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Results for “permeability models”

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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Impacts of Biomass Feedstock Pre-Processing on Heat and Mass Transfer During Pyrolysis Using X-Ray Computed Tomography and Multiscale Modeling

Knowledge of the transport properties of biomass particles such as porosity, tortuosity, and permeability is paramount for high-fidelity modeling of biomass pyrolysis due to the heat and mass transfer limitations imposed by particle microstructure. X-ray computed tomography (XCT) is a non-destructive imaging method that enables full 3D reconstructions of the biomass particle microstructure with high resolution, permitting direct calculation of porosity, tortuosity, and permeability from real particle geometries. In this study, XCT imaging revealed the 3D microstructures of particles and chars from pyrolytic conversion of cylindrically cut or milled/pelletized loblolly pine samples. The porosity, tortuosity, and permeability were calculated directly from the XCT geometries via open-source microstructural analysis tool MATBOX+TauFactor (https://github.com/NREL/MATBOX_Microstructure_analysis_toolbox) and computational fluid dynamics (CFD) simulations using our solver, Mesoflow (https://github.com/NREL/mesoflow). These properties were used in a reactor scale model developed in COMSOL of the single particle reactor at NREL to investigate the impact of feedstock pre-processing on biomass conversion during pyrolysis with rigorous experimental validation.

biomass↗
DOE OSTI2020

Effects of natural zeolites on field-scale geologic noble gas transport

Improving predictive models for noble gas transport through natural materials at the field-scale is an essential component of improving US nuclear monitoring capabilities. Several field-scale experiments with a gas transport component have been conducted at the Nevada National Security Site (Non-Proliferation Experiment, Underground Nuclear Explosion Signatures Experiment). However, the models associated with these experiments have not treated zeolite minerals as gas adsorbing phases. This is significant as zeolites are a common alteration mineral with a high abundance at these field sites and are shown here to significantly fractionate noble gases during field-scale transport. This fractionation and associated retardation can complicate gas transport predictions by reducing the signal-to-noise ratio to the detector (e.g. mass spectrometers or radiation detectors) enough to mask the signal or make the data difficult to interpret. Omitting adsorption-related retardation data of noble gases in predictive gas transport models therefore results in systematic errors in model predictions where zeolites are present.Herein is presented noble gas adsorption data collected on zeolitized and non-zeolitized tuff. Experimental results were obtained using a unique piezometric adsorption system designed and built for this study. Data collected were then related to pure-phase mineral analyses conducted on clinoptilolite, mordenite, and quartz. These results quantify the adsorption capacity of materials present in field-scale systems, enabling the modeling of low-permeability rocks as significant sorption reservoirs vital to bulk transport predictions.

58 GEOSCIENCES↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate

Characterizing the properties of groundwater aquifers is essential for predicting aquifer response and managing groundwater resources. In this work, we develop a high-dimensional scalable Bayesian inversion framework governed by a three-dimensional quasi-static linear poroelastic model to characterize lateral permeability variations in groundwater aquifers. In this work, we determine the maximum a posteriori (MAP) point of the posterior permeability distribution from centimeter-level surface deformation measurements obtained from Interferometric Synthetic Aperture Radar (InSAR). The scalability of our method to high parameter dimension is achieved through the use of adjoint-based derivatives, inexact Newton methods to determine the MAP point, and a Mat´ern class sparse prior precision operator. Together, these guarantee that the MAP point is found at a cost, measured in number of forward/adjoint poroelasticity solves, that is independent of the parameter dimension. We apply our methodology to a test case for a municipal well in Mesquite, Nevada, in which InSAR and GPS surface deformation data are available. We solve problems with up to 320,824 state variable degrees of freedom (DOFs) and 16,896 parameter DOFs. A consistent treatment of noise level is employed so that the aquifer characterization result does not depend on the pixel spacing of surface deformation data. Our results show that the use of InSAR data significantly improves characterization of lateral aquifer heterogeneity, and the InSAR-based aquifer characterization recovers complex lateral displacement trends observed by independent daily GPS measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Experimental study correlating damage and permeability in concrete using confined, flattened Brazilian disks

This research experimentally studies coupled hydro-mechanical relationships using flattened Brazilian disks under confinement, with concurrent permeability measurements and acoustic emission monitoring. A series of tests are performed on concrete, as representative samples of brittle geo-materials, under a range of confining stresses between 2.76 and 13.79 MPa. Acoustic emission data is used to quantify damage and identify damage thresholds. Damage is then correlated to pre-peak changes in permeability. The advancement of models that couple transport properties to mechanical responses are of interest in the fields of carbon sequestration, hydrofracking, production of geothermal energy, induced seismicity, and underground nuclear waste storage, and these experiments validate a novel experimental approach towards investigating hydro-mechanical relationships.

Boyce, S.↗
DOE OSTI2023

Simulation of Chilled-water Injection at EGS Collab Testbed 2 using the GEOS simulation framework

This work seeks to model the chilled-water injection that occurred at EGS Collab Testbed 2 as part of experiment 3, using the open-source GEOS simulation framework. Modeling of this process, previously conducted using STOMP-GT, confirmed that thermal breakthrough was expected to occur during the chill water injection and may have been masked by strong Joule-Thompsons effects induced by large pressure drops between the fractures and the production wells. Building a GEOS numerical model of this process will allow to include additional physics. In fact, GEOS can model fully coupled thermo-poromechanical processes in fractured porous media and can be employed to model the process of artificially enhancing rock permeability by hydraulically fracturing the rocks. For example, the GEOS model could help understanding the dynamic nature of the EGS collab experiment 3 flow system. Since the limited time and resources do not allow for an extensive study, the focus is on building a baseline model of this process. As such, the main outcomes of this modelling efforts are listed below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Fast permeability measurement for tight reservoir cores using only initial data of the one chamber pressure pulse decay test

Here, in this study, a mathematical model for fast determination of the permeabilities of tight rocks using measurements taken from the initial period of the One Chamber Pressure Pulse Decay (OC-PPD) test is presented. The model applies to measurements taken both before and after the pressure pulse front has reached the downstream end of the specimen. The analytical solutions for the pressure decay in the upstream chamber are derived based on a parabolic arc approximation of pore pressure distribution along the test specimen. This approximation allows converting the initial–boundary value problem of fluid diffusion in the specimen, governed by partial differential equations, to a system of ordinary differential equations that can be easily solved by explicit formulae. Thus, an explicit formula for the pressure decay rate is obtained, which enables inverse analysis of the initial experimental data to estimate the rock permeability. The proposed method expedites the pulse decay test as it does not require the system to reach equilibrium. The method is validated with three sets of experimental data of the OC-PPD test using helium as the diffusing fluid, for which the relative error of the permeability is found to be less than 6%. This method is particularly useful if the equilibrium time of the pulse decay test for rock specimens with permeabilities in the range of nano-Darcy takes hours or days.

early-time solution↗
DOE OSTI2022

Cation–Ligand Interactions Dictate Salt Partitioning and Diffusivity in Ligand-Functionalized Polymer Membranes

Membranes are an attractive alternative to current thermal separations due to their scalability and energy efficiency in desalinating water. Unfortunately, many of the conventional membrane materials available today are unable to differentiate between ionic solutes, especially alkali cations, compromising their use in ion–ion separations. Inspired by the ion-specific interactions exhibited by biological ion channels, recent research efforts have focused on synthesizing and characterizing new polymeric materials that incorporate ligands into polymer networks to bias solubility and/or diffusivity of one cationic species over another. Despite these efforts, little is known about the influence of incorporating ligands into polymer membranes on solubility and diffusivity of the complexing species. In this study, we first build a qualitative model of salt partitioning, diffusivity, and permeability in generic cation-complexing ligand-functionalized polymer membranes. Next, to validate our model and hypotheses, we perform atomistic molecular dynamics simulations of a 12-crown-4-functionalized membrane in the presence of alkali halide salts at low concentration. Generally, cation complexation enhances cation solubility but decreases diffusivity. Interestingly, the reduction in diffusivity is predicted to be larger than the enhancement in solubility for materials which operate by the mechanisms proposed in our physical picture, ultimately resulting in a reduction in the permeability of the selectively complexing ion.

36 MATERIALS SCIENCE↗
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 OSTI2025

Compressibility and permeability of particulated non-recyclable municipal solid waste

Biofuels from non-recyclable municipal solid waste (NMSW) stand at the forefront of energy sustainability. However, their widespread adoption is hampered by persistent material handling issues stemming from the variability in NMSW material properties. An enhanced understanding of particulated NMSW properties, particularly compressibility and permeability, is essential to address the feedstock handling challenges and optimize the handling equipment. This study measures the compressibility and gas permeability of five streams of NMSW materials (i.e., rigid plastics, cardboard, thin film, paper, and foam) and their mixtures under different stress conditions. The results highlight the significant variability in compressibility and gas permeability among different streams, as well as the impacts of particle sizes. A semi-empirical model capable of predicting the gas permeability of NMSW mixtures is established and validated. Here, the results also highlight that reduced NMSW particle size helps promote consistency in NMSW feedstock’s physical and mechanical properties, which is favored for handling equipment design in waste-to-energy recovery facilities.

09 BIOMASS FUELS↗
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 OSTI2021

Seismic waves in medium with poroelastic/elastic interfaces: a two-dimensional P-SV finite-difference modelling

SUMMARY We present a new methodology of the finite-difference (FD) modelling of seismic wave propagation in a strongly heterogeneous medium composed of poroelastic (P) and (strictly) elastic (E) parts. The medium can include P/P, P/E and E/E material interfaces of arbitrary shapes. The poroelastic part can be with (i) zero resistive friction, (ii) non-zero constant resistive friction or (iii) JKD model of the frequency-dependent permeability and resistive friction. Our FD scheme is capable of subcell resolution: a material interface can have an arbitrary position in the spatial grid. The scheme keeps computational efficiency of the scheme for a smoothly and weakly heterogeneous medium (medium without material interfaces). Numerical tests against independent analytical, semi-analytical and spectral-element methods prove the efficiency and accuracy of our FD modelling. In numerical examples, we indicate effect of the P/E interfaces for the poroelastic medium with a constant resistive friction and medium with the JKD model of the frequency-dependent permeability and resistive friction. We address the 2-D P-SV problem. The approach can be readily extended to the 3-D problem.

58 GEOSCIENCES↗
DOE OSTI2026

Peridynamic modeling of cementitious materials for nuclear waste management

Radioactive and hazardous waste generated from fuel processing plants, nuclear reactors, and hospitals, requires effective management strategies. Cementitious materials are widely applied for these needs, serving as structural materials, reactive barriers, or waste forms. In these applications cracking poses a significant risk to performance, driven by inconsistent shrinkage of the components and the varying strength and permeability of their interfaces. Traditional modeling approaches face challenges in representing the complex fracture behavior of cementitious materials due to the reliance on spatial derivatives and difficulties with mesh generation. Here, to overcome these limitations, we employ peridynamics, a novel continuum mechanics formulation that uses integrals to describe mechanical equilibrium, avoiding discontinuities associated with traditional methods. Through incorporation of a bi-linear softening model and quasistatics, an experimentally validated model for Portland cement concrete samples was created. Mechanical parameters, including compressive strength and elastic modulus were validated and variation due to aggregate packing was evaluated. Additionally, sensitivity analysis of the peridynamic parameters to the bulk material properties was established. The results lay the groundwork for evaluating the impact of unique conditions of cementitious waste forms that can be assessed to improve the reliability of waste management strategies.

Aggregates↗
DOE OSTI2020

Exploring an ‘ideal hill': how lithology and transport mechanisms affect the possibility of a steady state during weathering and erosion

We introduce a model of chemical reaction within hills to explore how evolving porosity (and by inference, permeability) affects flow pathways and weathering. The model consists of hydrologic and reactive-transport equations that describe alteration of ferrous minerals and feldspar. These reactions were chosen because previous work emphasized that oxygen- and acid-driven weathering affects porosity differently in felsic and mafic rocks. A parameter controlling the order of the fronts is presented. In the absence of erosion, the two reaction fronts move at different velocities and the relative depths depend on geochemical conditions and starting composition. In turn, the fronts and associated changes in porosity drastically affect both the vertical and lateral velocities of water flow. For these instances, estimates of weathering advance rates based on simple models that posit unidirectional constant-velocity advection do not apply. In the model hills, weathering advance rates diminish with time as the Darcy velocities decrease with depth. The system can thus attain a dynamical steady state at any erosion rate where the regolith thickness is constant in time and velocities of both fronts become equal to one another and to the erosion rate. The slower the advection velocities in a system, the faster it attains a steady state. For example, a massive rock with relatively fast-dissolving minerals such as diabase -- where solute transport across the reaction front mainly occurs by diffusion -- can reach a steady state more quickly than granitoid rocks in which advection contributes to solute transport. The attainment of a steady state is controlled by coupling between weathering and hydrologic processes that force water to flow horizontally above reaction fronts where permeability changes rapidly with depth and acts as a partial barrier to fluid flow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bayesian Poroelastic Aquifer Characterization From InSAR Surface Deformation Data. 2. Quantifying the Uncertainty

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. We develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. Here we design a generalized preconditioned Crank-Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank-based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two-dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Coreflood Tests to Evaluate Enhanced Oil Recovery Potential of Wettability-Altering Surfactants for Oil-Wet Heterogeneous Carbonate Reservoirs

Summary Oil-wetness and heterogeneity are two main factors that result in low oil recovery (OR) by waterflood in carbonate reservoirs. The injected water is likely to flow through high-permeability regions and bypass the oil in the low-permeability matrix. In this study, systematic coreflood tests were carried out in both “homogeneous” cores and “heterogeneous” cores with a wettability-altering surfactant. The homogeneous coreflood tests were conducted to evaluate surfactant retention, as well as to compare tertiary surfactant flooding with secondary surfactant flooding. The heterogeneous coreflood test was proposed to model bypassing in low-permeability matrix during waterfloods, and dynamic imbibition of surfactant into the low-permeability matrix. Surfactant retention results suggest that retention increases as initial oil saturation decreases. The retention of selected surfactant in the target reservoir cores was measured to be within a range of 0.07–0.12 mg/g-rock, which is economically acceptable. The results of homogeneous coreflood tests showed that both secondary waterflood and secondary surfactant flood can achieve high OR (>50%) from relatively homogeneous oil-wet cores. A shut-in phase after the surfactant injection resulted in a surge in oil production, which suggests that enough time should be given for wettability alteration by surfactants. The results of heterogeneous coreflood tests showed that more oil is bypassed in the tighter matrix by waterflood if the permeability is higher in the flooded layer and this bypassed oil is the target for the wettability-altering surfactant floods. Slow wettability-altering surfactant injection leads to imbibition into bypassed regions. When the oil-wet carbonate reservoirs have large unswept regions after waterflood, wettability-altering surfactants can significantly improve OR if enough time is given for imbibition.

Engineering↗
DOE OSTI2021

Heterogeneous multiphase flow properties of volcanic rocks and implications for noble gas transport from underground nuclear explosions

Of interest to the Underground Nuclear Explosion Signatures Experiment are patterns and timing of explosion-generated noble gases that reach the land surface. The impact of potentially simultaneous flow of water and gas on noble gas transport in heterogeneous fractured rock is a current scientific knowledge gap. This article presents field and laboratory data to constrain and justify a triple continua conceptual model with multimodal multiphase fluid flow constitutive equations that represents host rock matrix, natural fractures, and induced fractures from past underground nuclear explosions (UNEs) at Aqueduct and Pahute Mesas, Nevada National Security Site, Nevada, USA. Capillary pressure from mercury intrusion and direct air–water measurements on volcanic tuff core samples exhibit extreme spatial heterogeneity (i.e., variation over multiple orders of magnitude). Petrographic observations indicate that heterogeneity derives from multimodal pore structures in ash-flow tuff components and post-depositional alteration processes. Comparisons of pre- and post-UNE samples reveal different pore size distributions that are due in part to microfractures. Capillary pressure relationships require a multimodal van Genuchten (VG) constitutive model to best fit the data. Relative permeability estimations based on unimodal VG fits to capillary pressure can be different from those based on bimodal VG fits, implying the choice of unimodal vs. bimodal fits may greatly affect flow and transport predictions of noble gas signatures. The range in measured capillary pressure and predicted relative permeability curves for a given lithology and between lithologies highlights the need for future modeling to consider spatially distributed properties.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗