权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

基本信息

批准号：
2049025
负责人：
Ben Hodges
金额：
$ 31.94万
依托单位：
University of Texas at Austin
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=2049025&HistoricalAwards=false
关键词：
Collaborative Research Adverse Multiphase Flow

项目摘要

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.

该基金会将研究逆向多相流相互作用（AMFI），这是一种在暴雨期间发生的知之甚少的现象，这种现象是由雨水系统中的空气滞留引起的。在快速填充事件期间，该空气不能容易地逸出和压缩，从而导致诸如雨水“间歇泉”和入口盖移位的操作问题。AMFI的频率和严重程度与极端暴雨在雨水入口、下水道和隧道等复杂网络系统内相互作用的时空变异性有关。目前的雨水设计范例和工具已经针对尽量减少与街道洪水或污染流排放相关的故障，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，并且涉及更复杂的两相流条件。这意味着，在传统环境下行之有效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解决AMFI故障，而不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。该研究将提出一种全新的方法来识别AMFI的原因，创新地整合三个关键组成部分：（i）使用高分辨率随机降雨模型在系统范围内的时空入流变化;（ii）作为AMFI的预测因子的无量纲指数，其源自最先进的多相流建模;以及（iii）用于有效的全系统瞬态建模以跟踪驱动AMFI事件的流脉冲的新方法。本研究将探讨AMFI与暴雨时空结构的关系，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。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。将使用计算流体动力学工具对AMFI激活进行建模。因此，其发生的条件将被链接到新开发的无量纲流量指数，可以嵌入到更简单的一维系统范围内的雨水模型。这项研究将提供创新的方法，为系统范围内的预测AMFI在暴雨water.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的系统failure. This类的弹性增加设计实践。