L. Han).L. Han, et al., FireGrid: An e-infrastructure for next-generation emergency response support, J. Parallel Distrib. Comput. AbstractThe FireGrid project aims to harness the potential of advanced forms of computation to support the response to large-scale emergencies (with an initial focus on the response to fires in the built environment). Computational models of physical phenomena are developed, and then deployed and computed on High Performance Computing resources to infer incident conditions by assimilating live sensor data from an emergency in real time-or, in the case of predictive models, faster-than-real time. The results of these models are then interpreted by a knowledge-based reasoning scheme to provide decision support information in appropriate terms for the emergency responder. These models are accessed over a Grid from an agent-based system, of which the human responders form an integral part. This paper proposes a novel FireGrid architecture, and describes the rationale behind this architecture and the research results of its application to a large-scale fire experiment.'Keywords: Emergency response, Grid, High performance computing, Multi-agent system, Knowledge-based reasoning, Fire simulation model L. Han, et al., FireGrid: An e-infrastructure for next-generation emergency response support, J. Parallel Distrib. Comput.

A sensor-linked modelling tool for live prediction of uncontrolled compartment fires, K-CRISP, has been developed in order to facilitate emergency response via novel systems such as FireGrid. 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. 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.

This study is aimed at developing a predictive capability for uncontrolled compartment fires which can be "steered" by real-time measurements. This capability is an essential step towards facilitating emergency response via systems such as FireGrid, which seek to provide fire and rescue services with information on the possible evolution of fire incidents on the scene. The strategy proposed to achieve this is a novel coupled simulation tool, based on the Monte-Carlo-based fire model, CRISP, with scenario selection achieved via comparison with (pseudo) sensor inputs. Here, some key aspects of such a system are illustrated and discussed in the context of the detailed measurements obtained in the full-scale fire test undertaken in a furnished apartment at Dalmarnock. The capability of CRISP in reproducing the fire conditions -given knowledge of the approximate heat release rate in the fire -was first verified. It is then shown that continuous selection from amongst a multiplicity of scenarios generated in Monte-Carlo fashion can be achieved, so that the predictions evolve in a way that closely follows the real fire conditions. Whilst the benefits of sensor-steering are already clearly apparent, further improvements will be possible by establishing an appropriate feedback loop between the results assessment and the parametric space in which new fires are generated, perhaps using Bayesian methods. Nevertheless, true predictive capability remains crucially dependent on the sufficient representation in the model of the mechanisms of fire growth, and this must be the focus in achieving better forecasting ability.

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