Collaborative Research: Adverse Multiphase Flow Interactions in Urban Stormwater Systems

NSF的这笔拨款将调查不利的多相流相互作用(AMFI)，这是一种在暴雨期间发生的鲜为人知的现象，这种现象是由雨水系统中的空气被困引起的。在快速灌装过程中，这些空气不能很容易地逸出和压缩，导致操作问题，如暴雨“间歇泉”和入口盖子移位。AMFI的频率和严重性与极端暴雨的时空变异性有关，这些极端暴雨在由雨水入口、下水道和隧道组成的复杂网络系统内相互作用。目前的雨水设计范例和工具都是为尽量减少与街道洪水或受污染水流排放相关的故障而量身定做的，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，而且涉及更复杂的两相流条件。这意味着，在传统环境中运行良好的缓解措施和工具无法预测或防止AMFI失败。因此，城市目前分配资源来修复AMFI故障，而不了解或解决根本原因。缺乏系统层面的理解和预测AMFI的工具，对提高雨水基础设施的复原力造成了障碍。这项研究将提出一种全新的方法来识别AMFI的原因，创新性地集成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入流量变异性；(Ii)从最先进的多相流模拟得出的作为AMFI预测因子的无量纲指数；以及(Iii)有效的系统范围内暂态模拟的新方法，以跟踪驱动AMFI事件的流动脉冲。这项研究将研究AMFI与暴雨的时空结构的关系，以分离出有利于AMFI形成的降雨时间和长度尺度。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。这项研究将评估是否可以通过表示离散的脉冲波及其相互作用来实现AMFI预测。AMFI的激活将用计算流体力学工具模拟。因此，它们的发生条件将与新开发的无量纲流指数联系在一起，这些无量纲流指数可以嵌入更简单的一维全系统雨水模型中。这项研究将为暴雨中AMFI的系统范围预测提供创新方法，指导设计实践，以提高对这类新兴系统故障的弹性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.