Development of Fire Consequence Prediction Model in Fuel Gas Supply System Room with Changes in Operating Conditions during Liquefied Natural Gas Bunkering

Abstract

Recently, various dynamic risk analysis methods have been suggested for estimating the risk index by predicting the possibility of accidents and damage in real time. It is necessary to quickly estimate the risk of an accident by predicting the probability and consequences of accidents, which are quantitative criteria for ship risk assessment. This study aimed to develop an algorithm for predicting the consequences of accidents in real time to perform a dynamic risk assessment and, using this algorithm, formulate a ship accident–response procedure that can be used in fire accidents during LNG bunkering. The risk of fire was estimated by predicting the hole size due to changes in the liquefied natural gas (LNG) transport conditions and the consequence based on the fire accidents scenario in LNG-fueled ships. The prediction model was trained with the hole prediction data using Ansys CFX and with the consequence data using DNV Phast, and the consequences of fire were compared and evaluated by applying the trained results. A method for estimating the size of the fire based on the predicted consequence is proposed, which supports fast decision making in fire accidents during LNG bunkering by identifying the potential size of the fire at the beginning of an accident.

Keywords

• LNG bunkering
• deep learning
• fire consequence
• hole diameter
• accident consequence
• support for decision making