Data and Knowledge Dual-Driven Inversion of Heat Release Rate in Tunnel Fires

The heat release rate (HRR) indicates the scale of a tunnel fire, and inverting it in real time from ceiling sensors supports fire detection and ventilation control. Purely data-driven (deep learning) models are accurate within the training range but cannot extrapolate to larger fires, whereas a purely physics-based formula is less accurate and fails during the fast-growth transient. This paper proposes a data and knowledge dual-driven HRR inversion method. The data component is an encoder-only Transformer on ceiling thermocouples, and the knowledge component is a slope-corrected plume-scaling inversion. The two are coupled by a training-time soft constraint and an inference-time two-layer gate: a magnitude gate raising the physics weight beyond the training power ceiling, and a steady-state gate down-weighting it during transients. On 24 simulated cases (six slopes × four powers, 0.5–4 MW), data are split by slope and power into mutually exclusive training, validation, and test subsets, the test covering unseen slopes and powers. The method outperforms the physics formula at every power tier; on power extrapolation it far surpasses the pure deep learning model (R2 = 0.84), and on slope extrapolation it matches that model (R2 = 0.94). The results demonstrate, within the present single-geometry FDS tunnel configuration and the investigated working conditions (0–5% slopes, 0.5–4 MW, t2 growth, natural ventilation), that physics-guided gated fusion can improve HRR estimation under a 4 MW single-power extrapolation test while retaining the accuracy of the data-driven model under slope extrapolation; the conclusions are not claimed to be directly transferable to other tunnel configurations.