Fluid inertia controls mineral precipitation and clogging in pore to network-scale flows.

Mineral precipitation caused by fluid mixing presents complex control and predictability challenges in a variety of natural and engineering processes, including carbon mineralization, geothermal energy, and microfluidics. Precipitation dynamics, particularly under the influence of fluid flow, remain poorly understood. Combining microfluidic experiments and three-dimensional reactive transport simulations, we demonstrate that fluid inertia controls mineral precipitation and clogging at flow intersections, even in laminar flows. We observe distinct precipitation regimes as a function of Reynolds number (Re). At low Reynolds numbers (Re < 10), precipitates form a thin, dense layer along the mixing interface, which shuts precipitation off, while at high Reynolds numbers (Re > 50), strong three-dimensional flows significantly enhance precipitation over the entire intersection, resulting in rapid clogging. When injection rates from two inlets are uneven, flow symmetry-breaking leads to unexpected flow bifurcation phenomena, which result in enhanced concurrent precipitation in both downstream channels. Finally, we extend our findings to rough channel networks and demonstrate that the identified inertial effects on precipitation at the intersection scale are also present and even more dramatic at the network scale. This study sheds light on the fundamental mechanisms underlying mixing-induced mineral precipitation and provides a framework for designing and optimizing processes involving mineral precipitation.

Rapid formation of bioaggregates and morphology transition to biofilm streamers induced by pore-throat flows.

Bioaggregates are condensed porous materials comprising microbes, organic and inorganic matters, and water. They are commonly found in natural and engineered porous media and often cause clogging. Despite their importance, the formation mechanism of bioaggregates in porous media systems is largely unknown. Through microfluidic experiments and direct numerical simulations of fluid flow, we show that the rapid bioaggregation is driven by the interplay of the viscoelastic nature of biomass and hydrodynamic conditions at pore throats. At an early stage, unique flow structures around a pore throat promote the biomass attachment at the throat. Then, the attached biomass fluidizes when the shear stress at the partially clogged pore throat reaches a critical value. After the fluidization, the biomass is displaced and accumulated in the expansion region of throats forming bioaggregates. We further find that such criticality in shear stress triggers morphological changes in bioaggregates from rounded- to streamer-like shapes. This knowledge was used to control the clogging of throats by tuning the flow conditions: When the shear stress at the throat exceeded the critical value, clogging was prevented. The bioaggregation process did not depend on the detailed pore-throat geometry, as we reproduced the same dynamics in various pore-throat geometries. This study demonstrates that pore-throat structures, which are ubiquitous in porous media systems, induce bioaggregation and can lead to abrupt disruptions in flow.

Influence of inter-aquifer leakage on well-injection capacity: Theory and aquifer-scale mapping for artificial recharge.

Aquifer storage and recovery (ASR) is an important water resources management technique that involves the injection of a large volume of water underground. For the successful implementation of an ASR project, a target aquifer should have a sufficient injection capacity, which is the maximum volume of water that can be safely injected. In nature, no aquitard is perfectly impermeable, and inter-aquifer leakage may have a major impact on injection capacity. Despite the importance of determining the injection capacity for ASR planning, there is no quantitative methodology that estimates the injection capacity of leaky aquifers. In this study, we first develop a solution for injection capacity with inter-aquifer leakage based on the Hantush - Jacob solution, and conduct a comprehensive sensitivity analysis to elucidate the influence of inter-aquifer leakage on injection capacity. From the sensitivity analysis, we show that inter-aquifer leakage can impact injection capacity by more than one order of magnitude, depending on the hydrogeological and operational parameters. We then develop a practical mapping methodology that estimates the injection capacity of leaky aquifers. We demonstrate the proposed methodology by applying it to a potential ASR site in Minnesota, USA, where ASR is considered as a solution to alleviate groundwater contamination by PFAS chemicals. The case study results reveal significant spatial variability in injection capacity over the study area and show an average increase in the injection capacity of about 26% compared to that in the nonleaky scenario. We also analyze the uncertainty in the estimated injection capacity due to the variability of aquitard properties and show that the variability of aquitard vertical hydraulic conductivity leads to a larger uncertainty in the estimated injection capacity than does the variability of aquitard thickness. This study elucidates the effects of inter-aquifer leakage on injection capacity and provides a practical methodology for injection capacity mapping.

Identification of iron and sulfate release processes during riverbank filtration using chemical mass balance modeling.

Various hydrogeochemical processes can modify the quality of river water during riverbank filtration (RBF). Identifying the subsurface processes responsible for the bank-filtered water quality is challenging, but essential for predicting water quality changes and determining the necessity of post-treatment. However, no systematic approach for this has been proposed yet. In this study, the subsurface hydrogeochemical processes that caused the high concentrations of total iron (Fe) and sulfate (SO42-) in the bank-filtered water were investigated at a pilot-scale RBF site in South Korea. For this purpose, water quality variations were monitored in both the extraction well and the adjacent river over five months. The volumetric mixing ratio between the river water and the native groundwater in the RBF well was calculated to understand the effect of mixing on the quality of water from the well and to assess the potential contribution of subsurface reactions to water quality changes. To identify the subsurface processes responsible for the evolution of Fe and SO42- during RBF, an inverse modeling based on the chemical mass balance was conducted using the water quality data and the calculated volumetric mixing ratio. The modeling results suggest that pyrite oxidation by abundant O2 present in an unsaturated zone could be a primary process explaining the evolution of total Fe and SO42- during RBF at the study site. The presence of pyrite in the aquifer was indirectly supported by iron sulfate hydroxide (Fe(SO4)(OH)) detected in oxidized aquifer sediments.

Three-Dimensional Vortex-Induced Reaction Hot Spots at Flow Intersections.

We show the emergence of reaction hot spots induced by three-dimensional (3D) vortices with a simple A+B→C reaction. We conduct microfluidics experiments to visualize the spatial map of the reaction rate with a chemiluminescence reaction and cross validate the results with direct numerical simulations. 3D vortices form at spiral-saddle-type stagnation points, and the 3D vortex flow topology is essential for initiating reaction hot spots. The effect of vortices on mixing and reaction becomes more vigorous for rough-walled channels, and our findings are valid over wide ranges of channel dimensions and Damköhler numbers.

Emergence of Stable Laws for First Passage Times in Three-Dimensional Random Fracture Networks.

We study first passage behaviors in the flow through three-dimensional random fracture networks. Network and flow heterogeneity lead to the emergence of heavy-tailed first passage time distributions that evolve with increasing distance between the start and target planes, and transition toward stable laws. Analysis of the spatial memory of the first passage process shows that particle motion can be quantified stochastically by a time domain random walk conditioned on the initial velocity data. This approach identifies advective tortuosity, the velocity point distribution and the average fracture link length as key quantities for the prediction of first passage times. Using this approach, we develop a theory for the evolution of first passage times with increasing distance between the start and target planes and the convergence towards stable laws.

Gravity-driven controls on fluid and carbonate precipitation distributions in fractures.

Many challenges related to carbon-dioxide ([Formula: see text]) sequestration in subsurface rock are linked to the injection of fluids through induced or existing fracture networks and how these fluids are altered through geochemical interactions. Here, we demonstrate that fluid mixing and carbonate mineral distributions in fractures are controlled by gravity-driven chemical dynamics. Using optical imaging and numerical simulations, we show that a density contrast between two miscible fluids causes the formation of a low-density fluid runlet that increases in areal extent as the fracture inclination decreases from 90[Formula: see text] (vertical fracture plane) to 30[Formula: see text]. The runlet is sustained over time and the stability of the runlet is controlled by the gravity-driven formation of 3D vortices that arise in a laminar flow regime. When homogeneous precipitation was induced, calcium carbonate covered the entire surface for horizontal fractures (0[Formula: see text]). However, for fracture inclinations greater than 10[Formula: see text], the runlet formation limited the areal extent of the precipitation to less than 15% of the fracture surface. These insights suggest that the ability to sequester [Formula: see text] through mineralization along fractures will depend on the fracture orientation relative to gravity, with horizontal fractures more likely to seal uniformly.

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

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.

Spatial Markov model of anomalous transport through random lattice networks.

Flow through lattice networks with quenched disorder exhibits a strong correlation in the velocity field, even if the link transmissivities are uncorrelated. This feature, which is a consequence of the divergence-free constraint, induces anomalous transport of passive particles carried by the flow. We propose a Lagrangian statistical model that takes the form of a continuous time random walk with correlated velocities derived from a genuinely multidimensional Markov process in space. The model captures the anomalous (non-Fickian) longitudinal and transverse spreading, and the tail of the mean first-passage time observed in the Monte Carlo simulations of particle transport. We show that reproducing these fundamental aspects of transport in disordered systems requires honoring the correlation in the Lagrangian velocity.

Microfluidic pore model study of precipitates induced by the pore-scale mixing of an iron sulfate solution with simulated groundwater.

Precipitates induced by the pore-scale mixing of iron sulfate solutions with simulated groundwater were investigated using a microfluidic pore model to assess the environmental impacts of the infiltration of acid mine drainage into a shallow aquifer. This model was employed to visualize the formation of precipitates in a porous network and to evaluate their physicochemical influences on pore flow. Four types of groundwater (Na-HCO3, Na-SO4, Na-Cl, and Ca-Cl) were evaluated, and precipitation rates were calculated by processing images of precipitates in the pores captured via microscopy. The results showed that all groundwater types formed a yellow-brownish precipitate at the interface of the iron solution and simulated groundwater flow. Microscopic X-ray analyses demonstrated that precipitate morphology varied with groundwater type. Faster precipitation was observed in the following order by groundwater type: Na-HCO3 > Na-Cl > Na-SO4 > Ca-Cl, which was attributed to the different stability constants of the major anions in each simulated groundwater with Fe ions. Chemical equilibrium models suggested that precipitates were Fe minerals, with FeOOH as the predominant form consistent with the results of X-ray photoelectron spectrometry. The presence of FeOOH implies that precipitates may serve as an effective sorption barrier against some nutrients and heavy metals for the underlying groundwater. However, dye-flow experiments suggested that the precipitates may clog aquifer pores, thereby altering hydrogeological properties in the aquifer.

Spatiotemporal evolution of iron and sulfate concentrations during riverbank filtration: Field observations and reactive transport modeling.

Riverbank filtration is a commonly-used technology that improves water quality by passing river water through aquifers. In this study, a riverbank filtration site in Busan, South Korea, was investigated to understand the spatiotemporal evolution of high iron and sulfate concentrations observed in the riverbank-filtered water. Discrepancies between the nonreactive transport results and field measurements suggest that iron-sulfate-related geochemical reactions play a major role in the spatiotemporal evolution of the hydrochemical properties. Pyrite oxidation was hypothesized to be the main process driving the release of iron and sulfate. To test this hypothesis, a reactive transport model was developed, that implemented pyrite oxidation as a kinetic process and subsequent ferrous iron oxidation and ferric iron precipitation as equilibrium processes. The model accurately captured the temporal evolution of sulfate; however, iron concentrations were underestimated. Sensitivity tests revealed that adjusting reaction constants significantly improved the prediction of iron concentrations. The results of this study suggest that pyrite oxidation can affect the hydrochemistry of riverbank-filtered water and highlight the potential limitations of using theoretical reaction constants in field modeling applications.

Anomalous transport on regular fracture networks: Impact of conductivity heterogeneity and mixing at fracture intersections.

We investigate transport on regular fracture networks that are characterized by heterogeneity in hydraulic conductivity. We discuss the impact of conductivity heterogeneity and mixing within fracture intersections on particle spreading. We show the emergence of non-Fickian transport due to the interplay between the network conductivity heterogeneity and the degree of mixing at nodes. Specifically, lack of mixing at fracture intersections leads to subdiffusive scaling of transverse spreading but has negligible impact on longitudinal spreading. An increase in network conductivity heterogeneity enhances both longitudinal and transverse spreading and leads to non-Fickian transport in longitudinal direction. Based on the observed Lagrangian velocity statistics, we develop an effective stochastic model that incorporates the interplay between Lagrangian velocity correlation and velocity distribution. The model is parameterized with a few physical parameters and is able to capture the full particle transition dynamics.

Predictability of anomalous transport on lattice networks with quenched disorder.

We study stochastic transport through a lattice network with quenched disorder and evaluate the limits of predictability of the transport behavior across realizations of spatial heterogeneity. Within a Lagrangian framework, we perform coarse graining, noise averaging, and ensemble averaging, to obtain an effective transport model for the average particle density and its fluctuations between realizations. We show that the average particle density is described exactly by a continuous time random walk (CTRW), and the particle density variance is quantified by a novel two-particle CTRW.