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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.

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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

Operando visualization of porous metal additive manufacturing with foaming agents through high-speed x-ray imaging

Porous metals find extensive applications in soundproofing, filtration, catalysis, and energy-absorbing structures, thanks to their unique internal pore structure and high specific strength. In recent years, there has been an increasing interest in fabricating porous metals using additive manufacturing (AM), leveraging its unique advantages, including improved design freedom, spatial material control, and cost-effective small-batch production. In this study, we conducted pioneering operando visualization of AM porous metal using a laser powder bed fusion (L-PBF) setup combined with a high-speed synchrotron x-ray imaging system. Single track printing experiments using Ti6Al4V (Ti64) combined with titanium hydride (TiH 2 ) and sodium carbonate (Na 2 CO 3 ) as foaming agents, with varying mixing ratios were performed under different processing conditions. Here. the results elucidate the dynamic development of porosity formation. The average pore size is significantly influenced by the particle size of foaming agents when pore coalescence is absent. For all foaming agent content tested in the current study, the number of pores is found to be more sensitive to changes in laser power than in laser scanning speed. Increasing linear energy density (increasing laser power or reducing laser scanning speed) promotes the foaming agent activation thereby porosity formation. However, high linear energy density skews pore distribution towards the surface despite forming deeper melt pools. In addition, the impact of additional factors including foaming agent's laser absorptivity and decomposition kinetics with respect to AM time scales should be carefully considered to avoid ineffective activation of foaming agents during the AM of porous metals.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The Method of Finite Averages: A rigorous upscaling methodology for heterogeneous porous media

Rigorous upscaling techniques offer accurate and computationally-efficient strategies for modeling the average behaviors of multi-physical, multiscale phenomena in geological porous media. However, such techniques often rely on a variety of methodological assumptions that prohibit their rigorous application to practical systems (e.g., systems involving heterogeneous porous media, system-scale boundary conditions, and fine-scale dynamics that are not diffusion-dominant). In this work, we aim to formulate an upscaling methodology with few methodological assumptions to provide high levels of model generality and foster the utilization of rigorously-derived upscaled models in practice. In particular, we introduce the Method of Finite Averages (MoFA), a novel upscaling methodology for rigorously modeling heterogeneous porous media and system-scale boundary conditions. We then detail MoFA’s implementation for the advective–diffusive transport of a single species and compare the methodology with classic numerical techniques, as well as other rigorous upscaling techniques, to highlight MoFA’s unique combination of rigor and generality. We then validate the derived model while demonstrating its benefits in three numerical experiments. The results suggest that (1.) the applicability and a priori error guarantees of MoFA models do not directly depend on system geometry, (2.) a model’s applicability and error guarantees can be can arbitrarily expanded and reduced, respectively, with further computational expense, and (3.) downscaling with MoFA provides an efficient strategy for generating accurate pore-scale solutions from upscaled results. Ultimately, the results evidence that upscaled models can be rigorously derived for heterogeneous porous media systems and resolved in a fraction of the time it takes to perform the equivalent pore-scale simulations.

58 GEOSCIENCES↗
DOE OSTI2027

A multi-region approach for the analysis of porous materials and gas-solid reactions using USAXS-SAXS-WAXS: application to CaO carbonation

The reaction rate of gas-solid non-catalytic reactions is typically investigated using reactant conversion data over time and ex-situ measurements of the porous solid reactant textural properties; these data enable the experimental estimation of the initial intrinsic reaction rate, whereas the evolution of the textural properties and the reaction rate over time are evaluated theoretically using reaction models. In this work, a different methodology is presented, based on: a) in-situ time-resolved USAXS-SAXS-WAXS measurements of the solid sample, and b) a multi-region modeling approach; this methodology allows for the estimation of the textural properties and the intrinsic reaction rate at any time during the reaction. Referring to a reaction in which a solid product is obtained from a solid reactant and a gaseous reactant, the porous particle is described as consisting of several distinct regions with different microstructural properties, and both a gas-solid reaction model and a SAXS model are derived and applied to the carbonation of porous CaO. The proposed reaction model highlights the role of the inaccessible reactant and accurately predicts the solid reactant conversion versus time profile. The SAXS model accurately predicts the measured linear trends of the Porod invariant and of the pre-factor of the power law scattering profile versus CaO mass fraction; in the absence of chemical reactions, the proposed equations extend classical SAXS theory to porous materials containing macropores and nonporous solid phases in addition to standard nanoscale inhomogeneous regions.

CO2 solid sorbents↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

Liquid lithium wetting and percolation in a porous tungsten/liquid Li plasma facing component (PFC)

This work explores the concept of a hybrid material system designed to protect the plasma from high-Z material emission while also protecting an underlying high-Z wall material from erosion by integrating a low-Z component in the liquid phase. Plasma-surface interaction properties of a porous tungsten (W)-liquid metal hybrid system, having the favorable bulk thermomechanical properties of W while serving as a scaffold for a liquid metal with self-healing and radiative vapor shielding characteristics, are examined. W-substrates with 70% density of bulk W and 1–5 μm sized pores have been fabricated with 50-nm W powders using spark plasma sintering. Enhanced lithium (Li) wettability, driven by percolation through the porous W architecture, is demonstrated with in-situ liquid Li drop measurements. Finally, results show complete wetting of liquid Li at 250 °C in the porous W, 100 °C lower temperature than what has been observed on traditional W surfaces. In operando ion beam analysis of Li films deposited on porous W substrates provides microscopic evidence of Li percolation into the substrate and a first look at D retention up to a depth of 100 nm after plasma exposure at a flux of 10 20 m –2 s –1 , 250 eV/amu D + plasma to a fluence of 1.16 × 10 23 m –2 at a temperature of 200 °C.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Sodium diffusion in heterogeneous porous media: Connecting laboratory experiments and simulations

Sodium has been suggested as a tracer for brine in reservoir formations where a significant amount of sodium ion is found mainly in the aqueous phase. Fortunately, the most abundant sodium isotope, 23 Na, is Nuclear Magnetic Resonance (NMR) active, making it possible to study the structure and dynamical properties of both bulk and pore confined fluid systems. We investigated the diffusion of key dissolved sodium species in bulk solution and porous media as a function of composition, ionic strength, and permeability of the carbonate samples using NMR relaxometry and pulsed-field gradient (PFG) NMR techniques. We use Indiana limestone as an example of natural porous media and water as a freely diffusible tracer and carrier. We demonstrate apparent diffusion measurement of sodium based on changes in spin–spin relaxation time (T 2 ) signal in pore confinement. The diffusion rate of sodium decreases with increasing counter-ion size. This effect is greater at higher ionic strengths and lower chemical potentials in porous media. The reactive transport code, CrunchFlow, was used to complement the NMR experiments to simulate diffusion behavior in porous media. Combining these two methods provides a powerful approach to estimating effective diffusion coefficients in heterogeneous matrices. The modeling considers the influence of physical properties (porosity and tortuosity) and chemical properties (geochemical composition and chemical gradients). The results presented in this work highlight the advantage of measuring apparent diffusivity using NMR T 2 relaxometry in conjunction with numerical simulation to derive effective diffusivity and the corresponding matrix properties (i.e., tortuosity) of the system.

58 GEOSCIENCES↗
DOE OSTI2024

Phase field-volumetric lattice Boltzmann model of ion uptake in porous nuclear waste form materials under continuous flow

The flow field within the mesopores of sorbent particles plays a crucial role in radionuclide diffusion and ion uptake kinetics, thus, impacting the overall performance of porous nuclear waste form materials. To fundamentally understand the influence of microstructures and material properties on the radionuclide absorption and retention processes requires a coupled multi-physics model that considers the advection and diffusion within the flow field, the reaction at liquid-solid interfaces, and finally, the solid-state diffusion within a complex nanoporous medium. Here, this study employs the volumetric lattice Boltzmann method (VLBM) to accurately and efficiently calculate the steady state velocity field inside the mesopores of sorbent particles. The obtained velocity field is then utilized to calculate the advection of ions in the steady flow. A phase field (PF) model of ion uptake is used to describe the reaction occurring at the solid-liquid interface and diffusion inside the porous medium. The integrated PF-VLBM model is verified in terms of the mass conservation and numerical efficiency and validated qualitatively with experimental observation data. Then, it is applied to study the influence of thermodynamic and kinetic properties, as well as flow field conditions on the ion uptake kinetics. The numerical results demonstrate that the ion uptake kinetics in porous particles has three distinct stages, which is in agreement with the observations in continuous flow experiments. In the first stage, the kinetics is predominantly controlled by the flow field and ion diffusivity in the liquid phase. The kinetics in the second stage is primarily governed by ion diffusivity in the solid phase. In the third stage the system reaches a dynamic equilibrium with a net zero uptake flux at the interface. It is also found that porous structures significantly affect the efficiency and capacity of ion uptake. The simulation results can help to understand the physics behind the observed ion uptake kinetics in experiments and to facilitate the development of constitutive equations that can account for heterogeneous microstructures in engineering performance codes.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Nanoengineering Porous Silica for Thermal Management

Thermal insulation of solid materials originates from the nanoscale porous architectures to regulate thermal management in energy-critical applications from energy-efficient buildings to heat-sensitive energy devices. Here, we show nanoengineering of porous silica materials to control the architecture transition from mesoporous to nanocage networks. A low thermal conductivity of such a porous silica network is achieved at 0.018 W/(m K) while exhibiting a porosity of 92.05%, specific surface area of 504 m 2 /g, and pore volume of 2.37 cm 3 /g after ambient pressure drying. Meanwhile, the crosslinking of the porous silica and ceramic fiber frameworks show a tensile Young’s modulus of 2.8 MPa while maintaining high thermal insulation, which provides an effective thermal runway mitigation strategy for rechargeable lithium-ion batteries. Furthermore, the nanoengineering strategy reported here would shed light on achieving superthermal insulation of nanostructures for energy-critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Surface Hydration of Porous Nickel Hydroxides Facilitates the Reversible Adsorption of CO 2 from Ambient Air

Direct air capture (DAC) under humid ambient conditions typically requires the use of organic components, with sorbents that are purely inorganic in composition for the most part operating hundreds of degrees above room temperature. In this work, we report porous metal hydroxides as a novel class of water-tolerant, oxidatively and hydrothermally stable low-temperature sorbents that exhibit competitive DAC working capacities of 1.25 mmol/g over 5 consecutive temperature swing adsorption–desorption cycles in the presence of steam and oxygen. Aqueous miscible organic solvent treatments are used to create highly porous structures with surface areas exceeding 700 m 2 /g that capture CO 2 in the form of bicarbonates under dry conditions, and carbonates under wet conditions. Water exerts a facilitative rather than an inhibiting effect on CO 2 binding, and the presence of hydrating multilayers serves to stabilize carbonate species akin to moisture swing adsorbents except for the fact that solvation results in a remarkable (upto 10-fold) increase, not decrease, in DAC capacity. High-valent doping with cerium is used to improve DAC capacities by amplifying surface basicity, evidencing porous nickel hydroxides specifically (and porous metal hydroxides more generally) as a novel class of robust, earth-abundant DAC sorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Neural network–based pore flow field prediction in porous media using super resolution

Direct pore-scale simulations of fluid flow through porous media are computationally expensive to perform for realistic systems. Previous works have demonstrated using the geometry of the microstructure of porous media to predict the velocity fields therein based on neural networks. However, such trained neural networks do not perform well for unseen porous media with a large degree of heterogeneity. In this study we propose that incorporating a coarse velocity field in the input of neural networks is an effective way to improve the prediction performance. The coarse velocity field can be simulated with a low computational cost and provides global information to regularize the ill-posedness of the learning problem, which is usually caused by the use of local geometries due to the computational resource constraints. We show that incorporating the coarse-mesh velocity field significantly improves the prediction accuracy of the fine-mesh velocity field by comparison to the prediction that relies on geometric information alone, especially for the porous medium with a large interior vuggy pore space. We also show the flexibility of training the network in using coarse velocity fields with various resolutions. The results suggest that even using coarse velocity field with a very low resolution, the predictions are still enhanced and close to the ground truths. The feasibility of the method is further demonstrated by testing the trained network on real rocks. This study highlights the merits of incorporating a coarse-mesh velocity field into the input for neural networks, which provides global, physics-based information for the model, thereby improving the model's generalization capability.

42 ENGINEERING↗
DOE OSTI2022

Neutron tomography of porous aluminum electrodes used in electrocoagulation of groundwater

In this work, neutron computed tomography (CT) is employed to investigate the dissolution of porous aluminum electrodes during electrocoagulation (EC). Porous electrodes were chosen in efforts to reduce electric power requirements by using larger surface-area electrodes, having both inner and outer surface, for the EC process. Neutron CT allowed 3D reconstruction of the porous electrodes, and image analysis provided the volume of each electrode vs. thickness, which can indicate whether the inner surface is effectively involved in EC reactions. For the anode, the volume decreased uniformly throughout the thickness of the electrode, indicating that both the outer and inner surface participated in electrochemical dissolution, while the volume of the cathode increased uniformly vs. thickness, indicating deposition of material on both the outer and inner surface. The attenuation coefficient vs. thickness, increased for both anode and cathode, indicating surface chemistry changes. For the anode, the attenuation coefficient increased slightly but uniformly, probably due to aluminum oxide formation on the surface of the anode. For the cathode, the attenuation coefficient increased more than for the anode and nonuniformly. The higher increase in the attenuation coefficient for the cathode is due to precipitation of aluminum hydroxide on the electrode surface, which added hydrogen. Image analysis also showed that, although the attenuation coefficient increased throughout the thickness of the electrode, most of the hydroxide deposition occurred on the outer surface. Energy analysis showed that porous electrodes can be used to reduce process energy requirements by as much as 4 times compared to solid electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Estimation of extreme temperatures in direct solar methane pyrolysis within a porous medium

Porous media have wide application in renewable energy conversion processes, such as solar-thermal fuels production and decarbonization. Heat transport mechanisms within porous media can be highly complex, particularly under extreme conditions encountered in concentrated solar thermal reactors in which direct measurement of temperature is challenging. Here, we implement and report an inverse heat conduction model to estimate the temperature distribution throughout a porous substrate domain in a direct solar methane pyrolysis process. By solving a two-dimensional heat transfer problem and applying an inverse optimization algorithm, we estimate the quasi-steady state spatial temperature distribution in a fibrous porous carbon substrate. The results are validated indirectly by experimentally measured graphite deposition and a simplified reaction kinetic model.

finite difference method↗
NASA NTRS1993

(abstract) Alkali Metal Diffusion Through Porous Metal Electrodes in AMTEC Cells

The mechanisms of mass transport of an alkali metal through porous metal electrodes in alkali metal thermal-to-electric converter AMTEC cells is important in optimizing these high current density devices, but also affords the opportunity to investigate a variety of simple mass transport modes at high temperatures via electrochemical techniques. We have previously reported evidence of ionic, free molecular flow, and surface transport of sodium in several types of AMTEC electrodes. Quantitative investigations of Na transport through WPt(sub 3.5) via surface or grain boundary diffusion, and K transport through porous Mo electrodes by free molecular flow, over large ranges of temperature have been performed. WPt(sub 3.5) has especially low transport impedance over the 950 to 1200K temperature range. New results are the Na through porous WPt(sub 3.5) and K through porous Mo diffusion rates and mechanisms.

alkali metal mass transport porous metal electrode↗
NASA NTRS2017

Porous Plug Phase Separator and Superfluid Film Flow Suppression System for the Soft X-Ray Spectrometer Onboard Hitomi

When using superfluid helium in low gravity environments, porous plug phase separators are commonly used to vent boiloff gas while confining the bulk liquid to the tank. Invariably, there is a flow of superfluid film from the perimeter of the porous plug down the vent line. For the Soft X-ray Spectrometer onboard ASTRO-H (Hitomi), its approximately 30-liter helium supply has a lifetime requirement of more than 3 years. A nominal vent rate is estimated as ~ 30 ug/s, equivalent to ~ 0.7 mW heat load. It is therefore critical to suppress any film flow whose evaporation would not provide direct cooling of the remaining liquid helium. That is, the porous plug vent system must be designed to both minimize film flow and to ensure maximum extraction of latent heat from the film. The design goal for Hitomi is to reduce the film flow losses to <2 ug/s, corresponding to a loss of cooling capacity of <40 uW. The design adopts the same general design as implemented for Astro-E and E2, using a vent system composed of a porous plug, combined with an orifice, a heat exchanger, and knife-edge devices. In this paper, design, on-ground testing results and in-orbit performance are described.

Porous↗
DOE OSTI2025

Fabrication and Scale-Up of Porous Polybenzimidazole (PBI) Supports for Gas Separation Composite Membranes

Industrial gas separation often uses thin film composite (TFC) membranes comprising a porous support overlaid with a single-/multi-layer gas-selective thin film. An optimal porous support should possess high surface porosity to minimize gas transport resistance and nano-sized pores to ease pore penetration occurring during the thin film coating process. Good chemical and thermal stabilities are essential to withstand the aggressive solvents and heat required for the thin film coating and curing. However, few porous membranes satisfy all these requirements. This study presents a scalable membrane formation method of making highly porous polybenzimidazole (PBI) supports via non-solvent induced phase separation. This presentation also details the scale-up fabrication of PBI supports using a custom roll-to-roll membrane casting machine.

gas separation↗
DOE OSTI2023

Electrochemically Dealloyed 3D Porous Copper Nanostructure as Anode Current Collector of Li-Metal Batteries

The commercialization of high-energy Li-metal batteries is impeded by Li dendrites formed during electrochemical cycling and the safety hazards it causes. Here, a novel porous copper current collector that can effectively mitigate the dendritic growth of Li is reported. This porous Cu foil is fabricated via a simple two-step electrochemical process, where Cu-Zn alloy is electrodeposited on commercial copper foil and then Zn is electrochemically dissolved to form a 3D porous structure of Cu. The 3D porous Cu layers on average have a thickness of ≈14 um and porosity of ≈72%. This current collector can effectively suppress Li dendrites in cells cycled with a high areal capacity of 10 mAh cm -2 and under a high current density of 10 mA cm -2 . This electrochemical fabrication method is facile and scalable for mass production. In conclusion, results of advanced in situ synchrotron X-ray diffraction reveal the phase evolution of the electrochemical deposition and dealloying processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Synthesis and characteristics of porous ceramic tubes: a comparison of centrifugal casting and cold isostatic pressing methods

Short porous ceramic tubes in small quantities are needed for membrane-related research and development efforts and can be synthesized by centrifugal casting (CC) or cold isostatic pressing (CIP) methods. Here, this work evaluates the characteristics of porous ceramic tubes prepared by both methods and provides a strategy for selecting the proper method for ceramic tube fabrication, depending on its application. Samarium-doped ceria (SDC) with an average crystallite size of 10 nm was synthesized by a citrate method and used to make porous SDC tubes by the CC and CIP methods under different conditions. Porous SDC tubes were prepared from an aqueous suspension of the SDC powder with polyvinyl alcohol (PVA) using a cylindrical metallic mold, centrifugated at 4000–6000 rpm, followed by sintering in 1200–1500 °C. SDC tubes were also made by the CIP method from the SDC powder placed inside a cylindrical rubber bag followed by isostatic compression at 1379 bar. The pore structure of the SDC tubes was characterized by helium permeation and liquid nitrogen Archimedes method. Due to high compression pressure giving a low porosity green tube, the CIP method with lower sintering temperature produces SDC tubes with much lower porosity and average pore size and similar tortuosity compared to the CC methods. The CIP method should be used to prepare low porosity or dense ceramic tubes, and the CC method should be used for preparing high-porosity ceramic tubes or tubes with pore gradient tube wall.

36 MATERIALS SCIENCE↗