The Effect of Model Parameters on the Simulation of Fire Dynamics

Abstract

The sensitivity of computer fire modelling using results from NIST's Fire Dynamics Simulator (FDS) to a set of input parameters related to fire growth has been analyzed. The scenario simulated is the real-scale Dalmarnock Fire Test One and the modelling results are compared to the measurements. Fire size and location, convection, radiation and combustion parameters were varied in order to determine the associated degree of sensitivity. Emphasis is put in the prediction of secondary ignition and time to flashover. In this context and while keeping the HRR constant, simulations of fire growth are significantly sensitive to location of the heat release rate (HRR), fire area, flame radiative fraction, and material thermal and ignition properties. The simulations are relatively insensitive to the heat of combustion (while keeping the HRR constant), the soot yield and the heating from the smoke layer. The results indicate that the future development of successful fire forecast methodologies of fire growth using CFD must focus on the global HRR as well as the important parameters identified here. KEYWORDS: FDS, prediction, Dalmarnock, modelling, forecast NOMENCLATURE LISTINGHeat of combustion (kJ/kg)

Forecasting fire dynamics using inverse computational fluid dynamics and tangent linearisation

“…This is typically coupled with a mixture fraction model for combustion. Smoke movement and temperature distributions can only be reproduced reasonably well if the curve of HRR is known 4,20,21 . Fire modelling typically handles compartment length scales of around 10 m. Although depending on the size of the fire, with a large eddy simulation (LES) approach a grid resolution between 1 cm and 10 cm in the gas phase is generally required to obtain sufficient spatial resolution 19 .…”

“…20 is thus not necessarily a contradiction. This is the result of a different smoke flow pattern in the forward model than in the true fire due to the different fire areas which affects entrainment and the smoke plume dynamics 20 . Using sensors that are not only in the ceiling jet region would improve the forecast.…”

Forecasting Fire Growth using an Inverse CFD Modelling Approach in a Real-Scale Fire Test

“…[2,6], is thus implemented, estimating the spread rate of the fire on the sofa. Other parameters necessary for the simulation of a complex fire scenario such as Dalmarnock Test One were taken from previous analysis [2,7] and are assumed to be known. The sensitivity of these parameters is discussed in detail in Ref.…”

Using Computational Fluid Dynamics in the forensic analysis of a prison fire

“…The inherent difficulty of simulating fire spread and the resulting growth rate of the fire using a CFD based fire model such as FDSv5 with a relatively coarse grid, also limits its capability of predicting the transition from a preflashover to a post-flashover fire [22]. However, the model can give indications as to the time when flashover will occur.…”

“…It is therefore recommended to implement the HRR as a prescribed function of time, obtained from laboratory experiments [21]. If other pertinent parameters are correctly set, FDSv5 will then adequately compute the movement of the smoke impelled by the fire, yielding flow field and temperature distributions within the smoke [22].…”