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.

美国国家科学基金会的这项拨款将用于研究不利的多相流相互作用（AMFI），这是一种人们知之甚少的现象，这种现象在强降雨期间发生，是由于雨水系统中的空气滞留而引起的。在快速充水事件期间，空气无法轻易逸出和压缩，从而导致操作问题。 AMFI 的频率和严重程度与雨水入口、下水道和排水系统的复杂网络系统中相互作用的极端暴雨的时空变化有关。当前的雨水设计范例和工具是为了最大限度地减少与街道洪水或污染水流排放相关的故障而设计的，这两者都与单相水流的逐渐变化有关，相比之下，AMFI 故障发生的时间要短得多。时间框架并涉及更复杂的两相流条件，这意味着在传统环境下有效的缓解措施和工具无法预测或防止 AMFI 故障，目前在不了解或解决缺乏的根本原因的情况下分配资源来修复 AMFI 故障。系统级的理解和预测 AMFI 的工具为提高雨水基础设施的弹性设置了障碍，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加剧了这个问题。这项研究将提出一种全新的方法来识别。 AMFI 的原因，创新性地整合了三个关键组成部分：（i）使用高分辨率随机降雨模型在系统范围内的时空流入变化；（ii）作为预测因子的无量纲指数； AMFI，源自最先进的多相流建模；以及（iii）用于跟踪驱动 AMFI 事件的流脉冲的有效全系统瞬态建模的新方法。研究将评估暴雨的时间结构，以隔离有利于 AMFI 形成的降雨时间和长度尺度。是否可以通过表示离散脉冲波及其相互作用来实现 AMFI 预测，并将使用计算流体动力学工具对其发生条件进行建模，从而将其与新开发的无量纲流量指数联系起来，这些指数可以嵌入到更简单的一维系统中。这项研究将为雨水中 AMFI 的全系统预测和设计实践提供指导性创新方法，以提高对此类新兴系统故障的弹性。该奖项反映了 NSF 的法定使命，并通过评估被认为值得支持。使用基金会的智力价值和更广泛的影响审查标准。