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FDS Application Details

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Introduction

Fire Dynamics Simulator (FDS) is a model developed by National Institute for Standard and Technology (NIST, USA) that simulates fire and predicts its effects; in the package is included Smokeview, a graphical post-processor useful for FDS results analysis. FDS solves numerically a form of Navier-Stokes equations appropriate for low-speed, thermallydriven flow with an emphasis on smoke and heat transport of fire. FDS allows modelling of fire starting from a database of standard materials distributed with the program that can be modified by the user to introduce new materials, defined with their chemical and physical characteristics and with data obtained from experimental fire tests. Dynamics of fire is simulated through the parameters that characterize each material present in the simulation domain, each one with its characteristics of in-flammability and combustion or its reaction to fire. FDS can calculate and return as output, once the simulation has been set-up to effectively calculate the quantities of interest, all the values of the variables, both scalar and vectorial, computed in every cell of the domain and useful to understand the fire and to analyse its effects (concentrations of chemical species, distributions of temperature / pressures / gas velocities / smokes, visibility, ...). FDS can be used to model the following phenomena: Low speed transport of heat and combustion products (mainly smoke) from fire; Heat transfer between the gas and solid surfaces; Pyrolysis; Fire growth; Flame spread; Activation of sprinklers and heat detectors; Fire suppression by sprinklers. FDS is widely used by fire safety professionals. One of the applications is a simulation of fire and smoke transfer in human structures, for example for design of smoke control systems and sprinkler activation studies. FDS was also used in numerous fire reconstructions including the investigation into the World Trade Centre disaster.

FDS simulations with a real scenario represent long-time, computational intensive and memory consuming jobs. In order to find the programming model exhibiting the best performance and producing authentic true results for a given fire scenario, a great number of experiments, i.e. simulation runs with various input parameters, need to be fulfilled.

Unsuppressed tunnel fires involving Heavy Goods Vehicles (HGVs) can be extremely large. The simulation of an unsuppressed fire, which could not be conducted in the tunnel, provided a means of comparing conditions in a tunnel fire with and without an active suppression system. The tunnel fire tests (described in literature) showed that very large fires (20 to 60 MW) involving HGV fuel loads create highly turbulent conditions that vary from test to test, even for apparently similar test conditions.

More information:

http://www.cfd-online.com/Wiki/FDS

http://fire.nist.gov/fds

Fire Scenario

In order to keep the computational domain within reasonable limits, only the 180m long test tunnel was included in the simulation (in the first phase). Basic layout of instrumentation in the test tunnel: We have constructed a two-lane road tunnel model with dimensions 10m x 180m x 7m with two fans located on the tunnel ceiling at the distance 50m and 140m from the left entrance of the tunnel. The fire source in the simulation was represented by burning of a flammable liquid in a pool with dimensions 2 x 3m placed in the distance 92m from the left entrance of the tunnel, 1.1m above the floor level. During the simulation, the fire did not spread along the tunnel; no other flammable obstacles were included in the simulation. The total duration of the simulation was 150s. The initial air temperature in the whole tunnel was set to 20°C. Various control devices were installed inside the tunnel in order to record mean values of gas phase quantities (soot volume fraction, visibility, temperature and carbon monoxide mass fraction) inside small testing cube-like volumes placed under the ceiling of the tunnel and at the level of human head The slices of gas temperatures, oxygen and carbon monoxide mass fractions were also recorded for several planes. The wall temperature of the tunnel ceiling was detected above the fire.

Input data required to run the model

All of the input parameters required by FDS to describe a particular scenario are conveyed via one or two text files created by the user. These files contain information about the numerical grid, ambient environment, geometry of the problem modelled, material properties, boundary conditions and the fire itself. The input file should also contain information about the desired output quantities. All blockages in the FDS model have to conform to the computational mesh. For this reason, it is usually not possible for the FDS model geometry to be exactly the same as that of the physical model (unless a very fine mesh is used). The irregular edge appearance could have been reduced by selecting a smaller cell dimension, but with a resulting rise in the number of cells and, hence, increased computational time. The grid cells can either be uniform in size (default mode) or they can be stretched in one or two of the three coordinate directions. It is possible to use more than one rectangular mesh in a calculation. This allows creation of an efficient computational domain for geometries which cannot be easily fitted into a single rectangular grid. It also allows using regions with different grid resolutions within one computational domain. Both the grid stretching and the use of multiple meshes allow the user to apply better grid resolutions in critical areas (e.g. near the fire) without unnecessarily increasing the demand for computational power by applying fine mesh to the entire computational domain. The use of multiple meshes is also required when an FDS simulation is to be run in parallel processing on more than one computer. The parallelization requires a decomposition of computational domain into computational meshes, which affect the simulation outputs. In order to use MPI version of FDS, computational domain must be decomposed into meshes and each one of them can be assigned to specific MPI process. Velocity values at the mesh boundaries are then averaged in order to maintain stability. FDS models: FDS source code has been designed to provide 4 programming models: 1. sequential for a single CPU, 2. MPI parallel – applied on distributed memory systems: