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Solute transport and biogeochemical reactions in porous and fractured media flows are controlled by mixing, as are subsurface engineering operations such as contaminant remediation, geothermal energy production, and carbon sequestration. Porous media flows are generally regarded as slow, so the effects of fluid inertia on mixing and reaction are typically ignored. Here, we demonstrate through microfluidic experiments and numerical simulations of mixing-induced reaction that inertial recirculating flows readily emerge in laminar porous media flows and dramatically alter mixing and reaction dynamics. An optimal Reynolds number that maximizes the reaction rate is observed for individual pore throats of different sizes. This reaction maximization is attributed to the effects of recirculation flows on reactant availability, mixing, and reaction completion, which depend on the topology of recirculation relative to the boundary of the reactants or mixing interface. Recirculation enhances mixing and reactant availability, but a further increase in flow velocity reduces the residence time in recirculation, leading to a decrease in reaction rate. The reaction maximization is also confirmed in a flow channel with grain inclusions and randomized porous media. Interestingly, the domain-wide reaction rate shows a dramatic increase with increasing Re in the randomized porous media case. This is because fluid inertia induces complex three-dimensional flows in randomized porous media, which significantly increases transverse spreading and mixing. This study shows how inertial flows control reaction dynamics at the pore scale and beyond, thus having major implications for a wide range of environmental systems. 

An accurate estimation of three-dimensional (3D) temperature fields in channel flows is challenging but critical for many important applications such as heat exchangers, radiation energy collectors, and enhanced geothermal systems. In this paper, we demonstrate the possibility of inferring temperature fields from concentration fields for laminar convection flows in a 3D channel using a machine learning (ML) approach. The study involves generation of data using 3D numerical simulations, application of deep learning methodology using conditional generative adversarial networks (cGANs), and analysis of how dataset selection affects model performance. The model is also tested for applicability in different convection scenarios. Results show that cGANs can successfully infer temperature fields from concentration fields, and the reconstruction accuracy is sensitive to the training dataset selected. In this study, we demonstrate how ML can be used to overcome the limitations of traditional heat and mass analogy functions widely used in heat transfer research. 

Abstract The flow‐induced dissolution of porous rocks governs many important subsurface processes and applications. Solute mixing, which determines pore‐scale concentration fields, is a key process that affects dissolution. Despite its importance, the effects of pore‐scale mixing on large‐scale dissolution patterns have not been investigated. Here, we use a pore network model to elucidate the mixing effects on macroscopic dissolution patterns and solute transport. We consider two mixing rules at pore intersections that represent two end members in terms of the mixing intensity. We observe that the mixing effect on dissolution is the strongest at moderate Damköhler number, when the reactive and advective time scales are comparable. This is the regime where wormholes spontaneously appear. Incomplete mixing is shown to enhance flow focusing at the tips of the dissolution channels, which results in thinner wormholes and shorter breakthrough times. These effects on passive solute transport are evident independent of initial network heterogeneity. 

Abstract Fluids with different densities often coexist in subsurface fractures and lead to variable‐density flows that control subsurface processes such as seawater intrusion, contaminant transport, and geologic carbon sequestration. In nature, fractures have dip angles relative to gravity, and density effects are maximized in vertical fractures. However, most studies on flow and transport through fractures are often limited to horizontal fractures. Here, we study the mixing and transport of variable‐density fluids in vertical fractures by combining three‐dimensional (3D) pore‐scale numerical simulations and visual laboratory experiments. Two miscible fluids with different densities are injected through two inlets at the bottom of a fracture and exit from an outlet at the top of the fracture. Laboratory experiments show the emergence of an unstable focused flow path, which we term a “runlet.” We successfully reproduce the unstable runlet using 3D numerical simulations and elucidate the underlying mechanisms triggering the runlet. Dimensionless number analysis shows that the runlet instability arises due to the Rayleigh‐Taylor instability (RTI), and flow topology analysis is applied to identify 3D vortices that are caused by the RTI. Even under laminar flow regimes, fluid inertia is shown to control the runlet instability by affecting the size and movement of vortices. Finally, we confirm the emergence of a runlet in rough‐walled fractures. Since a runlet dramatically affects fluid distribution, residence time, and mixing, the findings in this study have direct implications for the management of groundwater resources and subsurface applications. 

Abstract Understanding mechanistic causes of non‐Fickian transport in fractured media is important for many hydrogeologic processes and subsurface applications. This study elucidates the effects of dead‐end fractures on non‐Fickian transport in three‐dimensional (3D) fracture networks. Although dead‐end fractures have been identified as low‐velocity regions that could delay solute transport, the direct relation between dead‐end fractures and non‐Fickian transport has been elusive. We systematically generate a large number of 3D discrete fracture networks with different fracture length distributions and fracture densities. We then identify dead‐end fractures using a novel graph‐based method. The effect of dead‐end fractures on solute residence time maximizes at the critical fracture density of the percolation threshold, leading to strong late‐time tailing. As fracture density increases beyond the percolation threshold, the network connectivity increases, and dead‐end fractures diminish. Consequently, the increase in network connectivity leads to a reduction in the degree of late‐time tailing. We also show that dead‐end fractures can inform about main transport paths, such as the mean tortuosity of particle trajectories. This study advances our mechanistic understanding of solute transport in 3D fracture networks. 

Abstract Mineral dissolution releases ions into fluids and alters pore structures, affecting geochemistry and subsurface fluid flow. Thus, mineral dissolution plays a crucial role in many subsurface processes and applications. Pore‐scale fluid flow often controls mineral dissolution by controlling concentration gradients at fluid‐solid interfaces. In particular, recent studies have shown that fluid inertia can significantly affect reactive transport in porous and fractured media by inducing unique flow structures such as recirculating flows. However, the effects of pore‐scale flow and fluid inertia on mineral dissolution remain largely unknown. To address this knowledge gap, we combined visual laboratory experiments and micro‐continuum pore‐scale reactive transport modeling to investigate the effects of pore‐scale flow and fluid inertia on mineral dissolution dynamics. Through flow topology analysis, we identified unique patterns of 2D and 3D recirculating flows and their distinctive effects on dissolution. The simulation results revealed that 3D flow topology and fluid inertia dramatically alter the spatiotemporal dynamics of mineral dissolution. Furthermore, we found that the 3D flow topology fundamentally changes the upscaled relationship between porosity and reactive surface area compared to a conventional relationship, which is commonly used in continuum‐scale modeling. These findings highlight the critical role of 3D flow and fluid inertia in modeling mineral dissolution across scales, from the pore scale to the Darcy scale.