CMG Collaborative Research: Stochastic Multiscale Modeling of Subsurface Flow and Reactive Transport

该CMG项目的总体目标是开发数学模型和高效、准确的数值算法来解决高非均质多孔介质中的流动和反应运移的大规模问题。本项目涉及三个跨学科的地球系统建模研究小组：俄克拉何马大学(OU)、匹兹堡大学(UPitt)和德克萨斯大学奥斯汀分校(UT-Austin)。召集起来的研究小组提议，通过更好地了解在多个空间和时间尺度上支配地下现象的物理过程，推进必要的数学和地球科学基础，以加强模拟器的预测能力。目标应用包括可靠和高效的复杂地质系统建模、不确定性评估以及随机模型和确定性模型的有效耦合。该项目旨在实现以下成果：(1)开发和分析用于估计随机系统的物理特征和统计的新的离散化方法；(2)多尺度随机问题的建模，以量化非均匀中的大尺度不确定性和子域系统参数中的小尺度不确定性；(3)研究迭代耦合和时间步进，以提高随机反应传输的精度和效率；以及(4)随机问题的大规模求解方法的数值实现，涉及蒙特卡罗模拟和传统矩展开的有效替代方法。人类与包括垃圾填埋场、污染场地、含水层和化石燃料库在内的广泛的自然和工程地质系统相互作用。因此，了解和模拟复杂的地球系统对于管理和优化环境清理和能源生产活动是至关重要的。CMG的这一努力提供了在大规模地球系统不确定性评估领域进行学术、政府和行业合作的可能性，并有可能推动软件商业化。此外，在该项目下开发的预测和计算工具将影响目前对各种系统的科学理解，包括生物组织、大气、多孔复合材料和智能材料。拟议的研究将涉及在跨学科环境中培训俄亥俄州立大学、德克萨斯大学皮特分校和德克萨斯大学奥斯汀分校的本科生、研究生和博士后研究员。

The overarching objective of this CMG project is to develop mathematical models and efficient and accurate numerical algorithms for solving large-scale problems of flow and reactive transport in highly heterogeneous porous media. The present project involves three interdisciplinary geosystems modeling research groups: The University of Oklahoma (OU), University of Pittsburgh (UPitt), and The University of Texas at Austin (UT-Austin). The assembled research team proposes to advance the mathematical and geoscience foundations necessary to enhance the predictive capabilities of simulators through an improved understanding of the physical processes that govern subsurface phenomena on multiple spatial and temporal scales. Target applications include reliable and efficient modeling of complex geosystems, uncertainty assessment and the effective coupling of stochastic and deterministic models. The project aims to achieve the following results: (1) development and analysis of novel discretization methods for estimating physical characteristics and statistics of stochastic systems; (2) modeling of multiscale stochastic problems for quantifying large-scale uncertainty in heterogeneity and small-scale uncertainty in subdomain system parameters; (3) investigation of iterative coupling and time stepping for improving accuracy and efficiency of stochastic reactive transport; and (4) numerical implementation of large-scale solution methods for stochastic problems involving efficient alternative methods to both Monte Carlo simulations and traditional moment expansions.Humankind interacts with a broad range of natural and engineered geosystems, including landfills, contaminated sites, aquifers and fossil fuel reservoirs. Therefore, understanding and simulation of complex geosystems are essential in managing and optimizing environmental cleanup and energy production activities. This CMG effort offers the possibility of academic, governmental, and industrial collaboration in the area of large-scale geosystems uncertainty assessment with the potential of driving software commercialization. Moreover, the predictive and computational tools developed under this project will impact current scientific understanding of a diverse array of systems, including biological tissues, the atmosphere, porous composite materials, and smart materials. The proposed research will involve the training of undergraduates, graduate students and post-doctoral fellows at OU, UPitt and UT-Austin, in an interdisciplinary environment.