Collaborative Research/DDDAS-TMRP: An Adaptive Cyberinfrastructure for Threat Management in Urban Water Distribution Systems

协作研究/DDDAS-TMRP：城市供水系统威胁管理的自适应网络基础设施

• 批准号: 0963571
• 负责人: Gregor von Laszewski
• 金额: $ 18.23万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-22 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0963571&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; TMRP; Adaptive

The goal of this collaborative/multi-disciplinary research project is to develop a cyberinfrastructure system that will both adapt to and control changing needs in data, models, computer resources and management choices facilitated by a dynamic workflow design. Using virtual simulation and a field study, this cyberinfrastructure will be tested on illustrative scenarios for adaptive management of contamination events in water distribution systems. Contamination threat management in drinking 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 measurements of flow, pressure and contaminant concentration 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.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.

这个协作/多学科研究项目的目标是开发一个网络基础结构系统，该系统将适应并控制由动态工作流设计促进的数据，模型，计算机资源和管理选择中不断变化的需求。使用虚拟仿真和现场研究，将在供水系统中污染事件的自适应管理中测试这种网络基础设施。 饮用水分配系统中的污染威胁管理涉及污染物来源和羽流的实时表征，控制策略的识别以及增量数据采样时间表的设计。这就需要与分析模块的流动，压力和污染物浓度的时间变化测量的动态整合，包括模型，以模拟系统状态，自适应采样方法的统计方法以及搜索有效控制策略的优化方法。对于现实的分配系统，分析模块是高度计算密集型的，需要通过计算机簇进行多级并行处理。虽然数据通常驱动分析模块，但数据需要在污染事件展开时动态变化的这些模块产生的解决方案的准确性和确定性。由于这种时间敏感的威胁事件需要实时响应，因此计算需求也必须与可用资源自适应匹配。因此，需要软件系统来通过高性能计算体系结构（例如Teragrid）来促进这种集成，从而使测量系统，分析模块和计算资源可以相互适应和引导。城市水分配系统很容易受到意外和有意污染的事件的影响，这些事件可能会导致人类健康和安全性影响。 典型的市政分配系统中的管道网络包括冗余流道，以确保网络部分无法使用时提供服务，并且设计具有大量存储空间，可在每日高峰需求期间输送水。 因此，典型的网络是高度互连的，并且在流和传输路径中经历了显着和频繁的波动。 这些设计无意识地在系统中的一个点造成污染，以通过网络通过不同的途径迅速传播，而消费者和运营商不知道。当通过第一道防线（例如，来自水质监视传感器网络的数据和消费者的报告）检测到污染事件时，市政当局在污染事件发生时面临着几个关键问题：污染的来源在哪里？这种污染发生了多长时间？在何处应采取其他液压或水质测量以更准确地查明来源？污染的当前和未来范围是什么？应采取哪些响应措施，例如关闭网络的部分，实施液压控制策略或引入去卫星，以最大程度地减少污染事件的影响？这些行动对消费者会有什么影响？对此类复杂问题的实时答案将带来重大的计算挑战。 该项目将通过开发自适应网络养育来应对这些挑战，该挑战将通过动态整合计算组件和实时传感器数据来实时处理和控制。该系统将使用基于大都市区域的现场尺度数据进行污染方案进行评估。