Collaborative Research: Adverse Multiphase Flow Interactions in Urban Stormwater Systems

这项NSF拨款将用于研究由雨水系统中空气夹持引起的暴雨期间发生的不利多相流相互作用（AMFI），这是一种鲜为人知的现象。在快速填充过程中，这些空气不能轻易地逸出并被压缩，从而导致诸如雨水“间歇泉”和入口盖位移等操作问题。极端暴雨的频率和严重程度与极端暴雨的时空变异性有关，极端暴雨在雨水入口、下水道和隧道的复杂网络系统中相互作用。目前的雨水设计范例和工具都是为了尽量减少与街道洪水或污染流排放相关的故障，这两种情况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，并且涉及更复杂的两相流条件。这意味着在传统情况下有效的缓解措施和工具无法预测或防止AMFI故障。因此，城市目前分配资源来解决AMFI失败，而没有了解或解决根本原因。缺乏系统级的理解和预测AMFI的工具，这对提高雨水基础设施的弹性造成了障碍。快速的城市化、老化的水利基础设施以及日益频繁和强烈的极端暴雨加剧了这一问题。本研究将提出一种全新的方法来确定AMFI的原因，创新地整合三个关键组成部分：(i)利用高分辨率随机降雨模型在全系统尺度上的时空入流变化；（ii）从最先进的多相流模型推导出的预测AMFI的无量纲指数；（iii）高效的全系统瞬态建模新方法，以跟踪驱动AMFI事件的流脉冲。研究AMFI与暴雨时空结构的关系，分离有利于AMFI形成的降雨时间尺度和降雨长度尺度。模拟的雨水流入将转化为离散的脉冲波，在整个雨水网络中传播，可能导致AMFI激活。该研究将评估AMFI预测是否可以通过表示离散脉冲波及其相互作用来实现。AMFI激活将使用计算流体动力学工具进行建模。因此，它们发生的条件将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范围的雨水模型中。这项研究将为雨水中AMFI的系统范围预测提供创新方法，指导设计实践，以增加对这类新兴系统故障的弹性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.