Collaborative Research: Hybrid Modeling of Reactive Transport in Porous and Fractured Media

合作研究：多孔和断裂介质中反应输运的混合建模

• 批准号: 1246315
• 负责人: Daniel Tartakovsky
• 金额: $ 20万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1246315&HistoricalAwards=false
• 关键词: Collaborative; Research; Hybrid; Modeling; Reactive

Subsurface flow and transport take place in complex heterogeneous environments that exhibit a hierarchy of scales. More often than not, physical and bio-geochemical phenomena on one scale (e.g., a pore scale) affect, and are coupled to phenomena on a vastly different scale (e.g., a field scale). Such phenomena defy coarse-scale continuum descriptions, since they exhibit high localization (e.g., propagation of reactive fronts and biofilm growth) and/or strong nonlinear coupling between the processes involved (e.g., dynamic changes in porosity and permeability due to dissolution or precipitation). Hybrid numerical algorithms are to be used when coarse-scale continuum models fail to accurately describe a physical phenomenon in a small part of a computational domain. Accurate and efficient coupling of two (or more) models operating on vastly different spatial and/or temporal scales in a hybrid remains a major theoretical and computational challenge. A key to the success of a hybrid method is an efficient implementation of coupling conditions on the interface between its constitutive models. Additionally, uncertainty about pore geometry undermines the veracity of pore-scale simulations and, hence, of hybrid simulations of which they are a constitutive part. Overreaching goals of the proposed activity are to establish a theoretical foundation for hybrid modeling of subsurface flow and transport, to develop corresponding numerical algorithms, and to provide computational tools for robust uncertainty quantification in hybrid models. To achieve these goals, the investigators will develop hybrid algorithms for reactive flows in fractured and porous media, quantify uncertainty in pore-scale geometry in hybrid simulations, and experimentally validate hybrid simulations. In terms of broader impact, this proposal will enhance sustainability of essential water resources. Groundwater is a principal source of domestic water supply in the United States, and it is a major source of fresh water for industrial, agricultural and public uses. By establishing a novel modeling framework, this proposal will provide a scientific basis for reliable predictions of impacts of land use change and climate change, and more accurate assessments of groundwater contamination risks. The methods and algorithms developed in the course of the proposed activity will lay a solid foundation for improved quantitative understanding of subsurface processes with tightly coupled pore- and field-scales. The proposal will support the efforts of graduate students. The investigators will actively promote STEM career paths of underrepresented groups, and establish an outreach K-12 program dedicated to providing an intensive college prep education for motivated low-income students.

地下水流和输运发生在复杂的非均质环境中，这种环境呈现出尺度层次结构。通常情况下，一个尺度上的物理和生物地球化学现象（例如，孔隙尺度）影响，并且与在非常不同的尺度上的现象相关联（例如，场尺度）。这种现象无视粗尺度连续统描述，因为它们表现出高度的局部化（例如，反应前沿的传播和生物膜生长）和/或所涉及的过程之间的强非线性耦合（例如，由于溶解或沉淀导致的孔隙度和渗透率的动态变化）。当粗尺度连续体模型不能准确描述计算域的一小部分中的物理现象时，将使用混合数值算法。精确和有效地耦合两个（或多个）模型在不同的空间和/或时间尺度上的混合仍然是一个重大的理论和计算挑战。混合方法成功的关键是在其本构模型之间的界面上有效地实现耦合条件。此外，孔隙几何形状的不确定性破坏了孔隙尺度模拟的准确性，因此，混合模拟，其中它们是一个组成部分。拟议活动的超越目标是为地下水流和输运的混合建模建立理论基础，开发相应的数值算法，并为混合模型中的鲁棒不确定性量化提供计算工具。为了实现这些目标，研究人员将开发裂缝和多孔介质中反应流的混合算法，量化混合模拟中孔隙尺度几何形状的不确定性，并通过实验验证混合模拟。就更广泛的影响而言，这一建议将加强基本水资源的可持续性。地下水是美国国内供水的主要来源，也是工业、农业和公共用途淡水的主要来源。通过建立一个新的模型框架，该建议将提供可靠的预测土地利用变化和气候变化的影响，更准确地评估地下水污染风险的科学依据。在拟议的活动过程中开发的方法和算法将奠定坚实的基础，以改善定量了解地下过程与紧密耦合的孔隙和现场规模。该提案将支持研究生的努力。调查人员将积极促进代表性不足的群体的STEM职业道路，并建立一个外展K-12计划，致力于为有动力的低收入学生提供密集的大学预科教育。