Research Topic

Pore-scale Microstructure, Mechanisms, and Models for Subsurface Flow and Transport

About this Research Topic

The microscopic structure of soil, rock and other geological material strongly influences the behavior of groundwater systems, geologic carbon sequestration, and other applications in subsurface flow and transport. Experimental and computational advancements now support a wide range of studies that explicitly link the microscopic structure of materials with both physical mechanisms and larger scale flow and transport behaviors. Predictive pore-scale studies to understand and model displacement and transport rely on geometric information obtained from experimental imaging. As new capabilities are developed, quantitative measurements can be made to inform understanding in a variety of ways. Pore-scale studies can provide insight into the operative mechanisms for particular processes, as well as serving as a tool to directly upscale results from the smaller scale to a larger one.

This Research Topic is focused on methods to advance pore-scale studies for applications to water resources systems. Key sources of uncertainty arise in pore-scale models due to experimental conditions, lack of information with respect to the chemical and physical properties for geological materials, errors due to data analysis, image processing, numerical modeling errors, and approximations that are introduced in the derivation of models that apply at various scales. As pore-scale methods become increasingly mature, efforts to reduce the associated uncertainties become more critical to support their application to a wider range of use cases.

This collection invites submissions that rely on pore-scale studies to advance the understanding of water resources systems. We particularly encourage submissions that address key sources of uncertainty and extend the applicability of pore-scale methods to new areas. Example topics of interest are:

● Experimental methods and analytical approaches to better describe the pore structure of geological materials and characterize associated physical and surface properties
● Approaches to quantify and reduce uncertainties that arise due to data processing
● Applications of machine learning / artificial intelligence to pore-scale systems
● Pore-scale modeling studies that provide insights into the physical behavior of systems
● Theoretical or computational approaches to upscale information from the pore-scale and inform larger-scale models


Keywords: Porous medium, pore-scale, Subsurface Flow, transport, Pore-scale modeling, porous medium systems, fluid flow


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The microscopic structure of soil, rock and other geological material strongly influences the behavior of groundwater systems, geologic carbon sequestration, and other applications in subsurface flow and transport. Experimental and computational advancements now support a wide range of studies that explicitly link the microscopic structure of materials with both physical mechanisms and larger scale flow and transport behaviors. Predictive pore-scale studies to understand and model displacement and transport rely on geometric information obtained from experimental imaging. As new capabilities are developed, quantitative measurements can be made to inform understanding in a variety of ways. Pore-scale studies can provide insight into the operative mechanisms for particular processes, as well as serving as a tool to directly upscale results from the smaller scale to a larger one.

This Research Topic is focused on methods to advance pore-scale studies for applications to water resources systems. Key sources of uncertainty arise in pore-scale models due to experimental conditions, lack of information with respect to the chemical and physical properties for geological materials, errors due to data analysis, image processing, numerical modeling errors, and approximations that are introduced in the derivation of models that apply at various scales. As pore-scale methods become increasingly mature, efforts to reduce the associated uncertainties become more critical to support their application to a wider range of use cases.

This collection invites submissions that rely on pore-scale studies to advance the understanding of water resources systems. We particularly encourage submissions that address key sources of uncertainty and extend the applicability of pore-scale methods to new areas. Example topics of interest are:

● Experimental methods and analytical approaches to better describe the pore structure of geological materials and characterize associated physical and surface properties
● Approaches to quantify and reduce uncertainties that arise due to data processing
● Applications of machine learning / artificial intelligence to pore-scale systems
● Pore-scale modeling studies that provide insights into the physical behavior of systems
● Theoretical or computational approaches to upscale information from the pore-scale and inform larger-scale models


Keywords: Porous medium, pore-scale, Subsurface Flow, transport, Pore-scale modeling, porous medium systems, fluid flow


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 December 2020 Abstract
01 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 December 2020 Abstract
01 April 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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