The effect of fire location and the reverse stack on fire smoke transport in high-rise buildings

In this paper, smoke transport in high-rise buildings through elevator shafts and stairwells is investigated for various fire location and stack effect conditions. For this purpose, a transient network model, Fire-STORM, is upgraded and used. The results are benchmarked by using a computational fluid dynamics (CFD) model. Six scenarios are tested, which are 1st floor, mid-floor, and top-most floor fires under normal stack (cold environment) and reverse stack (hot environment) conditions. For each scenario, the time history of pressures, temperatures, and soot mass fractions in the fire floors, elevator shafts, and stairwells and the average soot mass fraction in all stories of the building are presented. Overall, Fire-STORM has reasonably good accuracy compared to CFD with significantly faster computation times (90 s on a single core vs. 4 days on 32 cores in parallel). One of the intended uses of this fast low-order model is a data generation engine for neural network modeling of high-rise building fires. As such, one of the unique features of this work is the development of a realistic random heat release rate (HRR) modeling approach created using a Gaussian process.