Computational fluid dynamics modelling of hydrocarbon fires in open environments: Literature review

Abstract

Hydrocarbon fuels are involved in most major fire accidents occurring in industrial facilities. Due to the need for an in-depth understanding of the phenomena associated with hydrocarbon fires, computational fluid dynamics (CFD) modelling has been widely employed in the field of fire risk analysis over the last decades. The aim of the present review is to provide the reader with a comprehensive compilation and discussion of the most important aspects involving CFD modelling to simulate hydrocarbon fires in open environments. The fire sizes simulated, the fuels used, the codes employed, the variables of interest measured, the simulation purposes and the results accuracy have been examined through a wide literature survey, which includes peerreviewed journals and congress papers dating from the 90s until now.

Thermokinetic study of macadamia carpel pyrolysis using thermogravimetric analysis

“…The theoretical plots closest matching with the experimental plot were identified by the minimum RMSE calculated. [50] In the / from 0.15-0.50, the experimental master plot of macadamia carpel followed an R1 (unidirectional contraction) and D1 (one-dimensional diffusion) model. The geometrical contraction models (R) assume that the nucleation occurs rapidly on the solid surface, where the degradation initiates at random points that act as centres of the reaction propagation.…”

“…À Á 0:5 , where / = 0.5 is considered as the reference point. [22] The mechanism's corresponding parts were determined by root mean square error (RMSE), wherein the minimum error represents the predominant mechanism curve in Equation ( 44) [50] :…”

Optimization of firefighting strategies in process plants with emphasis on domino effects and safe evacuation

“…Considering domino effects as the most resourcedemanding event caused by fire in process plants, many studies have been devoted to modelling and risk assessment of domino effects. [6][7][8][9][10][11][12][13][14][15][16] However, work conducted on optimal firefighting and its role in preventing and reducing the risk of domino effects has been limited. [5,[17][18][19][20][21] Khakzad combined domino effect models with decisionmaking and optimization techniques such as dynamic influence diagram (an extension of dynamic Bayesian network [BN]), [5] mathematical programing, [5] and information theory, [20] to find optimal firefighting strategies.…”

Numerical prediction of fire dynamics and the safety zone in large‐scale multiple pool fire in a dike using flamelet model

“…The RANS approach focuses on solving the Navier–Stokes equations for the average flow variables and calculates the large‐scale motions exclusively. [ 32 ] On the other hand, the direct numerical simulation (DNS) approach aims to solve the equations in their full complexity, capturing eddies across all temporal and spatial scales. As a result, DNS requires significant computational power.…”

“…[1,24] These correlations are restricted to the small-scale MPF (less than 1 m in size). [1,24] To overcome these limitations, several computational fluid dynamics (CFD) studies [25][26][27][28][29][30][31][32][33] have been carried out to predict the burning characteristics such as flame temperature, flame height, and O 2 and CO 2 mass fraction profiles of pool fires. The CFD modelling of pool fires has been carried out using two different approaches including flamelet generated manifold (FGM) [34][35][36] and species transport model.…”