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.

河流是在景观演化中发挥重要作用的地貌特征。由于气候变化、严重干旱、洪水和人类干预导致河流景观发生变化，河流生态系统及其生态和经济价值以前所未有的方式做出反应，而其中大多数情况目前无法预测。通过准确估计流量和沉积物输送的工具来理解和预测河流系统的瞬态动力学仍然有限，部分原因是沉积物监测困难，而且还因为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来研究河流系统中湍流、泥沙输送和地貌变化之间的反馈。首席研究员和学生将在机器学习的辅助下开发和实施最先进的基于物理的模型，从而能够量化和预测现场规模河流的流量和沉积物动态。教育和推广计划与研究目标相结合，重点是（1）通过参与性写作创作科学漫画书，然后开发高中课程，让大学、本科生和研究生阶段的年轻女性参与地球科学。 ，作为加强地球科学教学和促进性别平等的工具，以及（2）通过被认为是非正式学习环境的大学艺术博物馆进行公共宣传。这项研究明确讨论了复杂的流体动力学如何在河流环境中表现出来，例如区域大流量分离，二次流、高速岩心骤降、速度反转和自由剪切层；以及宏观湍流在沉积物输送和河流形态动力学中所发挥的作用。总体目标是改变控制河流系统形态动力学变化的流体和沉积物耦合机制的基本物理的量化和预测方面的最新技术。将在从实验室到大河段的不同空间尺度上开发和测试基于物理/机器学习的混合算法以及沉积物传输和形态动力学求解器。水流形态动力学模型将使用大涡模拟（LES）技术来解决宏观湍流并预测计算域中的泥沙浓度和河床演化。基于机器学习算法的动态自适应、基于过程的域重新网格划分将用于细化湍流结构占主导地位的区域的复杂地形，这对于理解和量化再循环区和急流中存在的侵蚀和沉积过程至关重要，从而确保足够的空间尺度分辨率来表示地貌过程。一旦基本框架得到验证，就可以适应不同的河流环境，以测试其时空可迁移性。教育部分的预期社会成果侧重于：(1) 加强妇女和少数族裔的地球科学学习，(2) 改变地球科学界对妇女的陈规定型观念，以及 (3) 增加妇女在地球科学领域的代表性该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。