Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Porous flow”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The role of the spatial heterogeneity and correlation length of surface wettability on two-phase flow in a CO 2 -water-rock system

This study characterized and modeled heterogeneous surface wettability in sandstone and investigated the role of spatial heterogeneity and correlation length of surface wettability on relative permeability in a supercritical CO 2 (scCO 2 )-brine-rock system. Understanding the role of wettability heterogeneity on relative permeability is essential to geological CO 2 sequestration, oil and gas recovery, and contaminated groundwater remediation. Although numerous studies have attempted to understand the influences of surface wettability, capillary number (Ca), and viscosity ratio, the role of the spatial variation and correlation length of surface wettability on two-phase flow in three-dimensional (3D) porous media has not been unraveled due to the challenges in the measurement and representation of realistic rock surface wettability. In this work, we conducted in-situ measurements of surface contact angle (CA) in a Bentheimer sandstone after CO 2 flooding using micro-computed tomography (micro-CT), and found that the pore-scale CA distribution on rock surfaces followed a log-normal distribution associated with a spatial correlation length. Based on the statistical information from CT scanning, a Gaussian random field was used to model CA distributions that had desired standard deviations and spatial correlation lengths, which were then adjusted within a certain range of values for sensitivity analyses to study their combined effects on the two-phase flow in the porous medium using the lattice Boltzmann (LB) method. The LB two-phase flow simulation was accelerated using hybrid, multicore parallel computing to overcome the challenges in simulating multiphase flow in a large 3D domain having 800 × 800 × 600 nodes. The simulation results showed that the surface wettability heterogeneity (i.e., standard deviation of CA) had a lesser effect on the relative permeability of the wetting fluid (water) but a more significant impact on the relative permeability of the non-wetting fluid (scCO 2 ). The Corey model was used to fit the LB-simulated relative permeability curves of water and scCO 2 and showed that the variations in the relative permeability curves for both water and scCO 2 increased as the standard deviation and spatial correlation length of CA increased. This study illustrated that the assumption of homogeneous surface wettability may cause errors in multiphase flow simulations. Furthermore, the impacts of both the standard deviation and spatial correlation length of CAs should be accounted for. This is the first study that explored the spatial correlation lengths associated with CA distributions on sandstone surfaces and comprehensively investigated the roles of both spatial variation and correlation length of CA on two-phase flow properties in 3D porous media. The optimized LB multiphase flow model was proved a powerful tool to study the interplays and combined effects of these statistical parameters, which had critical applications in numerous natural and engineering processes that involved multiphase flow in porous media.

58 GEOSCIENCES↗
DOE OSTI2021

Predictive Data-driven Platform for Subsurface Energy Production

Subsurface energy activities such as unconventional resource recovery, enhanced geothermal energy systems, and geologic carbon storage require fast and reliable methods to account for complex, multiphysical processes in heterogeneous fractured and porous media. Although reservoir simulation is considered the industry standard for simulating these subsurface systems with injection and/or extraction operations, reservoir simulation requires spatio-temporal “Big Data” into the simulation model, which is typically a major challenge during model development and computational phase. In this work, we developed and applied various deep neural network-based approaches to (1) process multiscale image segmentation, (2) generate ensemble members of drainage networks, flow channels, and porous media using deep convolutional generative adversarial network, (3) construct multiple hybrid neural networks such as convolutional LSTM and convolutional neural network-LSTM to develop fast and accurate reduced order models for shale gas extraction, and (4) physics-informed neural network and deep Q-learning for flow and energy production. We hypothesized that physicsbased machine learning/deep learning can overcome the shortcomings of traditional machine learning methods where data-driven models have faltered beyond the data and physical conditions used for training and validation. We improved and developed novel approaches to demonstrate that physics-based ML can allow us to incorporate physical constraints (e.g., scientific domain knowledge) into ML framework. Outcomes of this project will be readily applicable for many energy and national security problems that are particularly defined by multiscale features and network systems.

58 GEOSCIENCES↗
DOE OSTI2020

Hydrate is a Nonwetting Phase in Porous Media

In porous media containing gas hydrate, the hydrate morphology impacts the flow behavior of the fluid phases. We hypothesize that hydrate emplaces itself as a nonwetting phase and use this idea to describe relative permeability of water in a hydrate/water system. We perform steady-state relative permeability measurements in hydrate-bearing samples with a range of hydrate saturation. We measure and compare water relative permeability in the presence of gas and in the presence of hydrate and find that the water relative permeability is the same in both cases. This suggests that (1) relative permeability for hydrate systems can be obtained without performing difficult experiments on hydrate bearing sediments, (2) flow properties are porous-media dependent, and (3) models that assume a fixed pore or tube geometry are inadequate.

58 GEOSCIENCES↗
DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2022

Learning functional priors and posteriors from data and physics

In this work, we develop a new Bayesian framework based on deep neural networks to be able to extrapolate in space-time using historical data and to quantify uncertainties arising from both noisy and gappy data in physical problems. Specifically, the proposed approach has two stages: (1) prior learning and (2) posterior estimation. At the first stage, we employ the physics-informed Generative Adversarial Networks (PI-GAN) to learn a functional prior either from a prescribed function distribution, e.g., Gaussian process, or from historical data and physics. At the second stage, we employ the Hamiltonian Monte Carlo (HMC) method to estimate the posterior in the latent space of PI-GANs. In addition, we use two different approaches to encode the physics: (1) automatic differentiation, used in the physicsinformed neural networks (PINNs) for scenarios with explicitly known partial differential equations (PDEs), and (2) operator regression using the deep operator network (DeepONet) for PDE-agnostic scenarios. We then test the proposed method for (1) meta-learning for one-dimensional regression, and forward/inverse PDE problems (combined with PINNs); (2) PDE-agnostic physical problems (combined with DeepONet), e.g., fractional diffusion as well as saturated stochastic (100-dimensional) flows in heterogeneous porous media; and (3) spatial-temporal regression problems, i.e., inference of a marine riser displacement field using experimental data from the Norwegian Deepwater Programme (NDP). The results demonstrate that the proposed approach can provide accurate predictions as well as uncertainty quantification given very limited scattered and noisy data, since historical data could be available to provide informative priors. In summary, the proposed method is capable of learning flexible functional priors, e.g., both Gaussian and non-Gaussian process, and can be readily extended to big data problems by enabling mini-batch training using stochastic HMC or normalizing flows since the latent space is generally characterized as low dimensional.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Mechanistic understanding of carbon mineralization in fracture systems using microfluidics

Carbon mineralization in mafic and ultramafic rocks presents an opportunity for permanent carbon storage in the Earth's subsurface. However, due to their lower permeability, pre-existing fracture networks are key for mineralization to occur. Therefore, to fully develop this technology, a mechanistic understanding of the mineralization behavior in fractures with the consideration of hydrodynamic components is required. We use high-pressure microfluidics to investigate key mechanisms influencing dissolution–precipitation in a fracture network. The experiments were conducted in micromodels made of natural rocks with a comb-shaped flow channel to mimic a fracture network. This enabled studying the effect of injection rate on coupled dissolution–precipitation in advection and diffusion-dominated flow paths. We used gypsum carbonation as an analog reaction to allow for realistic experimental time frames due to its rapid reaction kinetics. The experimental work is coupled with high-fidelity numerical simulations to enhance our understanding of the parameters affecting the mineralization reaction. Our results demonstrate the importance of flow rate on the rate and nature of the gypsum carbonation reaction revealing that higher flow rates enable deeper penetration of the mineral precipitation front into the dead-end channels. This is an important finding since for sustained mineralization in a fracture network, precipitation in dead-ends while still allowing for flowing fractures is critical. Detailed characterization of the precipitates showed that lower flow rates led to porous and loose precipitates in the form of aragonite while higher flow rates mimicked supersaturation behavior leading to the formation of calcite. The reactive transport simulations further demonstrated the significance of flow velocity in advection-dominated channels to influence the efficiency of carbon mineralization in diffusion-dominated channels, potentially clogging of dead-end channels. These findings highlight the need for coupling chemical, mechanical, and hydrodynamic processes to evaluate the nature and extent of carbon mineralization in fractured media critical for permanent storage in mafic and ultramafic formations. This research further highlights the need for more investigation in potential subsurface fracture generation techniques to aid carbon mineralization.

25 ENERGY STORAGE↗
DOE OSTI2020

Numerical modeling and performance analysis of a membrane-based air dehumidifier using ionic liquid desiccant

Membrane-based Liquid Desiccant Air Dehumidifier (MLDAD) has the potential to overcome the shortcomings of the conventional open-tower liquid desiccant air dehumidifier. The MLDAD uses membranes with high water vapor permeability to separate air stream from the liquid desiccant and thus eliminates carry-over and the resulting corrosion issues. Recently, the ionic liquid desiccant becomes a promising alternative to conventional liquid desiccant. The ionic liquid desiccant has a large potential of dehumidification, which is also non-corrosive to metals and non-crystallizable. A two-dimensional numerical heat and mass transfer model of the MLDAD using a recently identified ionic liquid desiccant, [EMIM]OAc, is presented in this paper. We report this model can simulate the performance of the MLDAD with various designs, including different selections of liquid desiccant and membrane materials, dimensions of the MLDAD, and flow patterns. For porous membranes, this model accounts for several micro-scale mass transfer mechanisms of vapor transportation across the membrane. It can also model the mass transfer performance of dehumidifiers using non-porous membranes based on the experimentally measured membrane permeance. The numerical model was validated against the performance data available from literature and experimental tests. The maximum discrepancy of the latent effectiveness between the measured and the model-predicted results was about 6% when porous membranes were used. A parametric study was conducted with the numerical model. The results indicated that the membrane permeability, air path height, and solution to air mass ratio were the most critical parameters, determining the dehumidification performance of the MLDAD.

42 ENGINEERING↗
DOE OSTI2020

Numerical modeling and performance analysis of a membrane-based air dehumidifier using ionic liquid desiccant

Membrane-based Liquid Desiccant Air Dehumidifier (MLDAD) has the potential to overcome the shortcomings of the conventional open-tower liquid desiccant air dehumidifier. The MLDAD uses membranes with high water vapor permeability to separate air stream from the liquid desiccant and thus eliminates carry-over and the resulting corrosion issues. Recently, the ionic liquid desiccant becomes a promising alternative to conventional liquid desiccant. The ionic liquid desiccant has a large potential of dehumidification, which is also non-corrosive to metals and non-crystallizable. A two-dimensional numerical heat and mass transfer model of the MLDAD using a recently identified ionic liquid desiccant, [EMIM]OAc, is presented here. This model can simulate the performance of the MLDAD with various designs, including different selections of liquid desiccant and membrane materials, dimensions of the MLDAD, and flow patterns. For porous membranes, this model accounts for several micro-scale mass transfer mechanisms of vapor transportation across the membrane. It can also model the mass transfer performance of dehumidifiers using non-porous membranes based on the experimentally measured membrane permeance. The numerical model was validated against the performance data available from literature and experimental tests. The maximum discrepancy of the latent effectiveness between the measured and the model-predicted results was about 6% when porous membranes were used. A parametric study was conducted with the numerical model. Finally, the results indicated that the membrane permeability, air path height, and solution to air mass ratio were the most critical parameters, determining the dehumidification performance of the MLDAD.

42 ENGINEERING↗
DOE OSTI2022

MPLBM-UT: Multiphase LBM library for permeable media analysis

MPLBM-UT is a specialized lattice-Boltzmann library that makes running single- and two-phase flow simulations in porous media accessible to everyone. We provide a suite of tools to pre-process computational domains for simulation, to set up custom boundary conditions, to run simulations, to post-process simulation outputs, and to visualize simulation results and data. All of these tools are easily accessible to users through the mplbm_utils Python package included in and automatically installed with MPLBM-UT. The high-performance, highly parallel library Palabos is used as the solver backend. MPLBM-UT is easily deployed in a variety of systems, from laptops to supercomputer clusters. MPLBM-UT also features multiple examples and benchmark templates that allow for fast prototyping of different porous media problems. We also provide an interface for reading in different file types and downloading domains from the Digital Rocks Portal to perform simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Redox-Based Chemical Looping Large-Scale Air Separation Unit Designs Using Perovskite Material

Oxygen production by means of air separation through redox cycle reactions of Sr 1–x Ca x FeO 3-δ perovskite particles is examined numerically in the context of modular large-scale units. Fixed-bed, multicomponent reactor designs allowing for the recovery of the energy released by the exothermic oxidation (or adsorption) to enhance the endothermic reduction (or desorption) of the perovskite material are considered and compared against a baseline cylindrical packed bed. Numerically, the gas–solid reacting system is approximated as a single-phase reacting gas flow through a porous medium of constant porosity. The redox kinetics account for the oxygen nonstoichiometry of the perovskite, which exhibits a dependence on the temperature and the oxygen partial pressure, in order to describe some mechanisms of the oxygen vacancies. Results show that the spontaneous release of the oxygen from the lattice arrangements, through the thermal management of the heat of the oxidation reaction from the geometrical design of the reactors, can be enhanced up to 4% with respect to the baseline oxygen production of 0.201 g O 2 ·h –1 ·g perovskite –1 with the cylindrical packed bed, in the condition of operations in this work, at 500 °C. Sensitivity to the operation temperature showed that the oxygen production is greatly enhanced by approximately 30% with the increase of the temperature from 500 to 550 °C. We report this result is consistent with experimental observations of faster release of the oxygen from the lattice arrangements of this perovskite at 550 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Quantifying the Effect of Pore‐Size Dependent Wettability on Relative Permeability Using Capillary Bundle Model

Abstract Relative permeability is a key parameter for characterizing the multiphase flow dynamics in porous media at macroscopic scale while it can be significantly impacted by wettability. Recently, it has been reported in microfluidic experiments that wettability is dependent on the pore size (Van Rooijen et al., 2022). To investigate the effect of pore‐size‐dependent wettability on relative permeability, we propose a theoretical framework informed by digital core samples to quantify the deviation of relative permeability curves due to wettability change. We find that the significance of impact is highly dependent on two factors: (i) the function between contact angle and pore size (ii) overall pore size distribution. Under linear function, this impact can be significant for tight porous media with a maximum deviation of 1,000%.

Yu, Siqin↗
DOE OSTI2023

Effect of surface defects on damage and failure of dynamically driven cylindrical shells

Here, in this work, expanding cylinder experiments are performed using a gas gun on the aluminum 6061 alloy to understand the dynamic deformation and failure behavior of this material in the presence of surface defects in the form of scratches. The experiments are then modeled with the modified Tepla (for tensile plasticity) model, which is equipped with the damage yield function of Gurson [“Continuum theory of ductile rupture by void nucleation and growth: Part I—Yield criteria and flow rules for porous ductile media,” (1977)] and Tvergaard and Needleman [Acta Metall. 32(1), 157–169 (1984)], over-stress viscosity [F. Addessio and J. Johnson, J. Appl. Phys. 74(3), 1640–1648 (1993)], and the underlying viscoplastic model of Preston et al. [J. Appl. Phys. 93(1), 211–220 (2003)] (Preston–Tonks–Wallace model) for the dense material. The Tepla model parameters were calibrated using the Bayesian approach against experimentally measured free-surface velocity data obtained from plate impact experiments on the aluminum 6061 alloy. The evolution of radial expansion and shapes of the deformed cylinder from the simulations on a pristine cylinder wall and a cylinder wall with a longitudinal surface defect shows excellent agreement with the corresponding experimental results. Finally, the role of surface defect size and cylinder wall geometry on the dynamic strength of the cylinder wall was also investigated through a series of simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Modified Lucas-Washburn theory for fluid filling in nanotubes

Ultrafast water transport in carbon nanotubes (CNTs) has drawn a great deal of attention in a number of applications, such as water desalination, power generation, and biomolecule detection. With the recent experimental advances in water filling of isolated CNTs, the Lucas-Washburn theory for capillary rise in tubes needs to be revisited for a better understanding of the physics and dynamics of water filling in CNTs. Here, the Lucas-Washburn theory is corrected for the hydrodynamic entrance effects as well as the variation of capillary pressure and hydrodynamic properties with the radius and length of CNTs. Due to the large slippage in CNTs, inclusion of the entrance effects is important particularly for the initial stages of filling where a L ∝ t scaling, as opposed to L 2 ∝ t , is observed in our molecular dynamics (MD) simulations. The corrected Lucas-Washburn theory is shown to predict the water filling dynamics in CNTs as observed in MD simulations. With the corrected theory, we achieve a better understanding of capillary rise and water filling dynamics in CNTs.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2022

Measurement of Transport Properties of Woody Biomass Feedstock Particles Before and After Pyrolysis by Numerical Analysis of X-Ray Tomographic Reconstructions

Lignocellulosic biomass has a complex, species-specific microstructure that governs heat and mass transport during conversion processes. A quantitative understanding of the evolution of pore size and structure is critical to optimize conversion processes for biofuel and bio-based chemical production. Further, improving our understanding of the microstructure of biochar coproduct will accelerate development of its myriad applications. This work quantitatively compares the microstructural features and the anisotropic permeabilities of two woody feedstocks, red oak and Douglas fir, using X-ray computed tomography (XCT) before and after the feedstocks are subjected to pyrolysis. Quantitative analysis of the three-dimensional (3D) reconstructions allows for direct calculations of void fractions, pore size distributions and tortuosity factors. Next, 3D images are imported into an immersed boundary based finite volume solver to simulate gas flow through the porous structure and to directly calculate the principal permeabilities along longitudinal, radial, and tangential directions. The permeabilities of native biomass are seen to differ by three to four orders of magnitude in the different principal directions, but we find that this anisotropy is substantially reduced in the biochar formed during pyrolysis. The quantitative transport properties reported here enhance the ability of pyrolysis simulations to account for feedstock-specific effects and thereby provide a useful touchstone for the biorefining community.

09 BIOMASS FUELS↗
DOE OSTI2022

PARTICLE RESOLVED DNS STUDY OF TURBULENCE EFFECTS ON HYPORHEIC MIXING IN RANDOMLY PACKED SEDIMENT BEDS

Pore-resolved direct numerical simulations (DNS) are used to investigate the interactions between stream-water flow turbulence and groundwater flow through a porous sediment bed in the hyporheic zone. Two permeability Reynolds numbers (2.56 and 5.17), representative of aquatic systems and representing ratio of permeability to viscous length scales, were simulated to understand its influence on the momentum exchange at the sediment-water interface (SWI). A doubleaveraging methodology is used to compute the Reynolds stresses, form-induced stresses, and pressure fluctuations. It is observed that both shear layer and turbulent shear stress penetration increases with ReK. Reynolds and form-induced bed-normal stresses increase with ReK. The peak values of the form-induced stresses for the lower (2.56) and higher (5.17) ReK happen within the top layer of the sediment bed. The sum of turbulent and form-induced pressure fluctuations, analyzed at their respective zero-displacement planes, are statistically similar and can be well approximated by a t location-scale distribution fit providing with a model that could potentially be used to impose boundary conditions at the SWI in reach scale simulations.

Karra, Shashank↗
DOE OSTI2026

Automated and Accelerated Continuum Model Development for Electrochemical Systems (Abbreviated Report)

Despite the availability of computational resources and advancements in numerical computing capabilities, the multiscale models core to understanding, predicting the behaviors of, and designing energy and environmental systems involving porous media are still 1.) developed through by-hand derivations and 2.) limited by many methodological assumptions employed during model derivation. As a result, the advancement of effective media models for engineering DOE mission-critical systems (e.g., batteries, flow batteries, electrolyzers, geothermal systems, subsurface chemical storage systems, etc.) is slow (i.e., it takes years for models to traverse from stages of “development” to “practical utilization”), hindering our ability to effectively optimize such systems and stay at the cutting-edge of the energy frontier. In this work, we aimed to address these limitations by 1.) automating and accelerating multiscale model derivation via symbolic computing and 2.) develop a novel multiscale modeling methodology for flow and transport through porous media that avoids the typical assumptions hindering previous models. As a result of our efforts, we 1.) developed a hybrid symbolic-numeric code called Fouriera for fully-automating the implementation of multiphysical and phase-field models via the Fourier spectral method for materials science research, and 2.) advanced a multiscale modeling methodology called The Method of Finite Averages that rigorously predicts the behaviors of flow and transport through heterogeneous porous media under the influence of non-local effects and strong advection. Ultimately, these deliverables provide strong foundations from which further efforts can advance multiscale modeling tools and capabilities that do not intrinsically rely on 1.) the speed and mathematical capabilities of humans, nor 2.) the methodological assumptions limiting current models.

36 MATERIALS SCIENCE↗