Application of the EnKF method for real-time forecasting of smoke movement during tunnel fires

Abstract Real-time prediction of smoke layer temperature and height of tunnel fires are crucial in guiding emergency rescue. However, current fire simulation tools are often not able to provide reliable modeling results due to poorly known input parameters and model errors. Besides, fire modeling are subject to computer resources, for instance, fire modeling by computational fluid dynamics (CFD) tools is often time-consuming. Moreover, sensors located in tunnels can only detect certain physical quantities within a certain level of uncertainties. In order to gain more reliable predictions of temperature and smoke layer height of tunnel fires in real time, a proposed method, inverse modeling based on Ensemble Kalman Filter (EnKF), is presented in this study to improve the predictability and address problems of demanding computer resources of tunnel fire simulation by doing data assimilation. The basic formulas of EnKF method are introduced and the application of EnKF to tunnel fires is implemented by connecting the fire simulation tool, CFAST, with a data assimilation software, OpenDA. In current study, observation data are generated under the framework of Observation System Simulation Experiment (OSSE), i.e., synthetic observations are generated by CFAST simulation assuming true value of control parameters are known. Studies are conducted to show the feasibility of real-time predicting smoke movement during tunnel fires. Results show that prediction performance are improved after applying the EnKF method compared to the standalone tunnel fires modeling.