Earl Brill Jr

Leakage in bulk water pipelines is a major problem for many water utilities as it leads to significant economic losses and cause service disruptions threatening public safety. Most utilities currently employ intrusive methods based on acoustic or infrared signals that can be expensive, time consuming, and require trained personnel. Non-intrusive methods that currently exist require significant computational resources and are not suitable for real-time application. We have developed extremely fast leakage detection algorithms that are suitable for real-time application. These methods rely on a hydraulic model and routine pressure measurements and were developed as part of an NSF project funded from 2011-2016. In partnership with DC Water, Lakewood City (CA), TAGO (an Internet of Things (IoT) company based in Raleigh, NC), and Citilogics (a smart water analytics company based in Cincinnati, OH) we will conduct a proof-of-concept study for isolated sections of each utilities?????????????????? water networks. Data from existing pressure sensors as well as new sensors installed at strategic locations in the network will be used to validate our algorithms using experimentally simulated leakage scenarios. Citilogics will provide necessary expertise and software for processing the real time data suitable for inputs into the hydraulic model from AMI, SCADA, and CMMS systems. TAGO will build an IoT framework for acquiring the pressure data (from sensors) in real-time and for integrating our algorithms and associated filters on cloud resources. Partner utilities will install the pressure sensors as needed and execute the experiment. NCSU will design the experiment, build the hydraulic model and the leak detection analytics, test the IoT architecture, and evaluate the potential for commercialization.

Dr. Earl Downey Brill, Jr. will serve as the representative of North Carolina State University to the Southeastern Transportation Research, Innovation, Development and Education (STRIDE) consortium. Dr. Brill is expected to spend about 8 hours per month participating in the consortium?s teleconferences, as well as in coordinating with faculty and staff at North Carolina State University.

NCSU??????????????????s CCEE department will work with Sensus and Town of Cary (ToC) in designing and executing a series of experiments to evaluate NCSU optimization algorithms (Phases 1-3). Phases 1-3 are expected to take 1 year and will require 1 graduate student from the NCSU CCEE department. Depending on the success of these experiments, additional phases may be carried out with the goal of developing software modules for efficient management and real time operation and control of urban water distribution systems. Activities beyond the experimental phase will be contingent on developing a successful intellectual property agreement between NCSU and Sensus.

Urban water distribution systems (WDS) are prone to leaks, deterioration, and contaminant intrusion. The risks associated with these conditions can be spatially diverse due to flow conditions, network characteristics, and external factors. The key to long term sustainability of these systems is to identify high risk regions and develop sound operational procedures and preventive maintenance plans. This project will develop an adaptive leak detection and contaminant intrusion framework that utilizes real time pressure, flow, and water quality data driven by a high performance simulation optimization engine. The research will also develop a risk analysis capability that could be used to analyze economic and public health risks associated with gradual leaks and contaminant intrusion occurring in day to day operations. In a recently completed project funded by NSF, the research team has developed an adaptive high performance simulation-optimization engine and associated optimization methodologies for contaminant source identification and sampling design in water distribution systems. While the proposed work will build on this work to an extent, a number of new developments are planned including: (a) a new simulation-based leak detection and contaminant intrusion detection methodology that uses routinely measured pressure, flow and water quality measurements (e.g., from a SCADA system), (b) a methodology for incorporating spatially varying macro indicators (e.g., pipe attributes) to improve the search process, (c) a methodology for incorporating demand uncertainty in leak detection, and (d) a risk assessment methodology that targets economic losses from leaks and public health risks from contaminant intrusion. The developed framework could be used by decision makers to make operational decisions and to develop long term maintenance and expansion plans. The system can also be used by decision makers to evaluate the risk reduction potential of different response and maintenance actions. In collaboration with a local utility, the leak detection and risk assessment framework developed through this research will be applied and validated using data from a mid-sized urban area. The major objectives are to: (a) develop a parallel simulation-optimization leak detection and contaminant intrusion methodology that uses routinely measured pressure and water quality data; (b) develop a Markov Chain Monte Carlo (MCMC) methodology to incorporate prior information and demand uncertainty into the leak and contaminant intrusion detection framework; (c) develop a methodology for evaluating economic and public health risks associated with leaks and contaminant intrusion; (d) test and evaluate the computational framework and the associated components for a mid- sized urban area; and (e) integrate and disseminate the research results in classroom teaching for college students, as well as continuing education and short courses for practitioners. Intellectual Merit. While complementing other related NSF funded efforts, the proposed research hinges on a number of paradigm shifting and transformational developments, including the conjunctive use of routine operational measurements and external factors for generating leak and contaminant intrusion maps in real time, incorporating uncertainties in a probabilistic framework, and the use of high performance computing technologies to enable real time computation. Broader Impact. Besides addressing one of the nation?s critical infrastructure security priorities in a real setting, the project contributes in general to the development of adaptive simulation methods and optimization algorithms that can harness high end computing resources. The computational framework developed through this proposal will set the stage for using emerging automated data collection systems for real time characterization.

The purpose of this proposal is to establish a graduate research fellowship program to train students to be future leaders in the area of engineering of resilient civil infrastructure systems for coastal regions considering natural hazards. This program will be conducted in coordination with the ongoing DHS Center of Excellence on Natural Disasters, Coastal Infrastructure and Emergency Management.

Effective threat management of urban water distribution systems, a critical civil infrastructure, is a challenging problem due to the dynamic nature of data and management requirements. Recent home-land security concerns have underscored the need for a dynamic feedback and control system that will automatically adapt to ever changing conditions and also devise optimal threat management strategies. Effective threat management requires that the source be characterized and contained and strategic shutdown and decontamination procedures implemented. An automated sensor network and an infrastructure that can use this data and control the data collection process is an important if not essential component in this process. How these systems are managed on a day to day basis and spontaneous threat management strategies will have profound socio-economic implications that may affect local city authorities as well as consumers. Adaptive management strategies will enable dynamic control of valves and pumps to control flow to meet demand conditions as well as to respond to accidents such as leaks and breakages. This in turn will lead to reliable and secure systems with increased operational life of various components (pipes, pumps, valves, sensors etc.). While some current systems have a level of automation in the way the major valves and pumps are controlled, they mostly rely on data provided by static sensor networks and do not provide any feed back to the sensors. This multi-disciplinary DDDAS proposal will attempt to develop a cyberinfrastructure system that will both adapt and control changing data, model, computer resources, and management needs.

The project will develop computer software to implement mathematical models designed to represent civil engineering infrastructure problems. In addition, the software will implement optimization techniques, such as mathematical programming and heuristic search methods. The techniques will be implemented so that alternative solutions, which meet given constraints on modeled objectives, will be obtained and provided to a user of the software.