Monday, 29 November 2010

Forecasting Fire Growth

We published recently a paper in Fire Safety Journal titled "Forecasting Fire Growth using an Inverse Zone Modelling Approach". We are happy that the work has been widely featured in the media and many people is being exposed to the novel idea:

    The idea is based on the fact that effective control of a compartment fire saves lives and money. When fire fighters manage to put out a fire before it grows out of proportions, live safety is greatly increased and significant damage can be avoided. Moreover, the affected building can be re-occupied without major investment of resources. But when a fire passes a certain size, the building might collapses as a consequence of the fire damage to the structure (eg, 2001 WTC or 2005 Windsor Tower) or might have to be demolished due to irreversible damages.

    Due to a lack of the required technology to support emergency response, fire fighters often have to follow their intuition when it comes to attacking the fire instead of basing their decisions on knowledge of the actual fire. This lack of information can lead to lost opportunities or unnecessary risks.

    Prediction of the ongoing fire development ahead of time under different possible conditions based on the current events taking place would give fire fighters insight into the dynamics of the particular fire being flighted. With this extra knowledge, they could weight other options and feed more information into the emergency management. However, fire dynamics follow complex physical processes closely coupled to one another, which makes current tools not able to accurately forecast fire development in real time.

    This emerging technology has been called Sensor Assisted Fire Fighting. The FireGrid project, to which this paper belongs together with the recent PhD thesis of the lead author, aims at providing physics-based forecasts of fire development by combining measurements from sensors in the fire compartment with a range of computational modelling tools. The sensor measurements can provide essential lacking information and compensate the accuracy lost, and thus overcome the shortcomings of current modelling tools and speed them up. The proposed methodology is to collect measurements in the fire compartment, and to assimilate this data into the computational model.

    When enough measurements are available to characterize the current fire, a forecast is made. This forecast is then constantly updated with new incoming data. If, for example, a door is opened or glazing breaks, and the ventilation conditions change drastically, the sensor measurements will steer the computational model towards capturing the new conditions. With this technology, fire fighters could act upon forecast behaviour.

    This paper presents one of the first steps in this direction. Data is assimilated into a simple zone model, and forecasts of the fire development are made. Positive lead times are reported here for the first time. These results are an important step towards the forecast of fire dynamics to assist the emergency response. Together with the application to CFD within the same PhD thesis, the previous thesis of Cowlard on flame spread predictions and the most recent paper by Koo et al. on probabilistic zone models, these establish the basis for technology for sensor assisted fire fighting. The envisioned system is not yet fit for operational purposes and further research is needed. The investigation of the effects of adding further realism in the fire scenarios will be the focus of future studies.

    NOTE: This paper was short-listed within the top 5 submissions to the Lloyd's Science of Risk Prize in the Technology Category. See related article Hot talent in risk research in the Staff Bulletin of the University of Edinburgh.