6 was the addition of an HVAC network solver, although a simple HVAC fan model was included in FDS 5.5 onwards [3]. Beji et al. [4] conducted a parametric analysis using FDS 5.5 to assess the influence of a number of key parameters on the pressure and temperature within a confined, mechanically-ventilated compartment. The scenario considered was similar to that used in the PRISME Source tests, with a pool fire source located at the centre of a single compartment. Their results showed that the specified ventilation operating conditions substantially influenced compartment pressure profiles and highlighted the need for accurate HVAC boundary conditions in the model. Wahlqvist and van Hees [5] presented validation of a pre-release version of FDS 6 against a number of the PRISME experiments from the Source, Door and Leak test campaigns. This work was primarily focussed on the evaluation of the HVAC network solver. Their results showed that the model was capable of capturing the pressure-induced effects on the mechanical ventilation system and complex combustion phenomena, such as ghosting flames, were predicted as a result of fluctuating flow rates at the ventilation inlet. Beji et al. [6] used FDS 5 to simulate the PRISME INTEGRAL-4 test, which involved a connected, multi-compartment configuration with a pool fire source. This modelling incorporated a simplified representation of the HVAC network, using isolated ducts with imposed fan curves for each of the HVAC inlets and the exhaust. Their results showed that the model correctly captured qualitative trends in compartment pressure, temperature and ventilation flow rates, but under-predicted the measurements by up to 22 %. This paper seeks to extend the work reviewed above to use FDS 6, including its HVAC network solver, to investigate a complex fire scenario taken from the PRISME Integral experimental series of tests. The scenario, PRISME INTEGRAL-4, involves a hydrogenated tetrapropylene (HTP) pool fire source located in a multi-compartment facility ventilated entirely through a mechanical ventilation network. DETAILS OF THE PRISME EXPERIMENTS Description of the Experimental Facility The PRISME fire tests were conducted inside the DIVA facility at the IRSN laboratories. The facility is located inside the JUPITER compartment. The DIVA facility is confined, mechanically-ventilated and constructed of 30 cm thick reinforced concrete walls. It comprises four interconnected rooms and a corridor (see Figure 1). Each of the rooms on the lower floor has dimensions of 5 m x 6 m x 4 m and the corridor, which runs alongside all three of these rooms, is 15.6 m x 2.5 m x 4 m in size. The rooms can be connected through openings and doors, or these can be closed. For the Integral tests simulated here, the upper room (Room 4 in Figure 1) was not connected and was not used in the experiments. Each room in the DIVA facility can also be connected to the inlet and outlet ducts of the mechanical ventilation system.

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The combination of rooms and mechanical ventilation allow fire experiments to be performed in configurations representing nuclear power plant. The data acquisition system in the DIVA facility allows measurements of these experiments to be made on up to 800 channels [7].

Figure 1

Perspective view of the DIVA facility inside the JUPITER compartment (Figure courtesy of IRSN [7])

Overview of the PRISME INTEGRAL Test Series The Integral test series was the final experimental campaign conducted under the PRISME project. The previous campaigns had investigated single effects. The Integral experiments were designed to investigate more complex scenarios by combining these effects and were aimed at studying fire behaviour of different fire sources in mechanically-ventilated, multicompartment configurations. Six experiments were performed to investigate: the influence of room configuration on smoke propagation through doorways in confined and mechanicallyventilated conditions; the behaviour of cable and electrical cabinet fires in under-ventilated conditions; the influence of damper closure and sprinkler activation on fire behaviour. Details of the PRISME INTEGRAL-4 Test The fire experiment used in the numerical study presented here is the PRISME INTEGRAL-4 test [7]. The test involved the lower three compartments and the corridor of the DIVA facility, shown schematically in Figure 2. For the Integral test simulated here, the ceilings of all three rooms and the corridor were lined with 50 mm thick rock-wool insulation. The walls of the fire room were lined with 30 mm and 60 mm thick rock-wool panels in the lower and upper portions of the room, respectively. The walls of room L3 were also lined with 30 mm thick rock-wool insulation. The walls of room L1 and the floor in all of the rooms and the corridor were not insulated. For the INTEGRAL-4 test the fire source was a 1 m diameter pool of HTP with an initial mass of 52 kg located at the centre of room L2 (see Figure 2). The fuel was contained in a steel pan of diameter 1.129 m, shielded with 50 mm thick rock-wool insulation, at an elevation of 0.4 m above the compartment floor. A propane burner was used as the ignition source.

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Figure 2

Schematic of the DIVA facility as used for the PRISME INTEGRAL-4 test– HVAC supply vents (blue) in L0 and L1, HVAC exhaust (red) in L3, fire (orange) at centre of room L2 and thermocouple tree locations (red dots)

The multi-compartment configuration was mechanically ventilated through supply vents located in room L1 and the corridor L0, and an exhaust vent located in room L3. All of the vents had a cross-sectional area of 0.18 m2 (0.3 m x 0.6 m). Initial flow rates of approximately 500 m3/h and 2600 m3/h were supplied through the vents in L0 and L1, respectively, and an exhaust flow rate of 3100 m3/h through vent L3 was used. The mechanical ventilation was used to ensure negative pressure confinement inside the DIVA compartments at the start of the fire tests. The DIVA facility was heavily instrumented during the PRISME tests with measurements of room pressure, ventilation flow rates, doorway velocities, room temperature and concentrations of O2, CO and CO2 obtained during the INTEGRAL-4 test. In addition, the fire heat release rate (HRR) was estimated based on a carbon dioxide generation (CDG) chemical method as described in [8], and the mass loss rate (MLR) of the source was also measured. CFD MODELLING CFD Model Setup The modelling work described in this paper has been performed using FDS version 6.4.0 [2]. This version of the model incorporates a full HVAC network solver coupled to the hydrodynamic solver used to model the fluid flow. These two aspects of the model are coupled such that changes in compartment pressure affect the mechanical ventilation, which in turn influences compartment conditions. An HVAC model was first included in FDS version 5.5 [3] to meet the need for more advanced ventilation boundary conditions. In earlier versions of the model it was only possible to use simple, fixed-flow or fixed-pressure boundary conditions to represent sources of ventilation and openings. The new functionality of the HVAC network solver expands the capabilities of FDS so that fire-induced pressure effects can more readily be accounted for within ventilation boundary conditions. Grid sensitivity tests were performed using mesh resolutions based on the characteristic fire diameter 𝐷 ∗ [2], where 𝐷∗ = (

𝑄̇ 𝜌∞ 𝑐𝑝 𝑇∞ √𝑔 4

2 5

)

(1)

Here 𝑄̇ is the fire power [kW], 𝜌∞ is the ambient air density [kg/m3], 𝑐𝑝 is the ambient air specific heat capacity [kJ//kg/K], 𝑇∞ is the ambient temperature [K) and 𝑔 is the acceleration due to gravity [m/s2]. Coarse, medium and fine grid resolutions, corresponding to values of 𝐷 ∗ /4 , 𝐷 ∗ /10 and 𝐷 ∗ /16 respectively, were used following the approach used in the extensive validation of FDS performed by the U.S. NRC [9]. This resulted in mesh cell spacing of approximately 36 cm, 14.5 cm and 9 cm. Turbulence was modelled using the default FDS formulation of the LES model with Deardorff turbulent eddy viscosity. The gray-gas radiation model, solved using the finite volume method, was also used [10]. The thermal properties of the rock-wool insulation and the concrete used in the DIVA facility were specified in the model in accordance with values determined by IRSN. For the rockwool insulation, the following material properties were used: thermal conductivity 0.102 W/m/K, specific heat capacity 840 J/kg/K, emissivity 0.95 and density 140 kg/m3. The thermal properties used for the concrete were: thermal conductivity 1.5 W/m/K, specific heat capacity 736 J/kg/K, emissivity 0.7 and density 2430 kg/m3 [1]. Fire Source and Combustion Modelling Thermal boundary conditions for the simulations of the INTEGRAL-4 test were specified following experimentally-determined fire characteristics. The measured fuel mass loss rate was used in FDS with the fire boundary condition imposed as a time-varying mass flux of fuel. The heat of combustion was specified as 42 MJ/kg in the model, following the value given in Audouin et al. [11]. The pool fire source used in the INTEGRAL-4 test was modelled as a horizontal 1 m x 1 m surface in FDS. Three approaches to modelling the pool fire combustion were used. Sensitivity of the model predictions to the specified combustion process was assessed. The first approach comprised a single-step, infinitely-fast combustion reaction with fixed soot and carbon monoxide yields of 0.042 kg/kg and 0.012 kg/kg, respectively. This is the default combustion model used in FDS, and widely used in other fire models. The resulting reaction mechanism is described by Eq. (2). 𝐶12 𝐻26 + 17.86857 𝑂2 → 13.0 𝐻2 𝑂 + 0.07286 𝐶𝑂 + 11.33214 𝐶𝑂2 + 0.595 𝑠𝑜𝑜𝑡

(2)

The second approach was an extension of the single-step reaction mechanism to incorporate a carbon monoxide oxidation step. The same soot yield as for the single-step reaction was used. This approach aims to allow additional CO formation if compartment conditions allow, thereby capturing effects of under-ventilation on species formation. The resulting combustion process is described by the following two-step reaction: 𝐶12 𝐻26 + 12.2025 𝑂2 → 13.0 𝐻2 𝑂 + 11.405 𝐶𝑂 + 0.595 𝑠𝑜𝑜𝑡 𝐶𝑂 + 0.5 𝑂2 → 𝐶𝑂2

(3)

The third approach uses a modelling approximation which aims to independently capture CO production in the well-ventilated and under-ventilated regimes. For this approach the total CO produced through combustion is defined to consist of two separate species, CO a and COb, both of which are produced during a fuel oxidation step. The CO contribution from the well-ventilated regime, COa, is produced with a fixed yield and does not undergo any further reaction step. The CO contribution due to effects of under-ventilation, COb, may oxidise to CO2, provided sufficient oxygen is present. The total CO is then defined by the sum of the two constituent species, COa and COb, using the same soot and total CO yields as specified

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in the single-step reaction case. This modified two-step combustion process is described by Eq. (4) and follows the approach introduced by Floyd and McGrattan [12]. 𝐶12 𝐻26 + 12.2025 𝑂2 → 13.0 𝐻2 𝑂 + 0.07286 𝐶𝑂𝑎 + 11.33214 𝐶𝑂𝑏 + 0.595 𝑠𝑜𝑜𝑡 𝐶𝑂𝑏 + 0.5 𝑂2 → 𝐶𝑂2 𝐶𝑂 = 𝐶𝑂𝑎 + 𝐶𝑂𝑏

(4)

Modelling the mechanical ventilation One of the principle aims of the PRISME tests was to investigate the interaction between fire-induced compartment conditions and the mechanical ventilation system. Compartment pressure significantly influences ventilation conditions, which can lead to loss of containment and spread of gaseous combustion products and smoke. An advantage of FDS 6 over previous versions of the model is the inclusion of an HVAC network solver to couple the CFD model with complex mechanical ventilation boundary conditions. The solver computes the flow through a network described by a collection of nodes and interconnected ducts. Each node must be a connection between one of the following: multiple ducts; the HVAC network and the CFD domain; or the network and ambient atmosphere. Ventilation components, such as fans and dampers, can also be incorporated in the HVAC network model.

Figure 3

Schematic of the HVAC network: IRSN (left); FDS model setup (right)

In the FDS setup for the PRISME Integral fire scenario modelled here, the HVAC network has been defined as a simplification of the network at the IRSN experimental facility with 6

ducts combined where possible to minimise the number required. Figure 3 shows a comparison of the complete network (left) and the simplified version incorporated into the modelling (right). In the model it has been assumed that the nodes inside the JUPITER compartment can be considered ambient, since it is not feasible to model the DIVA facility within the JUPITER enclosure. Nodal pressure and duct flow rate measurements, taken prior to ignition, have been used to calculate total loss coefficients for each of the modelled ducts using the following formula [3]: 𝑘=

2∆𝑝 𝜌∞ 𝑢2

(5)

Here 𝑘 is the total loss coefficient for the duct, ∆𝑝 (Pa) is the pressure drop across the duct, 𝜌∞ (m3/s) is the ambient air density and 𝑢 (m/s) is the velocity inside the duct (calculated from the volumetric flow rate and duct cross-sectional area). Where ducts were combined in the model, the average cross-sectional area of the combined ducts was used. Where there are discrepancies in the measured flow rates, for example where the inflow and outflow through a duct do not match, the ‘lost’ mass has been directed to an ambient node, since the FDS HVAC model does not allow mass storage inside the ducts. Furthermore, fixed flow rate fans have been used at the inlet and outlet branches of the ventilation network as the fans are considered to be sufficiently far along the network so as to be unaffected by the pressure inside DIVA. This is aided by the two significant bypasses in the system through the JUPITER compartment (N9 and N9a) and to open atmosphere (N25 and N26). Sensitivity Analyses To assess the sensitivity of the model to the choice of mesh resolution, model predictions of key quantities of interest have been compared for the coarse, medium and fine grids as defined previously. Figure 4 to Figure 6 compare model predictions of the HRR, temperature and oxygen concentration in the fire room for the single-step (left) and two-step (right) reaction mechanisms with the experimental data. Comparison of the medium and fine mesh results from these figures shows that reasonable grid independence is achieved for both of the combustion models.

Figure 4

INTEGRAL-4 grid sensitivity results in comparison to the experimental HRR for the single-step (left) and two-step (right) combustion modelling approaches

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Figure 5

INTEGRAL-4 grid sensitivity results in comparison to the experimental temperature profile at a height of 3.9 m at the NE corner of the fire room (L2)

Figure 6

INTEGRAL-4 grid sensitivity results in comparison to the experimental O2 concentration profile at the upper sensor in the fire room (L2)

Sensitivity of the model predictions to the combustion model used in the simulations was also examined. Figure 4 to Figure 6 clearly illustrate the difference in behaviour of one- and two-step combustion modelling approaches. The differences are most evident in the latter stage of the fire where the single-step reaction mechanism gives poorer agreement with the experimental data than the two-step model. It is clear that the second measured HRR peak at 1200 s to 1500 s is not captured when a single-step combustion reaction is used. However, with a two-step reaction mechanism, the model captures this second HRR peak well. Figure 5 and Figure 6 show that there is a second peak in fire room temperature and a large drop in O2 concentration, which correspond to the second HRR peak. The figures illustrate that the two-step combustion model captures these features more accurately than the singlestep approach. Whilst the two-step combustion modelling approach has been shown to outperform the single-step model in terms of capturing the measured HRR, fire room temperature and O 2 concentration, the two-step model does not accurately reproduce the CO concentrations measured during the INTEGRAL-4 test. As such, a modified two-step combustion model [12], as described previously, has been used in an attempt to capture the influence of underventilated compartment conditions on CO production. This model combines features of the single-step and two-step combustion models to improve predictions of the CO concentration. For the quantities presented in Figure 4 to Figure 6, this model gives results which are negligibly different to those for the two-step approach, thus figures showing these results have not been included here. This hybrid combination of combustion models has been used to produce the INTEGRAL-4 simulation results presented throughout the remainder of this paper.

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RESULTS In this section, FDS 6.4.0 results are presented in comparison to measured data taken from the PRISME INTEGRAL-4 experiment. The ability of the model to predict compartment pressure, ventilation flow rates, compartment temperature profiles and concentrations of combustion products is assessed. HVAC Network Compartment pressure and ventilation conditions in the HVAC network govern compartment fire behaviour to a large extent. As such it is important that the initial conditions are closely matched to the experimental data. For the simulations of the INTEGRAL-4 test the predicted initial static pressures at the HVAC network nodes were within 5.5 % of the measured values at all of the measurement locations. As such, it is clear that the initial HVAC network pressure conditions are generally well captured by the model. INTEGRAL-4 HTP Pool Fire The following simulation results presented for the INTEGRAL-4 scenario are based on a model setup using the modified two-step combustion approach [12], as previously described, with a mesh resolution of approximately 14.5 cm, corresponding to the medium mesh used during the grid sensitivity analysis. Comparisons of model predictions with the experimental data are shown in the subsequent figures for each of the three compartments and the corridor of the DIVA facility as used for the INTEGRAL-4 test. Figure 7 to Figure 17 show that FDS version 6.4.0 can be used to reproduce transient compartment conditions for a confined, mechanically-ventilated multi-compartment fire scenario. These figures show that profiles of temperature, species concentration, doorway velocity, pressure and HVAC flow rate are, in general, qualitatively captured by the model for the INTEGRAL-4 scenario. Figure 9 shows that FDS version 6.4.0 captures the variation in fire compartment pressure and the ventilation flow rates well. This is in part due to the inclusion of the HVAC network model resulting in more accurate ventilation boundary conditions, and in part due to the fact that the modified two-step combustion model better predicts the fire behaviour with regards to the HRR. As a result, the FDS version 6.4.0 results obtained here show better agreement with the experimental data than the results presented by Beji et al [6] in which a simpler representation of the mechanical ventilation and a standard single-step combustion model were used.

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Figure 7

Comparison of measured and predicted temperature in the fire compartment (L2) on the NE (top left), SW (top right), CW (bottom) thermocouple trees

Figure 8

Comparison of measured and predicted species concentrations in the fire compartment (L2): O2 (top left), CO2 (top right) and CO (bottom)

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Figure 9

Comparison of measured and predicted ventilation conditions: fire room (L2) pressure (top left), L0 inlet flow rate (top right), L1 inlet flow rate (bottom left), compartment L3 exhaust flow rate (bottom right)

Figure 10

Comparison of measured and predicted temperatures in the corridor (L0) at the CC (top left), CE (top right) and CW (bottom) thermocouple trees

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Figure 11

Comparison of measured and predicted species concentrations in the corridor (L0): O2 (top left), CO2 (top right) and CO (bottom)

Figure 12

Comparison of measured and predicted temperatures in compartment L1 at the CC (top left), CW (top right) and SE (bottom) thermocouple trees

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Figure 13

Comparison of measured and predicted species concentrations in compartment L1: O2 (top left), CO2 (top right) and CO (bottom)

Figure 14

Comparison of measured and predicted temperatures in compartment L3 at the CC (top left), CE (top right) and SW (bottom) thermocouple trees

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Figure 15

Comparison of measured and predicted species concentrations in compartment L3: O2 (top left), CO2 (top right) and CO (bottom)

Figure 16

Comparison of measured and predicted doorway velocity profiles: L0 – L2 doorway (top left), L1 – L2 doorway (top right) and L2 – L3 doorway (bottom)

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Figure 17

Comparison of measured and predicted doorway temperature profiles: L0 – L2 doorway (top left), L1 – L2 doorway (top right) and L2 – L3 doorway (bottom)

To quantify differences between the model predictions and the experimental results a functional analysis approach, as proposed by Peacock et al. [13], is used. The following metrics [13, 14] are used here to compare temporally-varying quantities:  

Cosine: taken as the inner product cosine between two datasets, Relative difference: taken as the normalised Euclidean distance between two datasets.

This approach also formed the basis of model comparison to experimental data for the first PRISME benchmark exercise [11], which looked at tests involving single effects, rather than the more complex Integral tests. The functional analysis cosine values [11] and [13] further illustrate that the profiles predicted by the model capture the observed behaviour. For each compared variable these show how closely the functional form of the model prediction is to that of the measured data. Values approaching unity indicate that the shape of the two curves differ by a constant multiplier [13]. The cosine values for the FDS predictions of the INTEGRAL-4 test are summarised in Error! Reference source not found.. The cosine values listed in Table 1 illustrate that the model predictions for CO concentrations show the largest deviation from the experimental data in terms of the shape of the predicted profiles. This is particularly evident in the fire room, L2. Figures 8, 11, 13 and 15 show comparisons of the measured and predicted CO profiles in the fire room (L2), corridor (L0), room L1 and room L3, respectively. The model predictions for the fire room show the largest deviation from the measured CO concentrations. In this room there is a significant difference in CO concentration in the upper and lower portions of the compartment. In the other compartments, where the upper and lower CO concentrations are similar, the model performs well. This result highlights a limitation of the FDS combustion model, and the modelling ap-

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proximation used here to account for the effects of under-ventilation. It is clear that FDS is not yet capable of reliably capturing the effects of under-ventilation on rates of CO production.

Table 1

Cosine values [13] for FDS version 6.4.0 predictions of the PRISME INTEGRAL-4 test Measured Quantity

Range *

Temperature

0.98 – 1.0

O2 concentration

0.97 – 1.0

CO2 concentration

0.95 – 1.0

CO concentration

0.57 – 0.96

Pressure

0.91

HVAC flow rates

0.99 – 1.0

* No cosine range for pressure as measurements were only taken in one of the compartments

Figure 18 shows the relative difference [13] between the predicted and measured compartment temperature profiles. The figure compares relative difference values for the upper and lower measurement locations for each of the compartments and doorways. From this figure it is clear that the quantitative performance of FDS is significantly better for predictions of upper layer temperatures than those in the lower gas layer.

Figure 18

Comparison of the relative difference between predicted and measured temperature profiles for the upper and lower gas layers in the fire room L2 (bars 1-3); corridor L0 (bars 4-6); inlet room L1 (bars 7-9); exhaust room L3 (bars 10-12) and the three doorways (bars 13-15).

CONCLUSIONS FDS 6.4.0 has been used to conduct numerical simulations of the PRISME INTEGRAL-4 fire test. The test considered a confined, mechanically-ventilated, multi-compartment scenario with an HTP pool fire as the source. The ability of the model to capture the fire-induced compartment conditions and the interaction between the fire and the mechanical ventilation 16

system has been assessed through comparison of model predictions with the experimental results. The INTEGRAL-4 simulation results show that FDS 6 is capable of capturing the fire-induced compartment conditions and the interaction between the fire and the mechanical ventilation system with reasonable accuracy for the majority of the parameters of interest. However, it has been shown that there are limitations in the available combustion modelling approaches. This inhibits the model’s ability to predict concentrations of gaseous combustion products, most notably CO, accurately in the under-ventilated compartment conditions observed during the experiment. It is clear from the sensitivity analysis simulations conducted for the INTEGRAL-4 test that the choice of combustion modelling approach can have a large impact on the ability of the model to accurately reproduce qualitative fire behaviour. The results presented here indicate that model sensitivity to the choice of combustion model should be assessed for scenarios for which under-ventilated conditions are anticipated. Comparison of the results presented in this paper with the work of Beji et al. [6], in which an earlier version of FDS was used, shows that the inclusion of coupled HVAC network and CFD solvers in FDS v6 results in improved model predictions for the scenario considered. The updated version of FDS gives model predictions which more closely match the measured compartment pressure and ventilation flow rates. The results presented here show that for well-defined fire sources FDS can provide reasonable predictions of fire consequences. For scenarios involving complex fire sources there will be significant uncertainty in both the specification of the source and representation of the source in the model. For such scenarios, carefully-chosen simplified fire sources could be used, such as standard design fire curves. Additional work is required to develop combustion modelling approaches which can adequately capture the influence of under-ventilated conditions on fire behaviour and combustion product yields. ACKNOWLEDGMENTS AND DISCLAIMER This publication was co-funded by the Office for Nuclear Regulation (ONR) and the Health and Safety Executive (HSE). The contents of the publication, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect ONR or HSE policy. REFERENCES [1]

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Audouin, L., et al.: OECD PRISME Project: fires in confined and ventilated nucleartype multi-compartments – overview and main experimental results, Fire Safety Journal 62, pp. 80-101, DOI: 10.1016/j.firesaf.2013.07.008, 2013. McGrattan, K., et al.: Fire Dynamics Simulator User’s Guide, NIST Special Publication 1019, Sixth Edition, DOI: 10.6028/NIST.SP.1019, Gaithersburg, MD, USA, August 2016. Floyd, J.: Coupling a network HVAC model to a computational fluid dynamics model using large eddy simulation, Fire Safety Science 10, pp. 459-471, DOI: 10.3801/IAFSS.FSS.10-459, 2011. Beji, T., J., Degroote, B. Merci: Parametric numerical analysis of fire-induced pressure variations in a well-confined and mechanically ventilated compartment, 6th European

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