Fire source location and heat release rate inversion in tunnel fires based on deep learning
The fire source location and heat release rate(HRR)are crucial information guiding emergency firefighting and rescue operations during tunnel fires.However,in practice,the informa-tion that can be obtained about the fire is limited.It is difficult to get the fire source key parameters directly.Therefore,we re-searched the deep learning-based method for inversing thefire source location and HRR in tunnel fires.Firstly,a tunnel fire data-set under different boundary conditions is established based on nu-merical simulations validated by experimental data.Based on con-volutional neural network(CNN)and long short-term memory network(LSTM),the mapping relationship between fixed tem-perature sensor data and fire source location and HRR was estab-lished.The inversion effectiveness of the model for fire source pa-rameters was evaluated.And the effect of time series length and sensor spacing on the inversion effectiveness were evaluated.The results demonstrate that the model has good inversion perfor-mance for both HRR and fire source location.When the time se-ries length was 20 s and the sensor spacing was 30 m,the R2 val-ues of the model inversion for HRR and fire location are 0.97 and 0.99,respectively.
万方数据
维普
