NSF DDDAS (Dynamic Data Driven Applications Systems)
An adaptive cyberinfrastructure for Threat Management in Water Distribution Networks

Contamination threat management in water distribution systems involves real-time characterization of the contaminant source and plume, identification of control strategies, and design of incremental data sampling schedules. This requires dynamic integration of time-varying flow, pressure and contaminant concentration measurement with analytical modules including models to simulate the state of the system, statistical methods for adaptive sampling, and optimization methods to search for efficient control strategies. For realistic distribution systems, the analytical modules are highly compute-intensive, requiring multi-level parallel processing via computer clusters. While data often drive the analytical modules, data needs for improving the accuracy and certainty of the solutions generated by these modules dynamically change when a contamination event unfolds. Since such time-sensitive threat events require real-time responses, the computational needs must also be adaptively matched with available resources. Thus, a software system is needed to facilitate this integration via a high-performance computing architecture (e.g., the TeraGrid) such that the measurement system, the analytical modules and the computing resources can mutually adapt and steer each other. The goal of this multi-disciplinary research is to develop a cyberinfrastructure system that will both adapt to and control changing data, models, computer resources and management needs. This cyberinfrastructure will be tested, using virtual simulations and a field study, for adaptive management of contamination events in water distribution systems.

The major research objectives are:

  • To develop simulation procedures and optimization & statistical algorithms that can adapt to changing conditions including data and computational resources.
  • To implement a grid-enabled dynamic work flow engine that can adaptively assemble and drive various data, computational, and computer resources components for changing conditions and demand.
  • To test and evaluate the work flow engine and the associated components for hydraulic control, water quality and hydraulic sensor network design, and confirmatory sampling procedures for an array of threat management scenarios using computer simulations.
  • To apply and demonstrate the integrated framework for contaminant characterization for realistic water distribution networks.

The cyberinfrastructure will consist of several coarse-grained components that together address four major categories: data, optimization & statistical algorithms, simulation models, and computer resources. A unique aspect of this proposal is that adaptivity embodies all four categories, and components in each category will include various levels of adaptivity. A sophisticated workflow engine will control the dynamic inter-play between the components. Specific coarse-grained components include, wireless sensors, hydraulic and quality data, optimization controller, optimization and Bayesian/Monte Carlo engines, simulation controller, simulation model, computer resource broker and allocator, and grid resources. The optimization engines and the simulation models will have a malleable nature so that they can be dynamically changed by their respective controllers.

This is a research effort funded by the DDDAS (Dynamic Data Driven Application Systems) Program at NSF.

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