Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

Abstract

Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multiphysics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Gridbased adaptive execution engine, distributed data management services for real-time data access, exploration, and coupling, and self-managing middleware services for seamless discovery and composition of components, services, and data on the Grid. This paper investigates how these solutions can be leveraged and applied to address another DDDAS application of strategic importance -the data-driven management of Ruby Gulch Waste Repository.

Advantages of Multiscale Detection of Defective Pills during Manufacturing

“…Measurements from a series of ISP-ARS sensors should control the manufacturing line dynamically and adjust the process conditions and ingredients in real time based on actual process measurements [15] and [16].…”

Introduction to the ICCS2006 Workshop on Dynamic Data Driven Applications Systems

“…In projects [9][10][11][12], the applications are in the areas of environmental and natural resource management. In [9] the project employs previous work performed by the investigators in the Instrumented Oil-Field DDDAS project that has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management.…”

“…In [9] the project employs previous work performed by the investigators in the Instrumented Oil-Field DDDAS project that has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This previous work has led to the implementation of advanced multi-physics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications.…”

Enabling End-to-End Data-Driven Sensor-Based Scientific and Engineering Applications

“…Several application domains, such as waste management [1], underwater ocean phenomenon monitoring [2], city-wide structural monitoring [3] and endto-end soil monitoring system [4], are already experiencing this revolution in instrumentation, and can potentially allow new quantitative synthesis and hypothesis testing in near real time as data streams in from distributed instruments. However, these application present many new and challenging requirements due to (1) the data volume and rates, (2) the uncertainty in this data and the need to characterize and manage this uncertainty and (3) the need to assimilate and transport required data (often from remote sites over low bandwidth wide area networks) in near real-time so that it can be effectively integrated with (running) computational models and analysis systems.…”

“…The former supports complex querying of the sensor system, while the latter enables development of in-network data processing mechanisms such as aggregation, adaptive interpolation and assimilations, both via semantically meaningful abstractions. The research is driven by the management and control of subsurface geosystems, such as managing subsurface contaminants at the Ruby Gulch waste repository [1] and management and optimization of oil reservoirs [6]. Crosscutting requirements of these applications include multi-scale, multi-resolution data access, data quality and uncertainty estimation, and predictable temporal response to varying application characteristics.…”