Real-time forecasting of fire in a two-story building using ensemble Kalman filter method

Abstract

Accurate and reliable prediction of smoke development process is critical for building fire protection design. Although modern computers allow us to use sophisticated models, e.g. CFD, to simulate certain fire scenarios, the simulation time is often drastically longer than zone models. This paper applies a real-time forecasting method, Ensemble Kalman filter (EnKF) algorithm, to a zone model, CFAST, for the simulation of a full-scale fire experiment in a two-story building structure. The results of the CFAST simulation with and without the EnKF algorithm are compared to the experimental data. It shows that the EnKF algorithm can significantly improve the accuracy of the CFAST simulation of the smoke layer temperature and height. To solve the problem of filter divergence and further reduce the computational cost, a general statistical interference method, the bootstrap method, is added to the EnKF algorithm. It shows that the bootstrap method improves the accuracy with fewer ensemble members and less computing time, thus even achieving a real-time forecasting on a personal computer. A sensitivity analysis is also conducted in nine case studies for demonstrating practical engineering applications of the real-time forecasting process.