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Sensor-Driven Fire Model (NIST SP 965)



William D. Davis, Glenn P. Forney


Modern building fire sensors are capable of supplying substantially more information to the fire service than just the simple detection of a possible fire. With the increase in the number of sensors installed in buildings for non-fire purposes, it is possible to capture this diverse information as input to fire alarm systems to enhance the value of the information in both fire and non-fire conditions. In order to use this information, a fire model needs to be developed that interprets a range of sensor signals and provides information about the building environment to the fire panel. Typical fire models useful for predicting the impact of fire in a building utilize a prescribed heat release rate (HRR) for the fire and can predict sensor response. For the inverse problem, a sensor-driven fire model uses sensor signals to estimate the HRR of the fire, identify areas where hazardous conditions are developing, and predict the development of the fire. A sensor-driven fire model is being developed at NIST for the NIST Virtual Cybernetic Building Test-bed to investigate the feasibility of such a model in buildings with HVAC systems. Version 1.1 of this model uses ceiling jet algorithms for temperature and smoke concentration to convert the analog or digital data from heat and smoke detectors to a HRR. A version of CFAST is then used to obtain layer temperatures and depths for the room of fire origin as well as surrounding rooms. With this information, the growth and spread of the fire and the location of hazardous conditions can be estimated. Details of the model will be presented and comparisons with experiments will be provided.
Special Publication (NIST SP) - 965
Report Number


fire detection, predictive models, fire detection systems, fire models, sensors, fire hazards


Davis, W. and Forney, G. (2001), Sensor-Driven Fire Model (NIST SP 965), Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD, [online], (Accessed April 23, 2024)
Created February 1, 2001, Updated November 10, 2018