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Fire modelling is used increasingly to enable fire-safety engineering solutions to meet performance-based regulatory requirements. There are many fire models, but perhaps the most useful are those based on computational fluid dynamics, or CFD (see also Digest DG 367[1]). Such tools in competent or expert hands are extremely powerful, but can be misused by those with limited knowledge of fire science and numerical modelling. Presentation of CFD results (often using video animation) can be very convincing, but the results must be assessed with some knowledge of the principles and awareness of any shortcomings of the model. Currently, CFD models employ either Reynolds-averaged NavierStokes (RANS) or large eddy simulation (LES) methodologies for application to practical fire problems.This Digest offers guidance on CFD models using the RANS methodology and on how to avoid some of the common pitfalls. No specific guidance is offered on LES models, but much of what is recommended here will also apply to them.INTRODUCTIONThe widespread application of computer fire models has grown as a result of the freedom to innovate offered by performance-based regulation[2]. Computer models enable designers to:test alternative fire-safety design solutions, quantify system performance specifications, andexplore solutions to what if? design questions.These processes are assisted by new standards, such as BS 7974:2001[3] and BS ISO/TR 13387[4]. Many computer fire models are available, ranging from simple engineering correlations and zone models to more advanced field (CFD) models. These models simulate the heat- and mass-transfer processes associated with a compartment fire, the heart of any systematic approach to safe design. The essential difference between zone and field models is in the way they treat the movement of the products of combustion within the building envelope and their respective reliance on experimental information.The two broad categories are illustrated in Figure 2, which shows the schematic representation of the same room fire-modelled by the two approaches. By solving the problem on a three-dimensional numerical grid (Figure 2b), not only are the predictions much more detailed, but an isosurface of the predicted temperature clearly shows that the fire plume becomes deflected by the air inflow at the doorway. This is important because it leads to higher entrainment rates of air into the fire and consequently higher smoke-production rates. The zone model does not account for this, unless it has been assumed a priori.DIGEST DG 511FIRE MODELLING WITH COMPUTATIONAL FLUID DYNAMICSSuresh KumarFigure 1: CFD modelling of fire inside a building illustrating smoke flows and thermal impact on a ceiling structural beam2Zone models are closely related to well-established, traditional methods for the treatment of smoke movement, which were initiated before the widespread availability of the modern computer. They were first suggested in guidance on roof venting and still form the basis of much current advice on smoke control (see, for example, Digest DG 396[5]). These methods use simplifying assumptions about the physics of smoke movement suggested by experimental observation of fires in compartments.In contrast, simulations using CFD are able to predict, without prior assumptions, the behaviour of smoke flow from a known fire and enable smoke-control strategies to be assessed. Like their zonal counterparts, these models allow comparisons between the developing hazard and the time available for safe escape of the occupants. CFD models are now being used extensively, particularly for complex designs where the zonal methodology may not be valid. As an illustration, Figure 3 shows an application of CFD to evaluate smoke-ventilation design for an underground train station, involving the solution of the underlying equations on a numerical grid comprising millions of grid cells. FIRE MODELLING WITH COMPUTATIONAL FLUID DYNAMICS DG 511Figure 3: Simulation of smoke flow in smoke-ventilation design for an underground train station using the CFD program JASMINEFigure 2: Schematic representation of zone and field modelsa: Zone modelling b: Field (CFD) modellingCeiling layerOutflow or spill plumeFire plumeBurning objectOutflow or spill plumeCeiling layerFire plumeBurning object3CFD MODELSThe development of CFD models continues apace following the inexorable growth in computer power. Three distinct CFD modelling methodologies have emerged: Reynolds-averaged NavierStokes (RANS),large eddy simulation (LES), and direct numerical simulation (DNS). The methodologies differ primarily in their treatments of the effects of turbulence on the heat- and mass-transfer processes and on the chemical kinetics.Figure 4 illustrates the degree of resolution achieved by the three methodologies. The fluctuating gas velocity at a particular point in a growing fire is shown. It has a relatively slow underlying increase with time as the fire grows, but fluctuations of different wavelengths are present depending on the local turbulent eddies in the flow. The degree of resolution of the contributions made by the different wavelengths achievable by the different turbulent methodologies is illustrated. The DNS methodology resolves fluctuations of all wavelengths, and thus takes into account exactly their contribution to fire and smoke spread. The LES methodology simulates the fluctuations carrying large wavelengths and uses a (sub-grid) turbulence model for the fluctuations of smaller wavelengths. The RANS methodology uses a turbulence model for fluctuations of all the wavelengths. For example, in Figure 4, for the RANS methodology, the square root of the turbulent kinetic energy (k) denotes the turbulence contribution of the fluctuating velocity and is represented here by the spread (in the form of dotted lines) around its mean value (). A detailed account of the three turbulence modelling methodologies is given by Luo[6]. Below is a summary of their main features.RANS methodologyRANS is, at present, the most widely used methodology for practical application, both in fire-specific commercial software such as JASMINE[7] and SOFIE[8] and in general-purpose CFD programs such as PHOENICS, CFX, FLUENT and STAR-CD. Essentially, the RANS methodology views the transient evolution of local gas temperature, velocity or chemical species as comprising a time-averaged component and a fluctuating component about that average. It solves only the statistically time-averaged equations that describe the principles of mass, momentum, energy and species conservation. These equations are supplemented by further transport equations that encompass the effect of turbulence influences and encapsulate the whole of the turbulence spectrum from very large room-scale turbulent eddy sizes (a few metres or more) down to the very smallest scales (order of a millimetre) associated with the dissipation of energy by viscosity and the chemical reactions. The most commonly used turbulence model in RANS CFD programs for fire applications is the two-equation k turbulence model, where k is the kinetic energy of turbulence and is its dissipation rate.RANS-based CFD models predict the evolution over time of the statistically time-averaged properties of the fire at millions of locations throughout the enclosure of interest. FIRE MODELLING WITH COMPUTATIONAL FLUID DYNAMICS DG 511Figure 4: An illustration of the resolution of velocity as a function of time using the RANS, LES and DNS methodologiesTimeVelocityDNSLES RANS k4LES methodologyLES-based CFD models capture the fluctuating low-frequency, larger eddies, but need to model the smaller eddies using a sub-grid model, in a similar way to the two-equation turbulence model used in the RANS methodology. In contrast to RANS, which uses time averaging, LES uses spatial averaging also referred to as filtering because it smoothes out the information contained in eddies smaller than grid cell size. By adopting an averaging procedure used for smoothing out temporal fluctuations, LES-based CFD models can also provide predictions of time-averaged properties at each grid cell, for comparison with RANS predictions or with measurements (such as gas velocity and gas temperature). The fineness of the numerical mesh determines the size of those eddies that are resolvable and those that are not in the LES model. This needs to be chosen with great care since coarse meshes can give misleading results (see Best-practice guidance below). LES models use finer numerical meshes than RANS models, so need far more computer power, which is now readily available.LES is being increasingly adopted as computers become more powerful. One advantage is that it makes fewer assumptions about the role of turbulent mixing than the RANS approach. Another attraction of the LES methodology is the very visually appealing graphical animations that it generates. The FDS model, developed at the National Institute of Standards and Technologys (NIST) Information Technology Laboratory in the US, is being used increasingly by fire safety practitioners[9].Figure 5 illustrates a typical graphical output of the simulation of a hydrogen jet release in the atmosphere from the RANS and LES methodologies. It can be seen that the LES methodology reproduces only the large-scale structure but filters out small-scale flow details, whereas the RANS methodology reproduces the integrated flow behaviour by time-averaging fluctuations of all scales.On the other hand, as explained in the next section, the DNS methodology reproduces the detailed flow field structure by exactly simulating the fluctuations of all turbulent properties (eg velocities, temperature, density, combustion species). However, since the DNS methodology is not yet ready to be exploited for practical application, its results are not shown here. DNS methodologyThe DNS methodology has a much higher degree of resolution and is potentially more a