Large-scale experimental study on precise dynamic ventilation control of tunnel fire smoke

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.