Potential impact of pore-scale incomplete mixing on biodegradation in aquifers: From batch experiment to field-scale modeling

Kang, Peter K.; Bresciani, Étienne; An, Seongnam; Lee, Seunghak

doi:10.1016/j.advwatres.2018.10.026

Cited by 24 publications

(12 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many existing reactive transport modeling approaches assume perfect mixing at some scale, while in reality incomplete mixing of reactive species occurs below this scale. The effects of this have been observed in theory [5][6][7][8], numerical modeling [5][6][7]9,10], laboratory experiments [11][12][13][14][15], and field studies [16][17][18]. Incomplete mixing typically results in an overestimation of the amount of reaction that will occur, presenting the need to artificially, and often non-physically, alter the effective reaction rate used in the model to better match observations [19].…”

Section: Introductionmentioning

confidence: 99%

Upscaling Mixing in Highly Heterogeneous Porous Media via a Spatial Markov Model

Wright

Sund

Richter

et al. 2018

Water

View full text Add to dashboard Cite

In this work, we develop a novel Lagrangian model able to predict solute mixing in heterogeneous porous media. The Spatial Markov model has previously been used to predict effective mean conservative transport in flows through heterogeneous porous media. In predicting effective measures of mixing on larger scales, knowledge of only the mean transport is insufficient. Mixing is a small scale process driven by diffusion and the deformation of a plume by a non-uniform flow. In order to capture these small scale processes that are associated with mixing, the upscaled Spatial Markov model must be extended in such a way that it can adequately represent fluctuations in concentration. To address this problem, we develop downscaling procedures within the upscaled model to predict measures of mixing and dilution of a solute moving through an idealized heterogeneous porous medium. The upscaled model results are compared to measurements from a fully resolved simulation and found to be in good agreement.

show abstract

Section: Introductionmentioning

confidence: 99%

Upscaling Mixing in Highly Heterogeneous Porous Media via a Spatial Markov Model

Wright

Sund

Richter

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…While coarse-scale concentrations of nonreactive chemicals may agree reasonably well with the ADE under certain conditions (Dagan, 1984), concentration fluctuations may still occur at the local scale. These local-scale fluctuations, which are not explicitly accounted for in classical formulations, may drive the outcome of nonlinear processes, such as chemical reactions, far from what would be predicted by the ADE (Kang et al, 2019). Note that by using Equation 2 (or similar stochastic formulations), where each particle follows its own unique random path, it is implied that at any given time each particle is sampling the local-scale fluid velocity field independently.…”

Section: Water Resources Researchmentioning

confidence: 99%

Lagrangian Modeling of Mixing‐Limited Reactive Transport in Porous Media: Multirate Interaction by Exchange With the Mean

Sole‐Mari

Fernàndez-García

Sánchez-Vila

et al. 2020

Water Resources Research

View full text Add to dashboard Cite

The presence of solute concentration fluctuations at spatial scales much below the scale of resolution is a major challenge for modeling reactive transport in porous media. Overlooking small‐scale fluctuations, which is the usual procedure, often results in strong disagreements between field observations and model predictions, including, but not limited to, the overestimation of effective reaction rates. Existing innovative approaches that account for local reactant segregation do not provide a general mathematical formulation for the generation, transport, and decay of these fluctuations and their impact on chemical reactions. We propose a Lagrangian formulation based on the random motion of fluid particles carrying solute concentrations whose departure from the local mean is relaxed through multirate interaction by exchange with the mean (MRIEM). We derive and analyze the macroscopic descriptionof the local concentration covariance that emerges from the model, showing its potential to simulate the dynamics of mixing‐limited processes. The action of hydrodynamic dispersion on coarse‐scale concentration gradients is responsible for the production of local concentration covariance, whereas covariance destruction stems from the local mixing process represented by the MRIEM formulation. The temporal evolution of integrated mixing metrics in two simple scenarios shows the trends that characterize fully resolved physical systems, such as a late‐time power law decay of the relative importance of incomplete mixing with respect to the total mixing. Experimental observations of mixing‐limited reactive transport are successfully reproduced by the model.

show abstract

“…The discrepancies between well-mixed reaction rates and effective reaction rates are known to be caused by both geochemical and physical heterogeneities of porous media systems 19,[21][22][23][24] . Geochemical heterogeneity originates from the variety of minerals and complexity in chemical reactions [25][26][27][28][29][30][31] , while physical heterogeneity is caused by the structural heterogeneity of porous media, which controls fluid flow and mass transfer 22,[32][33][34][35][36][37][38][39][40][41] .…”

Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Liu

Kwon

Kang

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.

show abstract

Potential impact of pore-scale incomplete mixing on biodegradation in aquifers: From batch experiment to field-scale modeling

Cited by 24 publications

References 81 publications

Upscaling Mixing in Highly Heterogeneous Porous Media via a Spatial Markov Model

Upscaling Mixing in Highly Heterogeneous Porous Media via a Spatial Markov Model

Lagrangian Modeling of Mixing‐Limited Reactive Transport in Porous Media: Multirate Interaction by Exchange With the Mean

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Contact Info

Product

Resources

About