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

这一协作/多学科研究项目的目标是开发一个网络基础设施系统，该系统将适应和控制由动态工作流程设计促进的数据、模型、计算机资源和管理选择方面不断变化的需求。利用虚拟模拟和实地研究，这一网络基础设施将在水分配系统中对污染事件进行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烟羽的实时表征、控制策略的确定和增量数据采样计划的设计。这需要将流量、压力和污染物浓度的时变测量与分析模块进行动态集成，包括用于模拟系统状态的模型、用于自适应采样的统计方法以及用于搜索有效控制策略的优化方法。对于现实的分布式系统，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。虽然数据经常驱动分析模块，但当污染事件发生时，提高这些模块生成的解决方案的准确性和确定性的数据需求会动态变化。由于此类对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进行自适应匹配。因此，需要一种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，使得测量系统、分析模块和计算资源能够相互适应和引导。城市供水系统容易受到意外和故意污染事件的影响，这些事件可能导致对人类健康和安全的不利影响。典型市政配水系统中的管网包括冗余流动路径，可在部分管网不可用时确保服务，并且设计有大量存储空间，可在日常高峰时段输送水。因此，典型的网络高度互联，流量和传输路径经历显著而频繁的波动。这些设计特征无意中使系统中单点的污染通过网络中的不同路径迅速传播，而消费者和运营商并不知道。当通过第一道防线检测到污染事件时，例如来自水质监测传感器网络的数据和来自消费者的报告，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？这种污染是何时发生的，持续了多长时间？应该在哪里进行额外的水力或水质测量，以更准确地确定源头？目前和不久的将来污染的程度是什么？应采取什么应对措施，例如关闭部分网络、实施液压控制策略或引入净化剂，以将污染事件的影响降至最低？这些行动会对消费者产生什么影响？对这些复杂问题的实时回答将带来巨大的计算挑战。该项目将通过开发一种自适应的网络基础设施来应对这些挑战，该基础设施将通过计算组件和实时传感器数据的动态整合来实现实时处理和控制。该系统将使用基于来自大城市地区的现场规模数据的污染情景进行评估。

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.