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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 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. 

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.