Transport in Porous Media最新文献

Many porous media are mixtures of inert and reactive materials, manifesting spatio-chemical heterogeneity. We study the evolution of scalar transport in a chemically heterogeneous material that mimics a green roof soil substrate, fractionally composed of inert and reactive adsorbing particles. These adsorbing particles are equivalent to biochar within a real soil substrate. The scalar transport evolution is determined using experiments and simulations calibrated from experimental data. Experiment 1 is used to determine the equilibrium capacity and adsorption rate of two biochar types when immersed in a methylene blue solution. Breakthrough curves of a packed bed of glass beads with randomly interspersed biochar are determined in experiment 2. Simulations are then run to investigate the solute transport and adsorption dynamics at the pore-scale. An analytical model is proposed to capture the behavior of the biochar adsorption capacity, and the simulation results are compared with experiment 2. A pore-scale analysis showed that uniformly sized beds are superior in contaminant breakthrough reduction, which is related to the adsorptive surface area and the rate at which adsorption capacity is reached. Cases using the adsorption capacity model display a tight distribution of particle surface concentration at later simulation times, indicating maximum possible adsorption. The beds with dissimilar particle sizes create more channeling effects which reduce adsorptive particle efficiency and consequently higher breakthrough concentration profiles. Comparison between experiments and simulations show good agreement. Improved biochar performance can be achieved by maintaining particle size uniformity alongside high adsorption capacity and adsorption rates appropriate to the rainfall intensity.

In this study, liquid jet impingement through porous metal foam-filled cooling channels with uniform cross sections and subject to a high heat flux value of 10⁵ W/m² is investigated numerically and several effective parameters are studied to achieve highly effective thermal control designs. The studied metal foam substrates are porous structures made of copper with porosity values of 0.45 and 0.86, and aluminum with the porosity value of 0.88. Three different jet inlet cross section shapes of rectangular slot, square, and circular are utilized in this work, while the jet flow rate for all cases is kept the same. To investigate the effect of jet size, three different circular jet diameters are modeled; one providing the same hydraulic diameter as that of the square jet, one indicating the same cross section area as that of the square case, and one representing a smaller jet cross section size. In addition, the effects of jet-to-target spacing and utilization of combined metal foam and conductive fins are studied. The comparisons are performed in terms of pressure drop, required pumping power, and nondimensional temperature profile and contour. The results indicate the advantage of utilizing copper foam with 0.86 porosity and circular jet impingement. Also, the local temperature can considerably be reduced when the combined foam and fin design is utilized. For hotspot treatment using combined foam and fin structure, the fin should be placed at the hotspot zone, right in front of the impinging jet. Among the studied fin-structured cases, the cross-shaped fin provides the most effective cooling without additional required pumping power.

The assessment of gas diffusion in water-saturated rocks is essential for quantifying gas loss and determining the amount of gas that could trigger abiotic and biotic processes, potentially altering fluid and rock properties. Additionally, estimating diffusion coefficients is critical for evaluating the balance between hydrogen generation and dissipation in radioactive waste repositories. This investigation involved experimental determination of diffusion coefficients for various gases both in water and in water-saturated Bentheim, Oberkirchner, Grey Weser, and Red Weser sandstones. Experimental conditions included pressures ranging from 0.2 to 1.0 MPa, consistently maintained at a temperature of 35 °C. The diffusion coefficients of hydrogen, helium, and methane in water were determined to be 6.7·10^–9, 9.6·10^–9, and 2.8·10^–9 m²/s, respectively, consistent with literature values obtained through gas concentration measurements without pressure gradients. However, the diffusivity of carbon dioxide and argon in water was measured at 10.9·10^–9 and 44.6·10^–9 m²/s, significantly exceeding their corresponding literature values by an order of magnitude. This discrepancy is attributed to the significant solubility of these gases in water, resulting in density-driven convection as the primary transport mechanism. Furthermore, the effective diffusion coefficients for hydrogen within the analyzed rock specimens varied from 0.8·10^–9 to 2.9·10^–9 m²/s, which are higher than those for methane and carbon dioxide, both ranging from 0.3·10^–9 to 0.9·10^–9 m²/s. This yielded diffusive tortuosity values ranging from 2.6 to 8.2. The observed effective diffusivity values were positively correlated with porosity, permeability, and mean pore size, while exhibiting a negative correlation with tortuosity. Given that the gas–liquid mass transfer coefficient is directly proportional to the effective gas diffusivity in water, the determined values for H₂ are essential for studying the impact of pore characteristics on microbial activity.

Media classification and the construction of pore network models from binary images of porous media hinges on accurately characterizing the pore space. We present a new method for (i) locating critical points, that is, pore body and throat centers, and (ii) partitioning of the pore space using information on the curvature of the distance map (DM) of the binary image. Specifically, we use the local maxima and minima of the determinant map of the Hessian matrix of the DM to locate the center of pore bodies and throats. The locating step provides structural information on the pore system, such as pore body and throat size distributions and the mean coordination number. The partitioning step is based on the eigenvalues of the Hessian, rather than the DM, to characterize the pore space using either watershed or medial-axis transforms. This strategy eliminates the common problem of saddle-induced over-partitioning shared by all traditional marker-based watershed methods and represents an alternative method to determine the skeleton of the pore space without the need for morphological reconstruction.

We investigate problems of convection with double diffusion in a saturated porous medium, where the saturating fluid is one of viscoelastic type, being specifically a Navier–Stokes–Voigt fluid, or a Kelvin–Voigt fluid. The double diffusion problem is analysed for a porous medium with Darcy and Brinkman terms, for a Navier–Stokes–Voigt fluid, and then for a general Kelvin–Voigt fluid of order N. The case where N has the value one is analysed in detail. We also propose a theory where the fluid and solid temperatures may be different, i.e. a local thermal non-equilibrium (LTNE) theory for a porous medium saturated by a Kelvin–Voigt fluid. A further generalization to include heat transfer by a model due to C. I. Christov is analysed in the context of Kelvin–Voigt fluids in porous media. Finally, we examine the question of whether a Navier–Stokes–Voigt theory should be used for nonlinear flows, or whether a suitable objective derivative is required.

Solubility trapping involves dissolution of supercritical carbon dioxide (CO(_text {2})) into the resident brine and is considered an important trapping mechanism for any Carbon Capture and Storage (CCS) project. Previous experimental and numerical studies indicate that density-driven convective mixing can greatly enhance solubility trapping, and is thus a key mechanism to capture when assessing the capacity of industry-scale CCS projects. However, convective mixing is a centimeter-scale phenomenon that is computationally challenging to resolve in standard reservoir simulation that uses coarse grid blocks. Therefore, the goal of this work is to incorporate convective mixing as a sub-grid effect within the traditional formulation of slightly miscible two-phase flow. We do this by adapting the classical model applied in individual grid cells to account for the transient behavior of the underlying convection and the downward transport of dissolved CO(_text {2})through the cell. The new sub-grid model for convective mixing is a mechanistic formulation based on observations from high-resolution simulations. The model employs a set of non-dimensional parameters, calibrated against 2D simulations that allow it to be applied generally to any reservoir properties. We show that the calibrated sub-grid model is easily implemented in a 3D reservoir simulator and benchmark it against a fully resolved field-scale simulation of CO(_text {2})injection in a sloping aquifer. The sub-grid model shows a marked improvement in computing the total amount of CO(_text {2})dissolved over time compared with the classical model for the tested cases. The new implementation is further applied to the openly available model for the Smeaheia storage site in the Norwegian North Sea, to demonstrate the utility of the new model to improve estimates of CO(_text {2})dissolution and explore parameter sensitivities for realistic storage projects.

Efficient decision-making in foam-assisted applications, such as soil remediation and enhanced oil recovery, frequently relies on intricate models that are developed based on a selection of component models that describe the underlying physics of the phenomenon at hand. Modeling foam flow is challenging due to the complex interactions between foam properties, porous media characteristics, and flow dynamics, which results in significant uncertainties in model predictions. Previous studies on uncertainty in foam flow models have only analyzed foam properties and relative permeability separately, leading to limited reliability of the findings. This study aims to bridge the gap of integrating foam implicit-texture parametrization and relative permeability into an uncertainty quantification (UQ) framework to evaluate multi-phase foam flow simulations in porous media more comprehensively than previously available. A foam representation based on the CMG-STARS and a Corey relative permeability model are employed. Bayesian techniques and polynomial chaos expansion (PCE) are employed for inverse and forward UQ. These techniques enable the quantification of uncertainties and the identification of influential parameters within the model. An initial guess algorithm to represent prior beliefs objectively is introduced for the inverse uncertainty quantification step. An in-house foam displacement simulator, aided by a surrogate model, is employed in forward uncertainty quantification and sensitivity analysis. The research findings contribute to understanding and designing reliable foam flow simulations. Sensitivity analyses indicate that incremental strategies to fit parameters can produce inaccurate predictions. Additionally, the article discusses how inaccurately estimated parameters can lead to underestimation or overestimation of foam performance in simulations.

Multicomponent, two-phase flow in porous media is a problem of practical relevance that remains difficult to study experimentally. Advanced methodologies are needed that enable the monitoring of both the saturation of each fluid phase within the pore space and the concentration of the chemical species within the fluids. We present an approach based on multimodality imaging and apply it to the case study of surfactant/polymer flooding in a sandstone for enhanced oil recovery. X-ray computed tomography and positron emission tomography (PET) are applied for the asynchronous acquisition of dynamic profiles of saturations (aqueous and oleic) and of the solute concentration within the surfactant/polymer slug, respectively. This complementary dataset enables precise investigation of the evolution of both the oil bank and the induced mixing at its rear arising from the surfactant/polymer flooding process. The dilution index, intensity of segregation and the spreading length are used to quantify the degree of mixing within the surfactant/polymer slug as a function of time from the spatial structure of the solute concentration field. Relative to the single-phase flow scenario, a threefold increase in dispersivity is observed. We demonstrate that mixing is systematically overestimated if only the PET dataset is used—highlighting the importance of implementing multimodality imaging. We also show that the advection–dispersion equation model, parameterised using the dispersivity derived from the experiments, provides reasonable estimates for the rate of both mixing and spreading.

In this work, we propose a novel method to quantify flows of inelastic non-Newtonian fluids in porous media based on the energy dissipation rate. Unlike the permeability of a Newtonian fluid with Darcy’s law, the permeability of a non-Newtonian fluid shows complicated behaviors due to non-separable effects of the geometry and rheology. We suggest a simple energy dissipation-based flow characterization method to resolve this problem, employing the concepts of effective viscosity and effective shear rate. These effective quantities can be defined with two flow numbers (the energy dissipation rate coefficient and the effective shear rate coefficient) independent of fluid rheology. New expressions for the permeability of Newtonian and mobility of non-Newtonian fluids were derived for model porous media in this approach. We show that the mobility (a ratio of permeability to viscosity) of a non-Newtonian fluid for a given porous media can be factored into the permeability of Newtonian fluid and the effective viscosity, exactly the same as in case of a Newtonian fluid. The proposed quantification method was validated through example problems of flows using numerical simulations (1) in two-dimensional (2D) transverse fibrous porous media (quadratic and hexagonal), (2) flows in three-dimensional (3D) regularly packed beds with spheres (faced-centered cubic and body-centered cubic), and (3) finally randomly distributed unidirectional fibers in 2D. The suggested method can quantitatively assess tortuous path in porous electrode for electrolyte transport and in the secondary oil recovery, offering the potential to optimize performance and efficiency in these applications.