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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An integrated approach to derive relative permeability from capillary pressure

Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability curve can be determined. Relative permeability is a numerical description of the interaction between two or more fluids and the porous media. It is a critical parameter for various tools that characterize subsurface multiphase flow systems, such as numerical simulation for carbon sequestration, oil and gas development, and groundwater contamination remediation. Therefore, it is critical to get a good statistical distribution of relative permeability in the porous media under study. Empirical formula for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility that is required to match laboratory-derived relative permeability curves. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability curves. Mercury intrusion capillary pressure (MICP) data from the Morrow 'B' Sandstone coupled with interfacial tension and contact angle measurements were used to create a suite of relative permeability curves. Furthermore, these curves were then calibrated to a small sample of existing laboratory curves to elucidate common fitting parameters for the formation that were then used to create relative permeability curves from MICP data that does not have an associated laboratory-measured relative permeability curve.

58 GEOSCIENCES↗
DOE OSTI2022

Induced microearthquakes predict permeability creation in the brittle crust

Predicting the evolution of permeability accurately during stimulation at the reservoir scale and at the resolution of individual fractures is essential to characterize the fluid transport and the reactive/heat-transfer characteristics of reservoirs where stress exerts significant control. Here, we develop a hybrid machine learning (ML) model to visualize in situ permeability evolution for an intermediate-scale (~10 m) hydraulic stimulation experiment. This model includes an ML model that was trained using the well history of flow rate and wellhead pressure and MEQ data from the first three stimulation episodes to predict average permeability from the statistical features of the MEQs alone for later episodes. Moreover, a physics-inspired model is integrated to estimate in situ fracture permeability spatially. This method relates fracture permeability to fracture dilation and scales dilation to the equivalent MEQ magnitude, according to laboratory observations. The seismic data are then applied to define incremental changes in permeability in both space and time. Our results confirm the excellent agreement between the ground truth and model-predicted permeability evolution. The resulting permeability map defines and quantifies flow paths in the reservoir with the averaged permeability comparing favorably with the ground truth of permeability.

induced seismicity↗
DOE OSTI2025

Silver diamine fluoride differentially affects dentin and hypomineralized enamel permeabilities

OBJECTIVES: To investigate the physicochemical effect of silver diamine fluoride (SDF) by correlating permeability with mineral density and elemental composition of hypomineralized enamel and carious dentin. METHODS: Enamel and dentin from human carious primary teeth with and without SDF treatment in-vivo, and hypomineralized enamel from permanent molars with and without SDF treatment in-vitro were scanned using micro X-ray computed tomography. Spatial maps of biometals (calcium, zinc), phosphorus, and silver were generated using X-ray fluorescence microprobe. Permeabilities were computed using Porous Microstructure Analysis software. RESULTS: The intrinsic permeability of SDF-treated carious dentin was 14.3 % lower than untreated sound dentin (6.39e-15 ± 3.01e-15 m² vs 7.46e-15 ± 1.82e-15 m²; P < 0.0001), while untreated carious dentin was 98.4 % higher (1.48e-14 ± 7.11e-15 m²; P < 0.0001). SDF-treated and untreated transparent dentin showed similar reduced permeabilities (75.6 % and 78.4 % lower than untreated sound dentin, respectively; P = 0.93). Severely hypomineralized enamel showed permeability reaching 108.1 % of adjacent sound dentin (5.71e-15 ± 2.04e-15 m² vs 5.28e-15 ± 1.30e-15 m²; P = 0.1409) and was significantly higher than mildly hypomineralized enamel (1.39e-15 ± 1.04e-15 m²; P < 0.0001). SDF treatment did not significantly impact the permeability of severely hypomineralized enamel (12.4 % reduction; P = 0.07). Principal component regression identified Zn level as a significant effector of tissue permeabilities in carious primary teeth (P < 0.0001). SIGNIFICANCE: This study introduces a computational method to measure dental tissue permeability, and demonstrates that SDF significantly reduces permeability in carious dentin but not intact hypomineralized enamel. The study reveals biometal Zn localization can alter dentin and enamel permeabilities, providing new insights into pathobiological mechanisms underlying caries and hypomineralization.

Chou, Conrad↗
DOE OSTI2026

AnisONet: A deep neural operator-based anisotropic permeability upscaler from pore to Darcy scale

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. In this work, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

42 ENGINEERING↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Zero-Valent Iron Permeable Reactive Barrier to Remediate Volatile Organic Compounds in Groundwater - 20209

The US DOE-SRS, the US EPA, and the South Carolina Department of Health and Environmental Control determined it was appropriate to perform a non-time critical removal action at the P-Area Groundwater Operable Unit at the SRS to reduce the mass and downgradient transport of trichloroethylene in the P-Area groundwater plume. Contaminated groundwater discharges to a nearby stream, Steel Creek, within the SRS boundaries, resulting in trichloroethylene concentrations above the maximum contaminant level. Impact to surface water is limited in areal extent and supported by recently collected characterization data. The P-Area Groundwater Operable Unit encompasses the groundwater beneath an industrial area within SRS, P Area, where the P-Reactor once operated. The boundaries of the P-Area Groundwater Operable Unit extend northwest to Steel Creek, northeast toward PAR Pond, and southeast to Meyers Branch. Groundwater in the Upper Three Runs Aquifer of the P-Area Groundwater Operable Unit has been impacted by reactor and facility operations between 1954 and 1991, including tritium and volatile organic compounds. The P-Area surface units contributing to groundwater contamination were remediated as part of the P-Area Operable Unit in 2011. The P-Reactor closure is one of the first of its kind in the DOE Complex and is one of only a few full-sized production reactors in the US to undergo completion of final closure activities. The nature and extent of groundwater contamination was determined using a variety of investigative approaches such as groundwater monitoring wells, direct-push technology, and surface water samples. Groundwater contamination associated with trichloroethylene is primarily exhibited in a narrow plume that extends from the source area at P-Reactor and west to Steel Creek. Maximum contaminant level exceedances in groundwater occur over an area of ∼6.9 hectares for trichloroethylene with concentrations as high as 7.7 milligrams per liter. To the west of the P-Area facility area, the trichloroethylene groundwater plume is controlled by a buried geologic feature, assumed to be an old stream bed, that further narrows the groundwater plume in what has been designated as the 'neck area.' This narrowing of the groundwater plume provides an ideal location for a treatment barrier. The non-time critical removal action alternative chosen is to install a zero-valent iron permeable reactive barrier within the neck area of the trichloroethylene groundwater plume, perpendicular to groundwater flow direction. This technology will provide a treatment barrier that will reduce trichloroethylene groundwater concentrations by 90% and has an anticipated useful life of at least 25 years. A pre-design investigation was performed in the neck area to confirm site lithology, hydrogeology, geochemistry, and extent of trichloroethylene contamination prior to a final design. A treatability study, conducted as part of the pre-design investigation, indicated that the subsurface and groundwater in the PArea Groundwater Operable Unit is compatible with the zero-valent iron and will not lead to excessive buildup from mineralization/precipitation or biofouling. Probabilistic modeling was conducted using field and laboratory data to determine the expected performance of the zero-valent iron permeable reactive barrier. The model simulations indicated that a 3.81-centimeter thick barrier would provide greater than 90% reduction of trichloroethylene groundwater concentration. The final design of the zero-valent iron permeable reactive barrier is a barrier that will extend 80.5 linear meters in a 'zigzag' orientation to best transect the trichloroethylene plume and account for varying groundwater flow. The barrier will be installed from 13.7 meters below ground surface to 41.1 meters below ground surface for 65.8 linear meters and from 13.7 meters below ground surface to 36.6 meters below ground surface for 14.6 linear meters, the base of which is 'keyed' into a low permeability zone. The barrier is designed to a thickness of 10.2 centimeters, which was determined to reduce the trichloroethylene groundwater concentrations by greater than 90% with a safety factor of 2.67. A total of approximately 689 metric tons of zero-valent iron will be injected through 22 injection wells spaced 3.66 meters apart, using guar to suspend the zero-valent iron. Zero-valent iron permeable reactive barrier construction will be monitored through 23 installed resistivity receivers offset 7.32 meters from the zerovalent iron permeable reactive barrier. The zero-valent iron will be energized with a low-voltage 100 Hertz signal during injection and will be monitored using the resistivity receivers to ensure complete coalescence of the zero-valent iron permeable reactive barrier. The zero-valent iron was sized to have a hydraulic conductivity greater than the natural subsurface, thus promoting groundwater flow through the barrier. As contaminated groundwater contacts the zero-valent iron, volatile organic compounds, including trichloroethylene, are immediately degraded to harmless compounds such as ethylene. The performance of the zero-valent iron permeable reactive barrier will be monitored using three upgradient monitoring well clusters, six downgradient monitoring well clusters, and four in-wall monitoring wells. The in-wall monitoring wells will indicate immediate reduction of trichloroethylene mass in the groundwater and allow analyses of the zero-valent iron permeable reactive barrier health. (authors)

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

Wellbore cement fracture permeability as a function of confining stress and pore pressure

Cemented annulus fractures are a major leakage path in a wellbore system, and their permeability plays an important role in the behavior of fluid flow through a leaky wellbore. The permeability of these fractures is affected by changing conditions including the external stresses acting on the fracture and the fluid pressure within the fracture. Laboratory gas flow experiments were conducted in a triaxial cell to evaluate the permeability of a wellbore cement fracture under a wide range of confining stress and pore pressure conditions. For the first time, an effective stress law that considers the simultaneous effect of confining stress and pore pressure was defined for the wellbore cement fracture permeability. Here the results showed that the effective stress coefficient (λ) for permeability increased linearly with the Terzaghi effective stress ( -p) with an average of λ = 1 in the range of applied pressures. The relationship between the effective stress and fracture permeability was examined using two physical-based models widely used for rock fractures. The results from the experimental work were incorporated into numerical simulations to estimate the impact of effective stress on the interpreted hydraulic aperture and leakage behavior through a fractured annular cement. Accounting for effective stress-dependent permeability through the wellbore length significantly increased the leakage rate at the wellhead compared with the assumption of a constant cemented annulus permeability.

58 GEOSCIENCES↗
DOE OSTI2026

Consolidation and Permeability of the B1 and D1 Gas Hydrate Bearing Sands and Associated Seal Sediments of the Extended-Duration Gas Production Test Site on the Alaska North Slope

Gas hydrate, a solid combination of gas (mostly methane in nature) and water molecules stable at low temperatures and elevated pressures, occurs naturally in marine and permafrost-associated environments. Gas hydrate reservoirs, such as those in the Alaska North Slope, have been considered potential energy resources for gas production. To understand the petrophysical and geo-mechanical characteristics of the reservoir, core samples retrieved from the site of the JOGMEC-DOE-USGS collaborative gas hydrate R&D project have been analyzed in the laboratory for their hydraulic and mechanical properties. This paper focuses on both seal and reservoir samples associated with the B1 and D1 sands, which are evaluated for index properties (including porosity, grain size distribution, liquid and plastic limits, specific surface area, and specific gravity), consolidation, permeability, and water retention. Furthermore, the reservoir core samples were tested with pore-filling, laboratory-grown tetrahydrofuran hydrate, in order to assess reservoir behavior during gas production from hydrates. Under simulated in situ stress conditions, the seal and hydrate-free reservoir cores had a permeability anisotropy ratio of k h /k v = 3.0−5.0, and k h /k v = 2.4−3.0 for the reservoir tetrahydrofuran hydrate-bearing cores. The data suggest that depressurizing the reservoir to induce hydrate dissociation alters the reservoir effective permeability in three ways: permeabilities decrease due to porosity lost (e.g., the initial reservoir thickness can decrease by up to 5% upon 7 MPa depressurization), permeability increases due to the loss of solid hydrate in the pore space, and permeability anisotropy k h /k v decreases in response to the evolving pore-space geometry. We show that given the simulated in situ gas hydrate saturations (i.e., S h = 32% in core 7P-2E and S h = 21% in core 20P-4), gas production from the dissociation of tetrahydrofuran hydrate in the two tested cores results in a net increase in effective permeability and a decrease in k h /k v . This study highlights the importance of investigating seal and reservoir sediments and the impacts of depressurization on the porosity and permeability responses during production.

Geological materials↗
DOE OSTI2024

Atomic view of photosynthetic metabolite permeability pathways and confinement in synthetic carboxysome shells

Carboxysomes are protein microcompartments found in cyanobacteria, whose shell encapsulates rubisco at the heart of carbon fixation in the Calvin cycle. Carboxysomes are thought to locally concentrate CO 2 in the shell interior to improve rubisco efficiency through selective metabolite permeability, creating a concentrated catalytic center. However, permeability coefficients have not previously been determined for these gases, or for Calvin-cycle intermediates such as bicarbonate (HCO$^–_3$), 3-phosphoglycerate, or ribulose-1,5-bisphosphate. Starting from a high-resolution cryogenic electron microscopy structure of a synthetic β-carboxysome shell, we perform unbiased all-atom molecular dynamics to track metabolite permeability across the shell. The synthetic carboxysome shell structure, lacking the bacterial microcompartment trimer proteins and encapsulation peptides, is found to have similar permeability coefficients for multiple metabolites, and is not selectively permeable to HCO$^–_3$ relative to CO 2 . To resolve how these comparable permeabilities can be reconciled with the clear role of the carboxysome in the CO 2 -concentrating mechanism in cyanobacteria, complementary atomic-resolution Brownian Dynamics simulations estimate the mean first passage time for CO 2 assimilation in a crowded model carboxysome. Despite a relatively high CO 2 permeability of approximately 10 -2 cm/s across the carboxysome shell, the shell proteins reflect enough CO 2 back toward rubisco that 2,650 CO 2 molecules can be fixed by rubisco for every 1 CO 2 molecule that escapes under typical conditions. The permeabilities determined from all-atom molecular simulation are key inputs into flux modeling, and the insight gained into carbon fixation can facilitate the engineering of carboxysomes and other bacterial microcompartments for multiple applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Permeability of membranes to amino acids and modified amino acids: mechanisms involved in translocation

The amino acid permeability of membranes is of interest because they are one of the key solutes involved in cell function. Membrane permeability coefficients (P) for amino acid classes, including neutral, polar, hydrophobic, and charged species, have been measured and compared using a variety of techniques. Decreasing lipid chain length increased permeability slightly (5-fold), while variations in pH had only minor effects on the permeability coefficients of the amino acids tested in liposomes. Increasing the membrane surface charge increased the permeability of amino acids of the opposite charge, while increasing the cholesterol content decreased membrane permeability. The permeability coefficients for most amino acids tested were surprisingly similar to those previously measured for monovalent cations such as sodium and potassium (approximately 10(-12)-10(-13) cm s-1). This observation suggests that the permeation rates for the neutral, polar and charged amino acids are controlled by bilayer fluctuations and transient defects, rather than partition coefficients and Born energy barriers. Hydrophobic amino acids were 10(2) more permeable than the hydrophilic forms, reflecting their increased partition coefficient values. External pH had dramatic effects on the permeation rates for the modified amino acid lysine methyl ester in response to transmembrane pH gradients. It was established that lysine methyl ester and other modified short peptides permeate rapidly (P = 10(-2) cm s-1) as neutral (deprotonated) molecules. It was also shown that charge distributions dramatically alter permeation rates for modified di-peptides. These results may relate to the movement of peptides through membranes during protein translocation and to the origin of cellular membrane transport on the early Earth.

Review↗
NASA NTRS1993

Influence of fiber packing structure on permeability

The study on the permeability of an aligned fiber bundle is the key building block in modeling the permeability of advanced woven and braided preforms. Available results on the permeability of fiber bundles in the literature show that a substantial difference exists between numerical and analytical calculations on idealized fiber packing structures, such as square and hexagonal packing, and experimental measurements on practical fiber bundles. The present study focuses on the variation of the permeability of a fiber bundle under practical process conditions. Fiber bundles are considered as containing openings and fiber clusters within the bundle. Numerical simulations on the influence of various openings on the permeability were conducted. Idealized packing structures are used, but with introduced openings distributed in different patterns. Both longitudinal and transverse flow are considered. The results show that openings within the fiber bundle have substantial effect on the permeability. In the longitudinal flow case, the openings become the dominant flow path. In the transverse flow case, the fiber clusters reduce the gap sizes among fibers. Therefore the permeability is greatly influenced by these openings and clusters, respectively. In addition to the porosity or fiber volume fraction, which is commonly used in the permeability expression, another fiber bundle status parameter, the ultimate fiber volume fraction, is introduced to capture the disturbance within a fiber bundle.

Cai, Zhong↗
NASA NTRS1954

Permeability and Strength Measurements on Sintered, Porous, Hollow Turbine Blades Made by the American Electro Metal Corporation under Office of Naval Research Contract N-ONR-295 (01)

An experimental investigation was made to determine the permeability and strength characteristics of a number of sintered, porous, hollow turbine rotor blades and to determine the effectiveness of the blade fabrication method on permeability control. The test blades were fabricated by the American Electro Metal Corporation under a contract with the Office of Naval Research, Department of the Navy, and were submitted to the NACA for testing. Of the 22 test blades submitted, ten were sintered but not coined, five were sintered and coined, and seven were sintered and not coined but contained perforated reinforcements integral with the blade shells. Representative samples of each group of blades were tested. Large variations in permeability in both chordwise and spanwise directions were found. Local deviations as large as 155 to -85 percent from prescribed values were found in chordwise permeability. Only one blade, an uncoined one, had a chordwise permeability variations which reasonably approached that specified. Even for this blade, local deviations exceeded 10 percent. Spanwise permeability, specified to be held constant, varied as much as 50 percent from root to tip for both an uncoined and a coined blade. Previous NACA analyses have shown that in order to maintain proper control of blade wall temperatures, permeability variations must not exceed plus or minus 10 percent. Satisfactory control of permeability in either the chordwise or the spanwise direction was not achieved in the blades tested. Spin tests made at room temperature for six blades revealed the highest material rupture strength to be 8926 pounds per square inch. This value is about one third the strength required for rotor blades in present-day turbojet engines. The lowest value of blade strength was 1436 pounds per square inch.

Richards, Hadley T.↗
NASA NTRS2018

Prediction of Thermal Protection System Material Permeability and Hydraulic Tortuosity Factor Using Direct Simulation Monte Carlo

Carbon preforms used in Thermal Protection System (TPS) materials are 80 to 90% porous, allowing for boundary layer and pyrolysis gases to flow through the porous regions. The bulk material properties such as permeability and hydraulic tortuosity factor affect the transport of the boundary layer gases. The use of Direct Simulation Monte Carlo along with the Klinkenberg permeability formulation allows us to compute the continuum permeability and Knudsen correction factor for flow in the transition regime. In this work, we have computed the permeability for two types of carbon preforms, namely, Morgan Felt and FiberForm, and assessed the effect of orientation on the permeability. Since both the materials are anisotropic, the permeability was found to depend on orientation, wherein, the materials are more permeable in the in-plane orientation than the through-thickness orientation. The through-thickness orientation was also more tortuous compared to the in-plane material orientation. Compared to Morgan Felt, FiberForm is less permeable, in both, through thickness and in-plane directions.

Jambunathan, Revathi↗
DOE OSTI2023

Permeability of Single–Layer–Free–Standing Meshes at Varying Capillary Pressure via a Novel Method

The permeability of mesh wicks is important for various applications, including two–phase heat transfer. However, the understanding of the permeability of single–layer, free–standing mesh wicks, with liquid–gas interfaces on both sides, is limited. A novel and simpler method is presented to determine the permeability of a free–standing wick and apply it to a representative mesh. This method involves modifying the capillary pressure via elevation and simultaneously measuring the permeability to determine the permeability–capillary pressure relationship. When applied to a copper mesh with plain weave having undergone surface cleaning, the permeability is found to decrease as capillary pressure for deionized water increases. A dimensional analysis is presented to generalize this data for other mesh sizes with similar weaves and fluids. The behavior of mesh in application is modeled, based on the integration of Darcy's law with an analytic function fit to measured data, and parametric studies are conducted to investigate the superficial velocity of liquids through the mesh under varying driving pressures, transport lengths, and liquid viscosity, based on the obtained capillary pressure–permeability relationship. This study provides valuable insights into the transport properties of mesh wicks, with potential applications in fields such as electronics cooling, electrochemical devices, and fluid purification technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Side-Chain and Ring-Size Effects on Permeability in Artificial Water Channels

Artificial water channels (AWCs) have emerged as a promising framework for stable water permeation, with water transport rates comparable to aquaporins (3.4–40.3 × 10 8 H 2 O/channel/s). In this study, we probe the influence of ring-size and side-chain length on the water permeability observed within a class of AWCs termed ligand-appended pillar[n]arenes (LAPs) that have an adjustable ring-size (m) and side-chain length (n). Through all-atom molecular dynamics simulations, we calculate the permeability of these channels using the collective diffusion model and find their permeabilities. We characterize the mechanistic influence of pillar[n]arene ring-size and side-chain length on the channel water permeability by analyzing the characteristics of the internal permeating water-wire and the surrounding channel structure. We observe that water permeability decreases as a function of increasing ring-size due to increases in hydrophilic contacts between the permeating water-wire and the oxygen groups on the channel wall. Further, we observe an increase in water permeability as a function of side-chain length due to increased partitioning of the channel terminal groups into the hydrophilic blocks of the surrounding bilayer. For the LAP6 channel, with increase in side-chain length, the distance between terminal groups increases and leads to an increase in pore size, thereby enhancing water permeability. In the case of LAP5, as side-chain length increases, the channel displays a compensatory effect between tilt and bend angle due to the flexible side-chains. Such flexibility leads to higher terminal group partitioning in the hydrophilic blocks of the bilayer and extends the permeating water-wire. Furthermore, this increase in water-wire length and hydrophilic block access overcomes the nonmonotonic pore size trend in pillar[5]arene channels.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Viscous Dissipation and Apparent Permeability of Gas Flow in Nanoporous Media

The nanopore confinement effect can increase the mobility for gas flow in shale rocks, and the apparent permeability is usually adopted to account for this effect. However, most of the apparent permeability calculation models are derived based on straight channels (capillaries) and neglect the influence of end effect. The end effect is caused by the bending of streamlines which yields more viscous dissipation and additional flow resistance for gas flow in nanoporous media. Here, for the first time the viscous dissipation for gas flow in nanoporous media is analyzed. In addition, an improved apparent permeability calculation model is proposed based on the Beskok-Karniadakis (B-K) model by introducing the influence of end effect, which is characterized by the ratio of the length to the width of a short channel (L/H). The accuracy of this model is validated by lattice Boltzmann simulations of gas flow in three different geometries: an infinite-length straight channel, a finite-length straight channel, and nanoporous media. For the infinite-length straight channel, there is no end effect therefore both the B-K model and the improved model can well predict the apparent permeability. However, for the finite-length straight channel and nanoporous media, the original B-K model overestimates the gas apparent permeability as it neglects the influence of end effect, while the improved model can still well predict the gas apparent permeability. In summary, the improved apparent permeability calculation model is more reasonable in predicting gas mobility in nanoporous media.

58 GEOSCIENCES↗