Fire room

Abstract: By applying the rules set in traffic flow and pedestrian flow models, a basic cellular automata model is presented to simulate occupant evacuation in fire. Some extended models are introduced to study the special phenomena of evacuation from the fire room. The key of the models is the introduction of the danger grade which makes the route choice convenient and reasonable. Fire not only influences the emotional and behavioral characteristics of an individual but also affects his physical constitution, which reduces his maximal possible velocity. The models consider these influence factors by applying a set of simple but effective rules. It is needed to emphasize that all rules are established according to the essential phenomenon in fire evacuation, that is, all the occupants would try to move to the safest place as fast as possible. Some simulation examples are also presented to validate the applicability of the models.

Abstract: By applying the rules set in traffic flow and pedestrian flow models, a basic cellular automata model is presented to simulate occupant evacuation in fire. Some extended models are introduced to study the special phenomena of evacuation from the fire room. The key of the models is the introduction of the danger grade which makes the route choice convenient and reasonable. Fire not only influences the emotional and behavioral characteristics of an individual but also affects his physical constitution, which reduces his maximal possible velocity. The models consider these influence factors by applying a set of simple but effective rules. It is needed to emphasize that all rules are established according to the essential phenomenon in fire evacuation, that is, all the occupants would try to move to the safest place as fast as possible. Some simulation examples are also presented to validate the applicability of the models.

Abstract: Most devices associated with measures to detect and suppress fires in the built environment (e.g., commercial or modern residential buildings) are located near ceiling surfaces. The gas flow induced by an accidental fire tends to form a shallow layer beneath the ceiling surface that carries heat and smoke to areas remote from the fire position. Such a flow, known as a Ceiling Jet, can activate fire detection and suppression devices that are properly positioned in the shallow layer but can also cause damage eventually by heating the ceiling surface or structure. In this chapter, important characteristics of the ceiling jet, such as layer thickness, gas temperature or velocity and heat transfer rate for unconfined and confined ceiling configurations and for both steady and transient fires are discussed. Algebraic formulas are presented in all cases discussed to allow for rapid and reasonably accurate calculation of ceiling jet characteristics that can be used to verify aspects of more detailed numerical models. These formulas have also been embedded in comprehensive zone fire models and in design standards or codes.

Abstract: Smouldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks—they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smouldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m3). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community.