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1. Harnessing elastic instabilities for enhanced mixing and reaction kinetics in porous media

   https://doi.org/10.1073/pnas.2320962121  

   Browne, Christopher A; Datta, Sujit S (July 2024, Proceedings of the National Academy of Sciences)

   Turbulent flows have been used for millennia to mix solutes; a familiar example is stirring cream into coffee. However, many energy, environmental, and industrial processes rely on the mixing of solutes in porous media where confinement suppresses inertial turbulence. As a result, mixing is drastically hindered, requiring fluid to permeate long distances for appreciable mixing and introducing additional steps to drive mixing that can be expensive and environmentally harmful. Here, we demonstrate that this limitation can be overcome just by adding dilute amounts of flexible polymers to the fluid. Flow-driven stretching of the polymers generates an elastic instability, driving turbulent-like chaotic flow fluctuations, despite the pore-scale confinement that prohibits typical inertial turbulence. Using in situ imaging, we show that these fluctuations stretch and fold the fluid within the pores along thin layers (“lamellae”) characterized by sharp solute concentration gradients, driving mixing by diffusion in the pores. This process results in a $3\times$reduction in the required mixing length, a $6\times$increase in solute transverse dispersivity, and can be harnessed to increase the rate at which chemical compounds react by $5\times$—enhancements that we rationalize using turbulence-inspired modeling of the underlying transport processes. Our work thereby establishes a simple, robust, versatile, and predictive way to mix solutes in porous media, with potential applications ranging from large-scale chemical production to environmental remediation. 

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2. Machine learning to predict effective reaction rates in 3D porous media from pore structural features

   https://doi.org/10.1038/s41598-022-09495-0  

   Liu, Min; Kwon, Beomjin; Kang, Peter K. (March 2022, Scientific Reports)

   Abstract 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. 

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3. Flow of Non-Newtonian Fluids in a Single-Cavity Microchannel

   https://doi.org/10.3390/mi12070836  

   Raihan, Mahmud Kamal; Jagdale, Purva P.; Wu, Sen; Shao, Xingchen; Bostwick, Joshua B.; Pan, Xinxiang; Xuan, Xiangchun (July 2021, Micromachines)

   Having a basic understanding of non-Newtonian fluid flow through porous media, which usually consist of series of expansions and contractions, is of importance for enhanced oil recovery, groundwater remediation, microfluidic particle manipulation, etc. The flow in contraction and/or expansion microchannel is unbounded in the primary direction and has been widely studied before. In contrast, there has been very little work on the understanding of such flow in an expansion–contraction microchannel with a confined cavity. We investigate the flow of five types of non-Newtonian fluids with distinct rheological properties and water through a planar single-cavity microchannel. All fluids are tested in a similarly wide range of flow rates, from which the observed flow regimes and vortex development are summarized in the same dimensionless parameter spaces for a unified understanding of the effects of fluid inertia, shear thinning, and elasticity as well as confinement. Our results indicate that fluid inertia is responsible for developing vortices in the expansion flow, which is trivially affected by the confinement. Fluid shear thinning causes flow separations on the contraction walls, and the interplay between the effects of shear thinning and inertia is dictated by the confinement. Fluid elasticity introduces instability and asymmetry to the contraction flow of polymers with long chains while suppressing the fluid inertia-induced expansion flow vortices. However, the formation and fluctuation of such elasto-inertial fluid vortices exhibit strong digressions from the unconfined flow pattern in a contraction–expansion microchannel of similar dimensions. 

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4. Fluid Rheological Effects on the Flow of Polymer Solutions in a Contraction–Expansion Microchannel

   https://doi.org/10.3390/mi11030278  

   Jagdale, Purva P.; Li, Di; Shao, Xingchen; Bostwick, Joshua B.; Xuan, Xiangchun (March 2020, Micromachines)

   A fundamental understanding of the flow of polymer solutions through the pore spaces of porous media is relevant and significant to enhanced oil recovery and groundwater remediation. We present in this work an experimental study of the fluid rheological effects on non-Newtonian flows in a simple laboratory model of the real-world pores—a rectangular sudden contraction–expansion microchannel. We test four different polymer solutions with varying rheological properties, including xanthan gum (XG), polyvinylpyrrolidone (PVP), polyethylene oxide (PEO), and polyacrylamide (PAA). We compare their flows against that of pure water at the Reynolds ( R e ) and Weissenburg ( W i ) numbers that each span several orders of magnitude. We use particle streakline imaging to visualize the flow at the contraction–expansion region for a comprehensive investigation of both the sole and the combined effects of fluid shear thinning, elasticity and inertia. The observed flow regimes and vortex development in each of the tested fluids are summarized in the dimensionless W i − R e and χ L − R e parameter spaces, respectively, where χ L is the normalized vortex length. We find that fluid inertia draws symmetric vortices downstream at the expansion part of the microchannel. Fluid shear thinning causes symmetric vortices upstream at the contraction part. The effect of fluid elasticity is, however, complicated to analyze because of perhaps the strong impact of polymer chemistry such as rigidity and length. Interestingly, we find that the downstream vortices in the flow of Newtonian water, shear-thinning XG and elastic PVP solutions collapse into one curve in the χ L − R e space. 

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5. Speeding-up Simulation of Multiphase Flow in Digital Images of Heterogeneous Porous Media by Curvelet Transformation

   https://doi.org/10.1007/s11242-021-01559-5  

   Aljasmi, Abdullah; Sahimi, Muhammad (March 2021, Transport in Porous Media)

   Blunt, MJ (Ed.)

   Multiphase fluid flow in porous media is important to a wide variety of processes of fundamental scientific and practical importance. Developing a model for the pore space of porous media represents the first step for simulating such flows. With rapid increase in the computation power and advances in instrumentation and imaging processes, it has become feasible to carry out simulation of multiphase flow in two- and three-dimensional images of porous media, hence dispensing with development of models of pore space that are based on approximating their morphology. Image-based simulations are, however, very time consuming. We describe an approach for speeding-up image-based simulation of multiphase flow in porous media based on curvelet transformations, which are specifically designed for processing of images that contain complex curved surfaces. Most porous media contain correlations in their morphology and, therefore, their images carry redundant information that, in the curvelet transform space, can be removed efficiently and accurately in order to obtain a coarser image with which the computations are far less intensive. We utilize the methodology to simulate two-phase flow of oil and water in two-dimensional digital images of sandstone and carbonate samples, and demonstrate that while the results with the curvelet-processed images are as accurate as those with the original ones, the computations are speeded up by a factor of 110–150. Thus, the methodology opens the way toward achieving the ultimate goal of simulation of multiphase flow in porous media, namely, making image-based computations a standard practice. 

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