Control method of mechanical smoke emission in high-rise building corridor

Abstract

The traditional method has a large control error in the corridor mechanical smoke control method. Therefore, a multi-task convolutional neural network-based high-rise building corridor mechanical smoke control method is proposed. Through the mechanical smoke exhaust principle of high-rise building corridors, the threshold of mechanical smoke exhaust is set to predict the mechanical smoke exhaust volume of high-rise building corridors. The movement of mechanical smoke in high-rise building corridors is simulated according to fire dynamics simulator to determine the turbulence state of mechanical smoke in high-rise building corridors. Input the mechanical smoke exhaust data of high-rise building corridors into the multi-task convolutional neural network to complete the mechanical smoke exhaust control of high-rise building corridors. Experimental results show that the maximum accuracy of this method is about 98%, and the control time is always less than 1 second.

Research on smoke control for an underground mall fire, based on smoke barrier and mechanical smoke exhaust system

“…A mechanical smoke exhaust system is set up in the middle of the roof of the underground shopping mall, with 10 (single row setup) smoke vents evenly distributed and the smoke exhaust volume of each vent set to 5 m 3 /s. The dimensions of three smoke vent sizes are 1.0 m × 0.5 m, 0.8 m × 0.5 m and 0.6 m × 0.5 m 28 , the functioning of the mechanical smoke exhaust system during a fire is simulated, and the results are used to analyze the effect of different smoke vent sizes on the smoke exhaust effect under the condition that the total smoke exhaust volume remains unchanged. Height of smoke layer at each monitoring point As can be seen from Fig.…”