A deep learning–based surrogate model for spatial-temporal temperature field prediction in subway tunnel fires via CFD simulation

The smoke temperature and toxic gas generated by subway tunnel fires threaten trapped personnel and hinder rescue work. In this study, with a view to ensuring that smoke movement is acquired quickly and contributing to the future development of subway tunnel fires to guide emergency rescue, a deep learning surrogate model based on a transposed convolution neural network (TCNN) was proposed to predict the spatial-temporal temperature field. A numerical database of subway tunnel fires was constructed based on the computational fluid dynamics (CFD) method. Considering different fire locations, heat release rates (HRRs), and ventilation velocities, 135 scenarios were conducted to collect fire data. An adaptive up-projection unit (AUPU) and axial attention fusion block were proposed to improve the TCNN. The results showed that, with the inputs of fire location, HRR, and ventilation velocity, the surrogate model can quickly identify given features and reproduce the spatial-temporal temperature field with 94 % accuracy within 0.034 s. The proposed deep learning surrogate model can aid emergency control and rescue efforts in the context of subway tunnel fires.