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Light nonaqueous phase liquids (LNAPLs), such as fuels, are the source of much soil and groundwater contamination. Though the mobility of LNAPLs is limited in many environments, dissolved-phase components, such as benzene, can produce groundwater plumes that are more mobile than the LNAPL source. In such a setting, it is commonly assumed that recovery of(More)
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(More)
FireGrid is a modern concept that aims to leverage a number of modern technologies to aid fire emergency response. In this paper we provide a brief introduction to the FireGrid project. A number of different technologies such as wireless sensor networks, grid-enabled High Performance Computing (HPC) implementation of fire models, and artificial intelligence(More)
The Gridweaver project reviewed the management tools and techniques currently available for Large Scale Configuration. We substantiated that these will not be able to sustain the demands of the Grid services currently in production, unless there is a paradigm shift towards high-level, declarative descriptions of fabric configurations. Our prototype(More)
This is the fourth report from the GridWeaver project. It describes a demonstrator that showcases a subset of the concepts explored in the previous report [ABK + 03a]. The aim of the demonstrator is to illustrate that we can model, configure, and manage a complex, adaptive system, using our prototype LCFG/SmartFrog architecture as the deployment(More)
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(More)
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 deployed on High Performance Computing (HPC) resources to interpret live sensor data from an emergency(More)
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