Reactive transport processes in porous media with dissolution of solid structures are widely encountered in scientific and engineering problems. In the present work, the reactive transport processes in heterogeneous porous structures generated by Monte Carlo stochastic movement are simulated by using the lattice Boltzmann method. Six dissolution patterns are identified under different Peclet and Damkohler numbers, including uniform pattern, hybrid pattern, compact pattern, conical pattern, dominant pattern, and ramified pattern. Particularly, when Peclet and Damkohler numbers are larger than 1, the increase in the heterogeneity rises the chance of preferential channel flow in the porous medium and thus intensifies the wormhole phenomena, leading to higher permeability. The pore-scale results also show that compared with the specific surface area, the permeability is more sensitive to the alteration of the structural heterogeneity, and it is challenging to propose a general formula between permeability and porosity under different reactive transport conditions and structural heterogeneity. Thus, deep neural network is employed to predict the permeability–porosity relationship. The average value of mean absolute percentage error of prediction of 12 additional permeability–porosity curves is 6.89%, indicating the promising potential of using deep learning for predicting the complicated variations of permeability in heterogeneous porous media with dissolution of solid structures.

Microchannel is helpful for cooling the microelectronic devices with high heat flux. In this study, topology optimization method with three objective functions (pumping power, bottom wall mean temperature and temperature uniformity) is adopted to optimize the structure of a manifold microchannel. The field synergy principle is used to evaluate the optimized structures. Several single-objective, bi-objective, and triple-objective function problems are numerically studied. It is found that the obtained structures with better cooling performance always present low synergy angle, in consistent with the field synergy principle. The optimized structures perform better than conventional microchannel with ribs. Effects of inlet velocity, thermal diffusivity ratio and heat flux at the bottom wall are also investigated. The topology optimization method with triple-objective functions is effective to improve the performance of manifold microchannels.

Improving the performance of proton exchange membrane fuel cells (PEMFCs) requires deep understanding of the reactive transport processes inside the catalyst layers (CLs). In this study, a particle-overlapping model is developed for accurately describing the hierarchical structures and oxygen reactive transport processes in CLs. The analytical solutions derived from this model indicate that carbon particle overlap increases ionomer thickness, reduces specific surface areas of ionomer and carbon, and further intensifies the local oxygen transport resistance (R_other). The relationship between R_other and roughness factor predicted by the model in the range of 800-1600 s m^-1 agrees well with the experiments. Then, a multiscale model is developed by coupling the particle-overlapping model with cell-scale models, which is validated by comparing with the polarization curves and local current density distribution obtained in experiments. The relative error of local current density distribution is below 15% in the ohmic polarization region. Finally, the multiscale model is employed to explore effects of CL structural parameters including Pt loading, I/C, ionomer coverage and carbon particle radius on the cell performance as well as the phase-change-induced (PCI) flow and capillary-driven (CD) flow in CL. The result demonstrates that the CL structural parameters have significant effects on the cell performance as well as the PCI and CD flows. Optimizing the CL structure can increase the current density and further enhance the heat-pipe effect within the CL, leading to overall higher PCI and CD rates. The maximum increase of PCI and CD rates can exceed 145%. Besides, the enhanced heat-pipe effect causes the reverse flow regions of PCI and CD near the CL/PEM interface, which can occupy about 30% of the CL. The multiscale model significantly contributes to a deep understanding of reactive transport and multiphase heat transfer processes inside PEMFCs.

Modeling of multiphase flow and reactive mass transport in porous media remains a pivotal challenge in the realm of subsurface energy storage, demanding a nuanced understanding across varying scales. This review paper presents a comprehensive overview of the latest advancements in multiscale modeling techniques that address the inherent complexity of these processes. Three cutting-edge approaches are presented: hybrid multiscale simulation, which leverages both continuum and discrete modeling frameworks to enhance model fidelity; approximated physics, which simplifies complex reactions and interactions to expedite computations without significantly sacrificing accuracy; and machine-learning-assisted multiscale simulation, which integrates predictive analytics to refine simulation outputs. Each method presents distinct advantages and hurdles, collectively advancing the precision and computational efficiency of subsurface modeling. Despite the substantial progress, we recognize the persistent challenges, such as the need for more robust coupling techniques, the balance between model complexity and computational feasibility, and effectively combining machine learning with traditional physical models. Promising directions for future work are discussed to address these challenges, aiming to push the boundaries of current multiscale modeling capabilities. Document Type: Invited review Cited as: Lyu, X., Wang, W., Voskov, D., Liu, P., Chen, L. Multiscale modeling for multiphase flow and reactive mass transport in subsurface energy storage: A review. Advances in Geo-Energy Research, 2025, 15(3): 245-260. https://doi.org/10.46690/ager.2025.03.07

Numerical models that accurately describe the nano/microscale transport phenomena in the proton exchange membrane fuel cells (PEMFC) are highly required. In this study, a multiscale PEMFC model is developed by coupling a pore-scale model for the catalyst layer (CL) and a cell-scale model. In the pore-scale model, oxygen transport and electrochemical reaction under different platinum loadings within porous agglomerates are simulated. The pore-scale model, as a sub-grid model, is then coupled into a two-phase and non-isothermal cell-scale model. The multiscale model developed successfully predicts the impact of Platinum loading on the limiting current density, while the traditional one with the classical agglomerate model fails to. Detail distributions of important variables including concentration, current density, and saturation are discussed. Furthermore, using the multiscale model, ionomer content is optimized to improve the cell performance as well as reduce the performance loss when Pt loading is reduced. It is demonstrated that multiscale model incorporating nano/microscopic transport phenomena is promising to more accurately reveal the coupling mechanisms of multiple reactive transport processes in PEMFCs.

Carbon capture, utilization, and storage have been an effective way to deal with global climate issues. Injecting CO₂ into depleted oil reservoirs can reach the dual goal of carbon storage and enhanced oil recovery. To optimize the gas injection strategy, it is necessary to understand the underlying mechanisms of three-phase fluid flow of oil, water, and gas. In this study, a lattice Boltzmann color gradient model is used to investigate the pore-scale three-phase displacement process in porous media. Gas is injected into the porous domain initially occupied by water and oil. Typical microscopic behaviors, including coalescence and split-up, pinch-off, double and multiple displacement, as well as parallel flow, are identified and discussed. Effects of water content (ϕ), capillary number (Ca), wettability and viscosity ratio (M) on the flow pattern, and oil recovery rate are explored. The oil ganglia inhibit the development of gas fingers, causing stronger viscous fingering characteristics with increasing ϕ. The fingering pattern is located in the crossover zone for the Ca from 5 × 10⁻⁵ to 5 × 10⁻⁴. As ϕ increases, the oil recovery rate reduces. The oil ganglia tend to occupy small pores as oil wettability enhanced, making it more difficult to be drained out. The reduction of oil viscosity is beneficial to improve connectivity, thereby effectively enhancing the oil recovery. Finally, the CO₂ storage rate is also evaluated. It is found that the storage rate is very sensitive to the initial oil–water distributions. In general, the storage rate increases as ϕ decreases, Ca increases, and oil wettability enhances.