Collaborative Research: Adverse Multiphase Flow Interactions in Urban Stormwater Systems

合作研究：城市雨水系统中的不利多相流相互作用

• 批准号: 2049094
• 负责人: Daniel Wright
• 金额: $ 28.13万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2049094&HistoricalAwards=false
• 关键词: Collaborative; Research; Adverse; Multiphase; Flow

This NSF grant will investigate Adverse Multiphase Flow Interactions (AMFI), poorly understood phenomena that occur during intense rain events that are caused by the entrapment of air within stormwater systems. During rapid filling events this air is unable to readily escape and compresses, causing operational issues such as stormwater “geysers” and inlet cover displacements. The frequency and severity of AMFI is linked to spatiotemporal variability of extreme rainstorms interacting within the complex networked systems of stormwater inlets, sewers, and tunnels. Current stormwater design paradigms and tools have been tailored toward minimizing failures associated with street flooding or with the discharge of contaminated flows, both of which are linked to gradual changes in single-phase water flows. In contrast, AMFI failures occur over much shorter timeframes and involve more complex two-phase flows conditions. This means that mitigation measures and tools that work well in traditional contexts cannot anticipate or prevent AMFI failures. Consequently, cities currently allocate resources to fix AMFI failures without understanding or addressing the root causes. This lack of system-level understanding and tools for predicting AMFI creates barriers to increasing stormwater infrastructure resiliency. This problem is aggravated by rapid urbanization, aging water infrastructure, and the increasing frequency and intensity of extreme rainstorms.This research will put forward an entirely new methodology to identify causes of AMFI, innovatively integrating three key components: (i) spatio-temporal inflow variability at system-wide scales using a high-resolution stochastic rainfall model; (ii) non-dimensional indices that are predictors of AMFI, derived from state-of-the-art multiphase flow modeling; and (iii) new methods for efficient system-wide transient modeling to track the flow impulses that drive AMFI events. The research will examine the relationships between AMFI and the spatio-temporal structure of rainstorms to isolate the rainfall time and length scales that are conducive to AMFI formation. Simulated stormwater inflows will be translated into discrete impulse waves that propagate throughout the stormwater network, potentially leading to AMFI activation. The research will assess whether AMFI prediction can be achieved by representing discrete impulse waves and their interactions. AMFI activation will be modeled with computational fluid dynamics tools. Conditions for their occurrence will thus be linked to newly developed non-dimensional flow indices that can be embedded within simpler 1D system-wide stormwater models. This research will provide innovative methods for system-wide prediction of AMFI in stormwater, guiding design practices for increased resiliency to this emerging class of system failures.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.

NSF赠款将研究不良多相流相互作用（AMFI），这是在雨水系统中空气中陷入的强烈降雨事件中发生的现象鲜为人知。在快速填充事件中，此空气无法轻易逃脱和压缩，从而导致雨水“间歇泉”和入口覆盖位移等操作问题。 AMFI的频率和严重程度与雨水插入物，下水道和隧道复杂网络系统中极端暴雨的空间时间变化有关。当前的雨水设计范例和工具是为了最大程度地减少与街道洪水相关的故障或污染流量的排放而定制的，这两者都与单相水流的等级变化有关。相反，AMFI失败发生在更短的时间范围内，并涉及更复杂的两相流量条件。这意味着在传统环境中可以很好地工作的缓解措施和工具无法预料或防止AMFI失败。因此，目前的城市分配资源来修复AMFI失败，而无需理解或解决根本原因。缺乏系统级别的理解和预测AMFI的工具为提高雨水基础设施的弹性造成了障碍。快速的城市化，老化的水基础设施以及极端暴雨的频率和强度的增加来汇总这个问题。这项研究将提出一种全新的方法，以识别AMFI的原因，并创新三个关键组成部分：（i）使用高级降雨模型的系统范围的空间 - 周期性易变性。 （ii）是源自最先进的多相模型的AMFI预测指标的非二维指数； （iii）有效的系统范围瞬态建模的新方法，以跟踪驱动AMFI事件的流动冲动。该研究将研究暴风雨的AMFI与空间临时结构之间的关系，以隔离降雨时间和长度尺度，这些降雨时间和长度尺度。模拟的雨水流入将转化为离散的冲动波，这些冲动波在整个雨水网络中传播，可能导致AMFI激活。该研究将通过表示离散的脉冲波及其相互作用来评估是否可以通过表示AMFI预测。 AMFI激活将使用计算流体动力学工具进行建模。因此，其发生的条件将与新开发的非二维流量指数有关，这些指数可以嵌入更简单的一维整个雨水模型中。这项研究将为雨水中AMFI的全系统预测提供创新的方法，并指导设计实践，以提高对这一新兴系统故障类别的弹性。该奖项反映了NSF的法定使命，并通过使用基金会的知识分子优点和更广泛的审查标准评估来诚实地获得支持。