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.

Tunnel fires pose a serious threat to life and property. The prediction of tunnel fires could reduce the risk and loss from thermal disasters. Computational fluid dynamics (CFD) provides a strong tool for quantitatively analyzing tunnel fires. However, CFD calculations are time-consuming, and reverse prediction from physical fields to key control parameters using the governing equation is impossible. To improve the prediction efficiency of tunnel fire information and solve the reverse prediction problem of key control parameters in tunnel fires, this paper proposes a deep learning model for fast bidirectional prediction between the entire physical fields and key control parameters of tunnel fires.

In this study, a deep learning model based on an encoder and a decoder is constructed, in which the encoder is used to construct the mapping from the physical fields to the key control parameters, and the decoder is used to construct the mapping from the key control parameters to the physical fields. In the model training process, the input of the encoder and the output of the decoder are required to be as close as possible, and the output of the encoder and the input of the decoder are also required to be as close as possible. The mathematical differences between them are therefore defined as the loss function. In this way, the encoder and the decoder form a cyclic structure. Data processing approaches are proposed so that all physical fields have a unified format and all key control parameters have the same distribution.

The proposed model is trained using a large high-resolution numerical database with different cases under various key control parameters. The data learning ability and prediction capacity of the deep learning model are evaluated. With the increase of the training epoch, the calculated temperature field and key control parameters increasingly agree with the true temperature field and key control parameters. After 100 training epochs, the loss function almost converges, and the proposed bidirectional prediction model with the constructed dataset achieves good training convergence. In addition, the physical fields and key control parameters can be reproduced on the training set. After the completion of model training, the prediction performance of the deep learning model is tested. The average temperature field of tunnel fires and the six key parameters of tunnel fires are accurately predicted, and the predictions encompass the geometric and physical information of the tunnel.

Overall, this article proposes a deep learning network model based on the characteristics of tunnel fires for predicting various physical fields and key control parameters of tunnel fires. This study can be applied for the rapid acquisition of the full physical fields of tunnel fires, which helps design ventilation systems in tunnels and risk evaluation. In addition, another application is to retrieve the key control parameters of tunnel fires, which helps to quickly obtain the key control parameters according to the recorded infrared temperature field in the postinvestigation of tunnel fires. The above application scenarios can provide theoretical bases and new ideas for the prevention and control of tunnel fires.