A Machine Learning-Assisted CFAST Simulation for Improved Fire and Evacuation Modeling in Ventilated Main Control Room

Accurately predicting evacuation time in a ventilated main control room (MCR) during fire emergencies is crucial for ensuring the safety of personnel at nuclear power plants. This study proposes an approach for integrating consolidated fire and smoke transport (CFAST) simulations with a neural network, one of the machine learning models, to predict evacuation time in MCRs. The CFAST model simulates fire and evacuation scenarios in a ventilated MCR with variations in input parameters such as door conditions, ventilation flow rate, leakage area, and fire propagation time. Target output parameters, such as hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD), are used alongside standardized evacuation variables to train a machine learning model for predicting evacuation time. The findings suggest that high ventilation flow rates help to dilute smoke and discharge hot gas, leading to lower target output parameters and quicker evacuation. Standardized evacuation variables exceed the required abandonment criteria for all door conditions, indicating the importance of proper evacuation procedures. The results show that the neural networks produce accurate predictions of evacuation times, which can be beneficial for emergency planning and decision-making.