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.
