Evaluation of CFD simulations of transient pool fire burning rates

Abstract

Fire is the most commonly occurring major accident hazard in the chemical and process industries, with industry accident statistics highlighting the liquid pool fire as the most frequent fire event. Modelling of such phenomena feeds heavily into industry risk assessment and consequence analyses. Traditional simple empirical equations cannot account for the full range of factors influencing pool fire behaviour or increasingly complex plant design. The use of Computational Fluid Dynamics (CFD) modelling enables a greater understanding of pool fire behaviour to be gained numerically and provides the capability to deal with complex scenarios.This paper presents an evaluation of the Fire Dynamics Simulator (FDS) for predictive modelling of liquid pool fire burning rates. Specifically, the work examines the ability of the model to predict temporal variations in the burning rate of open atmosphere pool fires. Fires ranging from 0.4 to 4 m in diameter, involving ethanol and a range of liquid hydrocarbons as fuels, are considered and comparisons of predicted fuel mass loss rates are compared to experimental measurements.The results show that the liquid pyrolysis sub-model in FDS gives consistent model performance for fully predictive modelling of liquid pool fire burning rates, particularly during quasi-steady burning. However, the model falls short of predicting the subtleties associated with each phase of the transient burning process, failing to reliably predict fuel mass loss rates during fire growth and extinction. The results suggest a range of model modifications which could lead to improved prediction of the transient fire growth and extinction phases of burning for liquid pool fires, specifically, investigation of: ignition modelling techniques for high boiling temperature liquid fuels; a combustion regime combining both infinite and finite-rate chemistry; a solution method which accounts for two-or three-dimensional heat conduction effects in the liquid-phase; alternative surrogate fuel compositions for multi-component hydrocarbon fuels; and modification of the solution procedure used at the liquid-gas interface during fire extinction.

Modeling and risk analysis of large‐scale crude oil pool fire on an offshore facility

“…In Section 3.2, the distribution of the injury areas above 180°C has been elaborated for 18 scenarios. Another injury criteria based on the probability of injury or death, and the probabilities of having first‐degree burn, second‐degree burn, and death are calculated based on the TDU (D) using Equations and 3–28,32–34 . Exactly 50% fatality and 100% fatality are calculated and compared with the above injury criteria where the probit variable is adopted to transform the TDU to the probability of injury or death represented as .…”

“…Since FDS software is validated to be suitable for modeling large pool fires, 11,12,21 FDS 6.7.7 is applied in this study for modeling large‐scale crude oil pool fire on an offshore platform. The overall framework is shown in Figure 1.…”

A study of fire and plume dynamics for static pool fires and their interaction with vegetation

“…The difference between the experimental and predicted mass burning rate is due to (1) the difference in the ignition process, (2) the difference in fuel properties used in the experiments and in the model (as reported in Stewart et al, 2021), (3) the neglection of internal convection within the pool in the model, while it can be quite the tree, a significant portion of the plume with sufficiently high velocity passes through the tree. Then, the flow velocity reduces due to drag by the fuel particles.…”

“…Several works have been reported earlier on small scale pool fires (Prasad et al, 1999;Attar et al, 2013;Wu et al, 2020), which are typically less than 0.3 m in diameter. However, only limited research has been conducted on medium and large-scale pool fires (Wen et al, 2007;Sudheer et al, 2013;Xin et al, 2005;Wahlqvist et al, 2016;Stewart et al, 2021). These fires fall either in the transition or turbulent regime and are dominated by radiative heat transfer.…”

“…Wahlqvist & van Hees (2016) used FDS to predict the mass loss rate of heptane pool fires with the inclusion of heat feedback from external sources, such as walls, and the oxygen concentration near the pool. Stewart et al (2021) numerically investigated the ability of FDS to predict medium and large-scale open pool fires of several hydrocarbon fuels including gasoline and diesel. Results showed that FDS predicted the three-step transient burning pattern of: fire growth, quasi-steady burning, and extinction, as observed in the experiments.…”