Sensor-linked fire simulation using a Monte-Carlo approach

Koo, Sung-Han; Fraser-Mitchell, Jeremy; Upadhyay, Rochan; Welch, Stephen

doi:10.3801/iafss.fss.9-1389

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…Since it is difficult for fire-fighters to be aware of the actual conditions in a built environment during a fire disaster, the research in [102] presented a novel e-infrastructure to infer the spreading of hazard based on predictive models and living sensory data in a faster-than-real time manner; the system consisted of on-site sensors including smoke detectors and temperature sensors and off-site computational models that were deployed on high-performance computing (HPC) resources. Gathered sensory data were used as inputs to a Monte Carlo-style fire spread model called K-CRISP [158] to predict the movement of fire and smoke; the results were interpreted by using a knowledge-based reasoning scheme within an agent-based command-and-control layer; the outputs were transmitted to fire-fighters for reference.…”

Section: Prediction-based Algorithmsmentioning

confidence: 99%

A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

Gelenbe

2019

Electronics

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The frequency, destruction and costs of natural and human-made disasters in modern highly-populated societies have resulted in research on emergency evacuation and wayfinding, which has drawn considerable attention. The subject is now a multidisciplinary area of research where information and communication technologies (ICT), and in particular the Internet of Things (IoT), have a significant impact on sensing and computing dynamic reactions that mitigate or prevent the worst outcomes of disasters. This paper offers state-of-the-art knowledge in this area so as to share ongoing research results, identify the research gaps and address the need for future research. We present a comprehensive review of research on emergency evacuation and wayfinding, focusing on the algorithmic and system design aspects. Starting from the history of emergency management research, we identify the emerging challenges concerning system optimisation, evacuee behaviour optimisation and data analysis, and the additional energy consumption by ICT equipment that operates the emergency management infrastructure.

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Section: Prediction-based Algorithmsmentioning

confidence: 99%

A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

Gelenbe

2019

Electronics

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“…We have developed a computational model entitled K-CRISP [16,17], an extension of CRISP, for simulating the fire in the experiment described in Section 5. This model is a sensor-linked extended zone model for fire development and simplified structural response for multiple rooms coupled with a model of human behaviour.…”

Section: Data Interpretationmentioning

confidence: 99%

FireGrid: An e-infrastructure for next-generation emergency response support

Han

Potter

Beckett

et al. 2010

Journal of Parallel and Distributed Computing

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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.

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“…A simple scenario selection procedure was adopted in earlier work using (pseudo) sensor measurements obtained in the full-scale fire test undertaken in a furnished apartment at Dalmarnock [10]. With this method a continuous selection from amongst a multiplicity of scenarios generated in Monte-Carlo fashion was achieved using a standard deviation parameter to characterise the difference between the predictions and the measurements.…”

Section: Steering Proceduresmentioning

confidence: 99%

Sensor-steered fire simulation

Koo

Fraser-Mitchell

Welch

2010

Fire Safety Journal

Self Cite

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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.

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Sensor-linked fire simulation using a Monte-Carlo approach

Cited by 9 publications

References 6 publications

A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

A Survey of Algorithms and Systems for Evacuating People in Confined Spaces

FireGrid: An e-infrastructure for next-generation emergency response support

Sensor-steered fire simulation

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