Forecasting fire development with sensor-linked simulation
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In fire, any information about the actual condition within the building could be essential for quick and safe response of both fire–fighters and occupants. In most cases, however, the emergency responders will rarely be aware of the actual conditions within a building and they will have to make critical decisions based on limited information. Recent buildings are equipped with numbers of sensors which may potentially contain useful information about the fire; however, most buildings do not have capability of exploiting these sensors to provide any useful information beyond the initial stage of warning about the possible existence of a fire. A sensor–linked modelling tool for live prediction of uncontrolled compartment fires, K– CRISP, has therefore been developed. The modelling strategy is an extension of the Monte– Carlo fire model, CRISP, linking simulations to sensor inputs which controls evolution of the parametric space in which new scenarios are generated, thereby representing real–time “learning” about the fire. CRISP itself is based on a zone model representation of the fire, with linked capabilities for egress modelling and failure prediction for structural members, thus providing a major advantage over more detailed approaches in terms of flexibility and practicality, though with the conventional limitations of zone models. Large numbers of scenarios are required, but computational demands are mitigated to some extent by various procedures to limit the parameters which need to be varied. HPC (high performance computing) resources are exploited in “urgent computing” mode. K–CRISP was demonstrated in conjunction with measurements obtained from two sets of full–scale fire experiments. In one case, model execution was performed live. The thesis further investigates the predictive capability of the model by running it in pseudo real–time. The approach adopted for steering is shown to be effective in directing the evolution of the fire parameters, thereby driving the fire predictions towards the measurements. Moreover, the availability of probabilistic information in the output assists in providing potential end users with an indication of the likelihood of various hazard scenarios. The best forecasts are those for the immediate future, or for relatively simple fires, with progressively less confidence at longer lead times and in more complex scenarios. Given the uncertainties in real fire development the benefits of more detailed model representations may be marginal and the system developed thus far is considered to be an appropriate engineering approach to the problem, providing information of potential benefit in emergency response. Thus, the sensor–linked model proved to be capable of forecasting the fire development super–real– time and it was also able to predict critical events such as flashover and structural collapse. Finally, the prediction results are assessed and the limitations of the model were further discussed. This enabled careful assessment of how the model should be applied, what sensors are required, and how reliable the model can be, etc.
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