Predictive Modelling of Wind-Influenced Dynamic Fire Spread Probability in Tank Farm Due to Domino Effect by Integrating Numerical Simulation with ANN

Abstract

Pool fires cause immense damage to fuel storage tank farms. Reduced fire escalation risk in tank farms improves fire safety. Computational fluid dynamics (CFD) has proven effective in assessing escalation of fire-related domino effects and is being utilized for pool fire consequences in tank farms. The past CFD-based analysis focused on primary fire effects on secondary targets. This study used fire dynamics simulator (FDS) to model complete evolution of the domino effect under different wind speeds and primary pool fire locations. Dynamic escalation probability (DEP) and fire spread probability of the tank farm were calculated. Offset tank failure increased by 3% and 31%, while inline tank failure dropped by 36% and 90%, at 2 and 8 m/s, respectively. An artificial neural network (ANN) incorporating the Levenberg–Marquardt algorithm is used to predict fire spread probability based on numerical data set. The use of ANNs for this purpose is one of the first attempts in this regard. ANNs can reliably predict dynamic fire spread probability and could be utilized to manage fire-induced domino effects. Moreover, dynamic fire spread probability in tank farms obtained from ANN modelling can be used for safety applications, such as updating mitigation time when fire spread probability is unacceptable for a specific wind speed.