Real-Time Forecasting of Building Fire Growth and Smoke Transport via Ensemble Kalman Filter

Abstract

Forecasting building fire growth and smoke dispersion is a challenging task but can provide early warnings to first responders and building occupants and thus significantly benefit active building fire protection. Although existent computer simulation models may provide acceptable estimations of smoke temperature and quantity, most simulations are still not able to achieve real-time forecast of building fire due to high computational requirements, and/or simulation accuracy subject to users’ inputs. This paper investigates one of the possibilities of using ensemble Kalman filter (EnKF), a statistical method utilizing the real-time sensor data from thermocouple trees in each room, to estimate the spread of an accidental building fire and further forecast smoke dispersion in real time. A general approach to forecasting building fire and smoke is outlined and demonstrated by a 1:5 scaled compartment fire experiment using a 1.0 kW to 2.8 kW propane burner as fire source. The results indicate that the EnKF method is able to forecast smoke transport in a multi-room building fire using 40 ensemble members and provide noticeable accuracy and lead time. Unlike other methods that directly use measurement data as model inputs, the developed model is able to statistically update model parameters to maintain the forecasting accuracy in real time. The results obtained from the model can be potentially applied to assist mechanical smoke removal, emergency evacuation and firefighting.