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

这个合作/多学科研究计划的目标是发展一个网络基础设施系统，以适应和控制在数据、模型、计算机资源和管理选择方面不断变化的需求，并通过动态工作流程设计加以促进。通过虚拟模拟和实地研究，该网络基础设施将在配水系统污染事件适应性管理的说明性场景中进行测试。饮用水分配系统中的污染威胁管理涉及污染源和羽流的实时表征，控制策略的确定以及增量数据采样时间表的设计。这需要将流量、压力和污染物浓度的时变测量与分析模块动态集成，包括模拟系统状态的模型、自适应采样的统计方法和寻找有效控制策略的优化方法。对于实际的配电系统，分析模块是高度计算密集型的，需要通过计算机集群进行多级并行处理。虽然数据通常驱动分析模块，但当污染事件发生时，数据需要提高这些模块生成的解决方案的准确性和确定性。由于此类时间敏感的威胁事件需要实时响应，因此计算需求还必须与可用资源自适应匹配。因此，需要一个软件系统来通过高性能计算架构（例如，TeraGrid）促进这种集成，这样测量系统、分析模块和计算资源可以相互适应并相互引导。城市配水系统容易受到意外和故意污染事件的影响，这些事件可能对人类健康和安全造成不利影响。典型的市政配水系统的管网包括冗余的流道，以确保在部分管网不可用时提供服务，并设计有大量的储存，以便在每日需求高峰期间供水。因此，一个典型的网络是高度互联的，并且在流量和传输路径上经历显著和频繁的波动。这些设计特点无意中使系统中单个点的污染通过网络的不同途径迅速传播，而消费者和运营商却不知道。当通过第一道防线（例如，来自水质监测传感器网络的数据和消费者的报告）检测到污染事件时，随着污染事件的展开，市政当局面临着几个关键问题：污染源在哪里？这种污染是什么时候发生的，持续了多久？在何处需要进行额外的水力或水质测量以更准确地查明水源？目前和近期的污染程度是什么？应该采取哪些应对措施，如关闭部分管网、实施液压控制策略或引入去污剂，以最大限度地减少污染事件的影响？这些举措会对消费者产生什么影响？实时回答如此复杂的问题将对计算提出重大挑战。该项目将通过开发自适应网络基础设施来解决这些挑战，该基础设施将通过动态集成计算组件和实时传感器数据来实现实时处理和控制。该系统将根据来自大城市地区的现场规模数据使用污染情景进行评估。

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.