NUMERICAL INVESTIGATION OF THE KORALM TUNNEL FIRE TESTS USING AN AUTONOMOUS MESHING APPROACH WITH ADAPTIVE MESH REFINEMENT

Understanding smoke propagation and temperature stratification in tunnels during fire incidents is crucial in order to ensure the road user’s safety. Proper ventilation strategies have to be employed to control smoke propagation and allow for safe self-rescue and evacuation procedures.
In order to accurately predict thermal stratification and smoke propagation, numerical tools such as Computational Fluid Dynamics (CFD) are invaluable. However, CFD simulations of fire incidents are complex, as fire dynamics are strongly transient and pose several challenges due to the involved physics.
With the objective to address those challenges, we present in this work a CFD model, which features an improved workflow and turnaround time by utilizing an autonomous mesh generation technology. A high-quality mesh is generated on the fly and enables the use of adaptive mesh refinement (AMR) based on local flow gradients.
To assess the proposed method, simulation results are compared with measurements from full-scale fire tests performed by the Institut für Verbrennungskraftmaschinen und Thermodynamik (IVT), TU Graz at the Koralm tunnel (Austria). Namely, temperature readings from several sensors were used as validation data, along with the observed maximum backlayering length.