Multi-Scale Modeling of Interdependent Critical Infrastructure System Performance During Hurricanes

飓风期间相互依赖的关键基础设施系统性能的多尺度建模

• 批准号: 0968711
• 负责人: Seth Guikema
• 金额: $ 25.52万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968711&HistoricalAwards=false
• 关键词: Multi; Scale; Modeling; Interdependent; Critical

Critical infrastructure systems such as water and electric power networks provide essential services that underlie the economic prosperity, security, and public health of the U.S. These complex, interdependent systems are prone to failure during hurricanes. Improved modeling of the ability of these systems to meet the needs of society after a hurricane makes landfall would substantially improve our ability to manage the risk of these systems failing. However, there are fundamental research needs of both conceptual and computational natures in the area of risk analysis for critical infrastructure systems in hurricane-prone areas. Conceptually, we do not yet have modeling frameworks that allow for accurate prediction of the performance of large-scale interdependent infrastructure systems during hurricanes, a necessary starting point for accurate risk assessment and management. Computationally, many of the available tools that aim to model infrastructure performance at the scale of large metropolitan areas require long run times on large computer clusters, limiting their usefulness for practical infrastructure planning and management. Recent advances in both statistical methods and computing based on graphical processing units (?graphics cards?) enable advances that can address both the conceptual and computational limitations inherent in current approaches for risk analysis for interdependent infrastructure systems in hurricane-prone areas. The focus of this project is on developing methods for accurate performance and risk modeling for interdependent infrastructure systems, methods that are practical for infrastructure managers to use. While the focus of this project is on coupled water and power systems, the advances will have application much more broadly.This project will enable significantly more accurate and rapid risk analysis for interdependent infrastructure systems, allowing highly limited public infrastructure funds to be spent more efficiently and helping to better protect economic and public health during disasters. The models developed in this project will be practical for use on desktop computers with existing higher-end graphics cards, greatly enhancing the ability of infrastructure managers to run these models on their existing computer hardware. In addition, this project will yield insights into the factors that lead interdependent infrastructure systems to be more resilient during a hurricane, helping engineers and utility system managers better understand how to strengthen their systems. In parallel with the research efforts, this project will aim to interest students traditionally underrepresented in engineering programs in pursuing engineering as a career. This will be done through outreach at multiple educational levels.

水和电力网络等关键基础设施系统提供支撑美国经济繁荣、安全和公共卫生的基本服务。这些复杂、相互依赖的系统在飓风期间很容易发生故障。改进对这些系统在飓风登陆后满足社会需求的能力的建模将大大提高我们管理这些系统故障风险的能力。然而，在飓风多发地区关键基础设施系统的风险分析领域，存在概念和计算性质的基础研究需求。从概念上讲，我们还没有能够准确预测飓风期间大规模相互依赖的基础设施系统性能的建模框架，这是准确风险评估和管理的必要起点。在计算方面，许多旨在对大都市区规模的基础设施性能进行建模的可用工具需要在大型计算机集群上运行很长时间，从而限制了它们在实际基础设施规划和管理中的实用性。基于图形处理单元（“显卡”）的统计方法和计算的最新进展使得能够解决飓风多发地区相互依赖的基础设施系统的当前风险分析方法固有的概念和计算限制。该项目的重点是为相互依赖的基础设施系统开发准确的性能和风险建模方法，这些方法对于基础设施管理者来说是实用的。虽然该项目的重点是耦合的水电系统，但这些进步将具有更广泛的应用。该项目将为相互依赖的基础设施系统提供更加准确和快速的风险分析，从而使高度有限的公共基础设施资金能够更有效地使用并帮助在灾害期间更好地保护经济和公共健康。该项目开发的模型将适用于具有现有高端显卡的台式计算机，从而大大增强基础设施管理者在其现有计算机硬件上运行这些模型的能力。此外，该项目还将深入了解导致相互依赖的基础设施系统在飓风期间更具弹性的因素，帮助工程师和公用事业系统经理更好地了解如何加强其系统。在开展研究工作的同时，该项目旨在激发传统上工程项目中代表性不足的学生的兴趣，以追求工程作为职业。这将通过多个教育级别的外展活动来完成。