Building fire safety design commonly relies on computational fluid dynamics simulations to obtain fire temperature fields. However, simulating the complete fire process in complex scenarios is time-consuming and cannot readily meet rapid assessment requirements. Existing methods for predicting fire temperature fields with conditional generative adversarial networks mostly rely on static mappings from building conditions to single-frame temperature fields, without fully accounting for the temporal dependencies between temperature fields at adjacent time steps as a fire develops. To address this issue, this study develops a temporal conditional generative adversarial network (TC-GAN), incorporating historical-state constraints into the pix2pix model. This study further optimizes the model to mitigate error accumulation in autoregressive prediction, thereby enabling rapid dynamic prediction of temperature-field evolution in single-story building fires.

A fire dataset was constructed using Fire Dynamics Simulator. It comprised 35 single-story building layouts, each configured with two fire-source locations, resulting in a total of 70 fire scenarios. Given the building layout, fire-source location, and prediction time, TC-GAN introduces the temperature field at the previous time step as a historical-state input and, during inference, continuously generates temperature-field sequences in an autoregressive manner. To mitigate error accumulation, three optimization strategies, namely, multi-step loss, scheduled sampling, and a video discriminator, were compared. Model performance was comprehensively evaluated using the normalized root mean square error (NRMSE), the structural similarity index measure (SSIM), temperature-evolution curves at key monitoring points, and the intersection over union (IoU) and area error of high-temperature regions above 70 ℃.

The results showed that TC-GAN with only historical-state constraints was affected by autoregressive error accumulation and exhibited lower predictive performance than pix2pix, whereas the model with scheduled sampling achieved the best performance. On the test set, the model with scheduled sampling reduced average NRMSE by 39.3% and increased average SSIM by 24.6% compared with pix2pix. Temperature-field visualizations showed that this variant captured the temperature diffusion process in complex building spaces more consistently, reducing the local high-temperature accumulation, spatial-pattern fragmentation, and abnormal diffusion observed in the pix2pix predictions. The predicted curves at key monitoring points likewise showed closer agreement with the reference temperature trends. For regions with temperatures above 70 ℃, the model with scheduled sampling increased the average IoU from 0.357 4 to 0.561 1 and decreased the average absolute area error from 0.276 5 to 0.212 2, indicating improved predictive capability for capturing the location and extent of high-temperature regions.

Historical-state constraints supply temporal evolution information for building-fire temperature-field prediction, but effective error-suppression strategies are required to mitigate autoregressive error accumulation. Under the conditions investigated in this study, TC-GAN combining scheduled sampling with historical-state constraints achieved the best overall predictive performance, outperforming the conventional pix2pix model in temperature-field accuracy, spatial structural consistency, temporal evolution at key monitoring points, and prediction of high-temperature regions. This method enables rapid prediction of temperature-field evolution in single-story building fires, reduces the time and computational costs of conventional numerical simulation, and provides a potential technical pathway for building fire safety design and rapid fire-risk assessment.

Traditional longitudinal ventilation systems for tunnel fires typically adopt a fixed critical velocity for control design, which fails to adapt to the dynamic evolution of smoke characteristics during fire growth. To solve this problem, this study conducts a large-scale experimental investigation on the precise dynamic ventilation control of tunnel fire smoke. A closed-loop longitudinal ventilation control system based on the proportional–integral–derivative (PID) algorithm is proposed. Taking real-time temperature signals from detectors as control inputs and dynamic fan frequency regulation as control outputs, the system realizes the adaptive regulation of tunnel fire smoke. Based on a 1:5 geometric scaled large-scale tunnel test platform, systematic tests are performed to explore the suppression effect of smoke back-layering and the maintenance mechanism of smoke stratification during smoke migration.

The test tunnel has an internal dimension of 260 m (length) × 2 m (width) × 2 m (height), and a 99% methanol pool fire is deployed as the fire source. Multipoint thermocouple arrays along the longitudinal direction, air velocity monitoring points, and a real-time fuel mass acquisition system are arranged to synchronously collect key parameters, including the temperature field distribution, longitudinal ventilation velocity, and fire heat release rate. A series of comparative experiments are carried out involving PID parameter tuning, system repeatability verification, and control point position variation tests. The control performance of the proposed system under diverse working conditions is clarified, and the influence of control point layout on smoke confinement efficiency is quantitatively analyzed.

The test results indicate that the optimized PID control system can dynamically adjust the longitudinal ventilation velocity in real time according to the temperature deviation at the detection point. Under various working conditions, the system effectively suppresses upstream smoke back-layering and maintains the stable stratification of downstream hot smoke layers. Repeatability tests demonstrate highly consistent temperature distribution characteristics and control effects, verifying the excellent robustness and repeatability of the proposed system. The control point position significantly affects the smoke control performance. When the control point is arranged near the tunnel ceiling and close to the fire source, the system exhibits faster response speed and higher control accuracy. In contrast, vertical downward offset of the control point aggravates the upstream migration trend of ceiling smoke and weakens the smoke front confinement capability of the system. When the control point is arranged axially farther from the fire source, the system can still confine smoke downstream of the target position; however, the decreased average ventilation velocity and heat exhaust capacity lead to higher temperature above the fire source and obvious control response delay.

This study verifies the feasibility and effectiveness of the PID-based intelligent smoke dynamic control method for tunnel fires through large-scale model experiments. The research findings provide solid experimental support and technical references for the improvement of tunnel fire ventilation control theory and the engineering promotion of intelligent tunnel smoke control systems.