CAREER: Toward a theory for smart stormwater systems

职业：智能雨水系统理论

• 批准号: 1750744
• 负责人: Branko Kerkez
• 金额: $ 50万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750744&HistoricalAwards=false
• 关键词: CAREER; Toward; theory; smart; stormwater

Many cities across the United States maintain large networks of stormwater basins, canals, and underground pipes. When it rains, this infrastructure moves water and pollutants away from buildings to nearby rivers and streams. In many cities, these stormwater systems are too small or too old, resulting in dangerous flooding and pollution of downstream rivers and lakes. Upgrading infrastructure is a costly alternative. Instead of relying on new construction, the PI will demonstrate how existing stormwater systems can be used more efficiently and made "smarter" through the use of wireless sensors, valves, and pumps to address this national issue. This approach will enable automatic control of stormwater systems to reduce flooding and improve water quality. To this end, the PI will investigate computational methods to embed stormwater systems with "intelligence," helping to reduce the cost of improving US infrastructure while protecting public health and the environment. The PI will also develop training programs for a new generation of technicians, who will deploy and maintain smart stormwater systems. The PI will improve the fundamental understanding of the water flow and water quality benefits that can be achieved through the real-time control of urban watersheds. Results of this research will lead to a theoretical and conceptual framework to begin studying the impacts of autonomous technologies on the scale of entire watersheds. The research hypothesis of this proposal is that "smart" stormwater systems will vastly shrink the size of infrastructure required to manage runoff pollution. The hypothesis will be tested across three objectives: (1) investigate new algorithms, based on dynamical systems theory and reinforcement learning to control flows across city-scale stormwater systems, (2) study the distributed real-time control of water quality across the scale of entire watersheds with a focus on removing of sediment-bound and dissolved pollutants, and (3) explore how competing water flow and water quality goals can be unified to resiliently manage urban watersheds in real-time. Results of this research will allow entire watersheds to be controlled, akin to large distributed treatment plants, while vastly improving the fundamental understanding of the benefits, tradeoffs, and performance limits of "smart" water systems. All findings, data and algorithms will be shared freely with other researchers and practitioners through an open source web portal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

美国许多城市都维护着大型的雨水盆地、运河和地下管道网络。下雨时，这些基础设施会将水和污染物从建筑物转移到附近的河流和溪流。在许多城市，这些雨水系统太小或太旧，导致危险的洪水和下游河流和湖泊的污染。升级基础设施是一种成本高昂的替代方案。 PI 将展示如何通过使用无线传感器、阀门和泵来更有效地利用现有雨水系统并使其变得“更智能”，以解决这一国家问题，而不是依赖新的建筑。这种方法将实现雨水系统的自动控制，以减少洪水并改善水质。为此，PI将研究将雨水系统嵌入“智能”的计算方法，帮助降低改善美国基础设施的成本，同时保护公众健康和环境。 PI 还将为新一代技术人员制定培训计划，他们将部署和维护智能雨水系统。 该PI将提高对通过城市流域实时控制可实现的水流和水质效益的基本了解。这项研究的结果将形成一个理论和概念框架，以开始研究自主技术对整个流域规模的影响。该提案的研究假设是“智能”雨水系统将大大缩小管理径流污染所需的基础设施规模。该假设将在三个目标上进行测试：（1）研究基于动力系统理论和强化学习的新算法，以控制城市规模雨水系统的流量，（2）研究跨规模水质的分布式实时控制整个流域的治理，重点是去除沉积物和溶解的污染物，以及（3）探索如何统一相互竞争的水流和水质目标，以实时弹性管理城市流域。这项研究的结果将允许控制整个流域，类似于大型分布式处理厂，同时极大地提高对“智能”水系统的好处、权衡和性能限制的基本理解。所有研究结果、数据和算法都将通过开源门户网站与其他研究人员和从业者自由共享。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

pystorms: A simulation sandbox for the development and evaluation of stormwater control algorithms

pystorms：用于开发和评估雨水控制算法的模拟沙箱

• DOI: 10.1016/j.envsoft.2023.105635
• 发表时间: 2023-04
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Rimer, Sara P.;Mullapudi, Abhiram;Troutman, Sara C.;Ewing, Gregory;Bowes, Benjamin D.;Akin, Aaron A.;Sadler, Jeffrey;Kertesz, Ruben;McDonnell, Bryant;Montestruque, Luis;et al
• 通讯作者: et al

StormReactor: An open-source Python package for the integrated modeling of urban water quality and water balance

StormReactor：用于城市水质和水平衡集成建模的开源Python包

• DOI: 10.1016/j.envsoft.2021.105175
• 发表时间: 2021-11
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Mason, Brooke E.;Mullapudi, Abhiram;Kerkez, Branko
• 通讯作者: Kerkez, Branko