An Adaptive Leak Detection and Risk Analysis Framework for Urban Water Distribution Systems

城市供水系统的自适应泄漏检测和风险分析框架

• 批准号: 1100458
• 负责人: Gnanamanikam Mahinthakumar
• 金额: $ 29.11万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1100458&HistoricalAwards=false
• 关键词: Adaptive; Leak; Detection; Risk; Analysis

The primary goal of this project is to understand how routine measurements of pressure, flow and water quality data could be used to characterize leaks and contaminant intrusion in urban water distribution systems and illustrate how this information could be used to aid in the risk assessment of these systems. Research will be conducted in adaptive simulation methods and optimization algorithms enabled by high performance computing to develop a quantitative framework for leak detection and contaminant intrusion characterization that utilizes real time pressure, flow, and water quality data. In addition, this framework will integrate new methodologies for incorporating spatially varying macro indicators and demand uncertainty. The research will also provide a risk analysis capability for analyzing economic and public health risks associated with gradual leaks and contaminant intrusion during routine operations. This quantitative prototype framework is envisioned for use by decision-makers to generate and identify operational strategies as well as to develop long-term maintenance and expansion plans. In addition, it will enable decision-makers to evaluate the risk reduction potential of different response, retrofit and maintenance actions. In collaboration with a major water utility in North Carolina, this quantitative prototype framework will be applied and validated using data from a mid-sized urban area. If successful, this project will address one of the nation's critical infrastructure security priorities in a real setting and contribute to the development of adaptive simulation methods and optimization algorithms that are enabled by harnessing the power of high-end computing resources. The computational framework that is formulated and implemented through this research project will set the stage for using emerging automated data collection systems for real time water distribution infrastructure condition assessment. The research methods and tools will also be made available via web-accessible interfaces for instructional and outreach purposes.

该项目的主要目标是了解如何使用压力、流量和水质数据的常规测量来描述城市供水系统中的泄漏和污染物入侵，并说明如何使用这些信息来帮助评估这些系统的风险。研究将在自适应模拟方法和优化算法上进行，通过高性能计算来开发一个利用实时压力、流量和水质数据进行泄漏检测和污染物入侵表征的定量框架。此外，该框架将纳入新的方法，以纳入空间变化的宏观指标和需求不确定性。该研究还将提供风险分析能力，以分析与日常操作中逐渐泄漏和污染物侵入相关的经济和公共卫生风险。这个量化的原型框架被设想为决策者用来生成和确定运营策略，以及制定长期维护和扩展计划。此外，它将使决策者能够评估不同响应、改造和维护行动的风险降低潜力。与北卡罗莱纳州的一家主要水务公司合作，这个定量原型框架将使用一个中型城市地区的数据进行应用和验证。如果成功，该项目将在现实环境中解决国家关键基础设施安全优先事项之一，并通过利用高端计算资源的力量，为自适应模拟方法和优化算法的发展做出贡献。通过该研究项目制定和实施的计算框架将为使用新兴的自动化数据收集系统进行实时供水基础设施状况评估奠定基础。研究方法和工具也将通过可访问的网络界面提供，用于教学和推广目的。