Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Sensor-Driven Fire Model Version 1.1
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 (SDFM) 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 SDFM 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 SDFM will be presented and comparisons with expertise will be provided.
Sensor-Driven Fire Model Version 1.1
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 (SDFM) 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 SDFM 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 SDFM will be presented and comparisons with expertise will be provided.
Sensor-Driven Fire Model Version 1.1
W. D. Davis (Autor:in) / G. P. Forney (Autor:in)
2001
42 pages
Report
Keine Angabe
Englisch
| British Library Conference Proceedings | 2001
Sensor and Modelling Driven Real-Time Fire Forecast
| Springer Verlag | 2024