Smart Smoke Control as an Efficient Solution for Smoke Ventilation in Converted Cellars of Historic Buildings

Abstract

The paper is focused on the topic of smoke control in a confined, underground cellar area of a historical building, that is undergoing conversion to a restaurant. Similar venues were host to some of the most devastating fires in history. We have investigated the performance of a novel solution, “smart smoke control (SSC)”, and compared its performance with “traditional” smoke venting solution. The investigation was based on CFD simulations performed in a commercial code ANSYS Fluent, modified with user-defined functions to include for the steering algorithm of the SSC. The simulation results for a traditional system were unsatisfactory, while the SSC removing substantially more smoke (approx. 50% more) has provided tenable conditions over the whole course of the analysis. The results indicate that smoke control solutions based on the adaptive performance of the smoke exhaust fan, based on the conditions within the protected premise, can be favourable for compartments with the limited size of the smoke reservoir or limited space available for ducts. These findings are also true for existing buildings, where it may be advantageous to replace the existing smoke control solution with SSC. Future investigations should include full-scale experiments, and improvements in the simple algorithms used so far.

Computational Analysis of Choices in the Design of Smoke Extraction Duct Systems (SEDS) for Compartment Fires

“…To the authors' best knowledge, there are no similar CFD studies in the literature where a sensitivity study on the influence of different SEDS parameters has been investigated to this extent. Some numerical studies have modelled the SEDS as a simple vent on a wall without modelling the flow inside the duct (e.g., [3][4][5]) while other studies have considered already existing ducts in buildings without investigating the influence of different SEDS parameters (e.g., [6,7]). In addition, the types of occupancies can also vary significantly ranging from e.g., atria to tunnels, which can pose different requirements with respect to the design of the SEDS when compared to our scenarios.…”

Special Issue on “Smart Systems in Fire Engineering”

“…Panev et al [15] applied machine learning to predict fire resistance of composite shallow floor systems accurately. We ˛grzyn´ski et al [16] devised a novel ''smart smoke control'' (SSC) system for historic buildings. Brown et al [17] leveraged data obtained from wireless sensor networks embedded in fire hoses to enable smart firefighting activities.…”