DDDAS-TMRP (Collaborative Research): An
adaptive cyberinfrastructure for threat management in urban water distribution
systems
Funding
(Duration): $779,986 (01/06 – 12/08)
Sponsor:
National Science Foundation (Dynamic Data Driven
Application Systems Program)
PI/PD:
G. Mahinthakumar
NCSU:
G. Mahinthakumar (PI),
Abstract: Urban
water distribution systems are vulnerable to accidental and intentional
contamination incidents that could result in adverse human health and safety
impacts. The pipe network in a typical
municipal distribution system includes redundant flow paths to ensure service
when parts of the network are unavailable, and is designed with significant
storage to deliver water during daily peak demand periods. Thus, a typical network is highly
interconnected and experiences significant and frequent fluctuations in flows
and transport paths. These design features
unintentionally enable contamination at a single point in the system to spread
rapidly via different pathways through the network, unbeknown to consumers and
operators. When a contamination event is detected via the first line of
defense, e.g., data from a water quality surveillance sensor network and
reports from consumers, the municipal authorities are faced with several
critical questions as the contamination event unfolds: Where is the source of
contamination? When and for how long did this contamination occur? Where
additional hydraulic or water quality measurements should be taken to pinpoint
the source more accurately? What is the current and near future extent of
contamination? What response action, such as shutting down portions of the
network, implementing hydraulic control strategies, or introducing
decontaminants, should be taken to minimize the impact of the contamination
event? What would be the impact on consumers by these actions? Real-time
answers to such complex questions will present significant computational
challenges. This project will address
these challenges by developing an adaptive cyberinfrastucture
that will enable real-time processing and control through dynamic integration
of computational components and real-time sensor data. This system will be
evaluated using contamination scenarios based on field-scale data from a large
metropolitan area.