CAREER: Understanding the Physics of Turbulent Flow, Erosion and Depositional Patterns in River Systems

职业：了解河流系统中湍流、侵蚀和沉积模式的物理原理

• 批准号: 2239550
• 负责人: Laura Alvarez
• 金额: $ 55.24万
• 依托单位: University of Texas at El Paso
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239550&HistoricalAwards=false
• 关键词: CAREER; Understanding; Physics; Turbulent; Flow

Rivers are geomorphologic features that play an essential role in landscape evolution. As the river landscape changes due to climate change, severe droughts, floods, and human interventions, the fluvial ecosystems and their ecological and economic values respond in unprecedented ways, and the majority of these cases cannot currently be predicted. Understanding and predicting transient dynamics in river systems through tools that accurately estimate flow and sediment transport is still limited, partially because of the difficulty of monitoring sediment but also because of the inability to understand the fluid dynamics. This work aims to provide a theoretical and numerical framework to study the feedback between turbulent flow, sediment transport, and geomorphologic changes in river systems. The principal investigator and students will develop and implement state-of-the-art physically-based models aided by machine learning that allow the quantification and forecasting of the flow and sediment dynamics in field-scale rivers. The education and outreach plan, integrated with the research objectives, focuses on (1) engaging young women at college, undergraduate, and graduate levels into Earth science, through participatory writing for the creation of a science comic book, followed by high school curriculum development, as tools to enhance Earth science pedagogy and promote gender equity, and (2) public outreach through the university art museum that is considered to be an informal learning environment.This study addresses explicitly how convoluted fluid dynamics manifest in fluvial environments, such as regions of massive flow separation, secondary flows, high-velocity core plunges, velocity inversions, and free shear layers; and the role played by macro-turbulence in sediment transport and river morpho-dynamics. The overall objective is to transform the state of the art in quantifying and predicting the fundamental physics of the coupled fluid and sediment mechanisms that control the morpho-dynamic changes in fluvial systems. A hybrid physics-based/ machine learning algorithm coupled with a sediment transport and morphodynamic solver will be developed and tested at different spatial scales, from laboratory to large river reaches. The hydro-morphodynamic model will use the Large Eddy Simulation (LES) techniques to resolve macro-turbulence and predict the sediment concentration and riverbed evolution in the computational domain. A dynamically adaptive, process-based domain re-meshing, based on machine learning algorithms, will be applied to refine the complex topography in areas where turbulent structures are dominant and fundamental to understanding and quantifying erosion and depositional processes present in recirculation zones and plunging flows, thus ensuring a sufficient spatial scale resolution to represent geomorphologic processes. Once the fundamental framework is validated, it could be adapted to different river environments to test its spatio-temporal transferability. The expected societal outcomes of the educational component are focused on: (1) enhancing Earth science learning among women and racial minorities, (2) modifying stereotypes of women in the Earth science community, and (3) increasing the representation of women in Earth science and creating new literacy in gender equity.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.

河流是地貌特征，在景观演化中起着至关重要的作用。随着由于气候变化，严重的干旱，洪水和人类干预的变化，河流生态系统及其生态和经济价值观以空前的方式做出反应，目前无法预测这些病例的大多数。通过准确估计流量和沉积物传输的工具，理解和预测河流系统中的瞬时动态仍然有限，部分是由于难以监测沉积物，也是由于无法理解流体动力学。这项工作旨在提供一个理论和数值框架，以研究河流系统中湍流，沉积物传输和地貌变化之间的反馈。首席研究员和学生将通过机器学习的帮助，开发和实施基于物理的最先进的模型，从而可以量化和预测现场尺度河流中的流量和沉积物动态。与研究目标融合的教育和宣传计划专注于（1）通过参与式写作创建一本科学漫画书的参与式写作，使年轻女性吸引年轻妇女进入地球科学，并以高中课程的发展为工具，以增强地球科学教学教育学并促进性别公众的启发性，并促进了如何促进性别公众，并促进了该学术博物馆的范围。复杂的流体动力学表现在河流环境中，例如大规模流动分离，次要流，高速核心跌落，速度反演和游离剪切层；宏扰动在沉积物运输和河流形态动力学中所扮演的角色。总体目的是在量化和预测控制河流系统中形态动力变化的耦合流体和沉积物机制的基本物理学方面改变最新技术的状态。将在不同的空间尺度上开发和测试一种基于混合物理学/机器学习算法，再加上沉积物传输和形态动力求解器，从实验室到大河流范围。水型型动力学模型将使用大型涡流模拟（LES）技术来解决宏扰动，并预测计算域中的沉积物浓度和河床进化。基于机器学习算法的动态自适应，基于过程的域重新介绍将应用于在湍流结构在理解和量化再循环区域中存在和量化沉积过程中占主导地位和基础的区域的复杂地形，从而确保了足够的空间尺度分辨率，以代表地层级别的过程。一旦验证了基本框架，它就可以适应不同的河流环境来测试其时空可转移性。 The expected societal outcomes of the educational component are focused on: (1) enhancing Earth science learning among women and racial minorities, (2) modifying stereotypes of women in the Earth science community, and (3) increasing the representation of women in Earth science and creating new literacy in gender equity.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审查标准。