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

CMG 合作研究：地下流和反应输运的随机多尺度建模

• 批准号: 0620631
• 负责人: Dongxiao Zhang
• 金额: $ 23.99万
• 依托单位: University of Oklahoma Norman Campus
• 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 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0620631&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Stochastic; Multiscale

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项目的首要目标是开发数学模型和高效准确的数值算法，用于解决高度非均质多孔介质中的流动和反应输运的大规模问题。目前的项目涉及三个跨学科的地理系统建模研究小组：俄克拉荷马州大学（俄克拉荷马州），匹兹堡大学（UPitt）和得克萨斯大学奥斯汀分校（UT-Austin）。组装的研究小组建议推进必要的数学和地球科学基础，以提高模拟器的预测能力，通过更好地了解在多个空间和时间尺度上管理地下现象的物理过程。目标应用包括复杂地质系统的可靠和有效建模，不确定性评估以及随机和确定性模型的有效耦合。本计画的主要目标为：（1）发展与分析新的离散化方法，以估计随机系统的物理特性与统计特性;（2）建立多尺度随机问题的模型，以量化异质性中的大尺度不确定性与子域系统参数中的小尺度不确定性;（3）研究了迭代耦合和时间步进方法，以提高随机反应输运的精度和效率;（4）大规模的数值实现随机问题的尺度求解方法，涉及Monte Carlo模拟和传统矩展开的有效替代方法。人类与各种自然和工程环境相互作用，（b）在全球地质系统中，包括垃圾填埋场、受污染场地、含水层和化石燃料库。因此，了解和模拟复杂的地理系统是管理和优化环境清洁和能源生产活动的关键。这CMG的努力提供了可能性，学术，政府和工业合作领域的大规模地理系统的不确定性评估与驱动软件商业化的潜力。此外，在该项目下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的环境中对密歇根大学、匹兹堡大学和德州大学奥斯汀分校的本科生、研究生和博士后研究员进行培训。