FireGrid Cluster
http://hdl.handle.net/1842/1156
2019-04-09T22:41:11ZFireGrid: An e-infrastructure for next-generation emergency response support
http://hdl.handle.net/1842/3988
FireGrid: An e-infrastructure for next-generation emergency response support
Han, Liangxiu; Potter, Stephen; Beckett, George; Pringle, Gavin; Welch, Stephen; Koo, Sung-Han; Wickler, Gerhard; Usmani, Asif; Torero, Jose L; Tate, Austin
The 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.Emergency response, Grid, High performance computing, Multi-agent system, Knowledge-based reasoning, Fire simulation model
Peer reviewed paper published in Journal of Parallel and Distributed Computing.
2010-01-01T00:00:00ZSensor-steered fire simulation
http://hdl.handle.net/1842/3534
Sensor-steered fire simulation
Koo, Sung-Han; Fraser-Mitchell, Jeremy; Welch, Stephen
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.
2010-01-01T00:00:00ZInverse Modelling to Forecast Enclosure Fire Dynamics
http://hdl.handle.net/1842/3418
Inverse Modelling to Forecast Enclosure Fire Dynamics
Jahn, Wolfram
Despite advances in the understanding of fire dynamics over the past decades and
despite the advances in computational capacity, our ability to predict the behaviour of fires in general and building fires in particular remains very limited. This thesis proposes and studies a method to use measurements of the real event in order to steer and accelerate fire simulations. This technology aims at providing forecasts of the fire development with a positive lead time, i.e. the forecast of future events is ready before those events take place. A simplified fire spread model is implemented, and sensor data are assimilated into the model in order to estimate the parameters that characterize the spread model and thus recover information lost by approximations. The assimilation process is posed as an inverse problem, which is solved minimizing a non linear cost function that measures the distance between sensor data and the forward model. In order to accelerate the optimization procedure, the ‘tangent linear model’ is implemented, i.e. the forward model is linearized around the initial guess of the governing parameters that are to be estimated, thus approximating the cost function by a quadratic function.
The methodology was tested first with a simple two-zone forward model, and then
with a coarse grid Computational Fluid Dynamics (CFD) fire model as forward model.
Observations for the inverse modelling were generated using a fine grid CFD simulation
in order to illustrate the methodology. A test case with observations from a real scale
fire test is presented at the end of this document.
In the two-zone model approach the spread rate, entrainment coefficient and gas
transport time are the governing invariant parameters that are estimated. The parameters
could be estimated correctly and the temperature and the height of the hot layer were reproduced satisfactorily. Moreover, the heat release rate and growth rate were estimated correctly with a positive lead time of up to 30 s. The results showed that the simple mass and heat balances and plume correlation of the zone model were enough to satisfactorily forecast the main features of the fire, and that positive lead times are possible. With the CFD forward model the growth rate, fuel mass loss rate and other parameters of a fire were estimated by assimilating measurements from the fire into the model.
It was shown that with a field type forward model it is possible to estimate the growth
rates of several different spread rates simultaneously. A coarse grid CFD model with
very short computation times was used to assimilate measurements and it was shown
that spatially resolved forecasts can be obtained in reasonable time, when combined
with observations from the fire.
The assimilation of observations from a real scale fire test into a coarse grid CFD
model showed that the estimation of a fire growth parameter is possible in complicated
scenarios in reasonable time, and that the resulting forecasts at localized level present good levels of accuracy.
The proposed methodology is still subject to ongoing research. The limited capability
of the forward model to represent the true fire has to be addressed with more detail,
and the additional information that has to be provided in order to run the simulations has to be investigated. When using a CFD type forward model, additional to the detailed geometry, it is necessary to establish the location of the fire origin and the potential fuel load before starting the assimilation cycle. While the fire origin can be located easily (as a first approximation the location of the highest temperature reading can be used), the fuel load is potentially very variable and its exact distribution might be impractical to continually keep track of. It was however shown that for relatively small compartments
the exact fuel distribution is not essential in order to produce an adequate forecast, and
the fuel load could for example be established based on a statistical analysis of typical compartment layouts.
2010-05-01T00:00:00ZUsing Simulation for Decision Support: Lessons Learned from FireGrid
http://hdl.handle.net/1842/3074
Using Simulation for Decision Support: Lessons Learned from FireGrid
Wickler, Gerhard; Beckett, George; Han, Liangxiu; Koo, Sung-Han; Potter, Stephen; Pringle, Gavin; Tate, Austin
This paper describes some of the lessons learned from the FireGrid project. It starts with a brief overview of the project. The discussion of the lessons learned that follows is intended for others attempting to develop a similar system, where sensor data is used to steer a super-real time simulation in order to generate predictions that will provide decision support for emergency responders.
2009-05-01T00:00:00Z