Experimental study on the effect of canyon cross wind on temperature distribution of buoyancy-induced smoke layer in tunnel fires

Experiments were conducted in a 1:20 arced tunnel model to investigate the effect of canyon cross wind on buoyancy induced smoke flow characteristics of pool fres,involving smoke movement behaviour and longitudinal temperature distribution of smoke layer.The canyon wind speed,longitudinal fre location and fre size were varied.Results show that there are two special smoke behaviours with the fre source positioned at different flow feld zones.When the fire source is positioned at the negative pressure zone,with increasing canyon wind speed,the smoke always exists upstream mainly due to the vortex,and the smoke temperature near the fire source increases frst and then decreases.However,when the fre source is located in the transition zone and the unidirectional flow zone,there is no smoke appearing upstream with a certain canyon wind speed.Meanwhile,the smoke temperature near the fre sources are decreases with increasing canyon wind speed.The dimensionless temperature rise of the smoke layer OT:*along the longitudinal direction of the tunnel follows a good exponential decay.As the canyon wind speed increases,the longitudinal decay rate of△T.*decreases.The longitudinal decay rate of AT*downstream of the fire is related to the fre location and canyon wind speed,and independent of the fire size.The empirical correlations for predicting the longitudinal decay of OT:*downstream of the fre are established.For a relatively large-scale fre,the longitudinal decay rate of AT:*upstream of the fire increases as the distance between the fire source and the upstream portal increases,especially for larger canyon wind speeds.

Thermal Radiation Modelling in Tunnel Fires

Modelling based on Computational Fluid Dynamics(CFD)is by now effectively used in fire research and hazard analysis.Depending on the scenario,radiative heat transfer can play a very important role in enclosure combustion events such as tunnel fires.In this work,the importance of radiation and the effect of the use of different approaches to account for it were assessed.Firstly,small-scale tunnel fire simulations were performed and the results compared with experimental data,then realistic full-scale scenarios were simulated.The results show up the capability of CFD modelling to reproduce with good approximation tunnel fires.Radiation proved to be noteworthy mainly when the scale of the fire is relatively large.Among the various approaches employed to simulate radiation,the use of the Discrete Transfer model gave the most accurate results,mainly when the absorption-emission characteristics of the combustion products were taken into account.Finally,the suitability of the use of CFD in quantitative Fire Hazard Analysis is discussed.

A real-time forecast of tunnel fire based on numerical database and artificial intelligence

The extreme temperature induced by fire and hot toxic smokes in tunnels threaten the trapped personnel and firefighters.To alleviate the potential casualties,fast while reasonable decisions should be made for rescuing,based on the timely prediction of fire development in tunnels.This paper targets to achieve a real-time prediction(within 1 s)of the spatial-temporal temperature distribution inside the numerical tunnel model by using artificial intelligence(Al)methods.A CFD database of 100 simulated tunnel fire scenarios under various fire location,fire size,and ventilation condition is established.The proposed Al model combines a Long Short-term Memory(LSTM)model and a Transpose Convolution Neural Network(TCNN).The real-time ceiling temperature profile and thousands of temperature-field images are used as the training input and output.Results show that the predicted temperature field 60 s in advance achieves a high accuracy of around 97%.Also,the Al model can quickly identify the critical temperature field for safe evacuation(i.e.,a critical event)and guide emergency responses and firefighting activities.This study demonstrates the promising prospects of Al-based fire forecasts and smart firefighting in tunnel spaces.