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

CMG“合作研究”：“地下流动和反应输运的随机多尺度建模”

• 批准号: 0618679
• 负责人: Mary Wheeler
• 金额: --
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0618679&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Stochastic; Mulstiscale

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.

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，以解决高度异质多孔介质中的大规模流动和反应输运问题。目前的项目涉及三个跨学科地球系统建模研究小组：俄克拉荷马大学 (OU)、匹兹堡大学 (UPitt) 和德克萨斯大学奥斯汀分校 (UT-Austin)。组成的研究团队建议通过更好地了解控制多个空间和时间尺度的地下现象的物理过程，推进增强模拟器预测能力所需的数学和地球科学基础。目标应用包括复杂地球系统的可靠且高效的建模、不确定性评估以及随机和确定性模型的有效耦合。该项目旨在取得以下成果：（1）开发和分析用于估计随机系统物理特性和统计的新颖离散方法； (2) 多尺度随机问题建模，用于量化异质性的大尺度不确定性和子域系统参数的小尺度不确定性； (3) 研究迭代耦合和时间步进以提高随机反应输运的准确性和效率； (4) 随机问题大规模求解方法的数值实现，涉及蒙特卡洛模拟和传统矩展开的有效替代方法。人类与广泛的自然和工程地球系统相互作用，包括垃圾填埋场、污染场地、含水层和化石燃料库。因此，理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至关重要。 CMG 的这项工作为大规模地球系统不确定性评估领域的学术、政府和工业合作提供了可能性，并具有推动软件商业化的潜力。此外，该项目开发的预测和计算工具将影响当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对俄勒冈大学、匹兹堡大学和德州大学奥斯汀分校的本科生、研究生和博士后进行培训。