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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
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 OSTI2020

Joint Focal Mechanism Inversion Using Downhole and Surface Monitoring at the Decatur, Illinois, CO2 Injection Site

ABSTRACT The three-year CO2 injection period at the Illinois Basin - Decatur Project site (Decatur, Illinois, United States) produced a number of microseismic events distributed in very distinct spatiotemporal clusters with different orientations. Further characterization of the microseismicity encompasses the determination of the event source mechanisms. Initially, the microseismic monitoring network consisted solely of borehole sensors, but has been extended with surface sensors, thereby significantly improving the data coverage over the focal sphere. This article focuses on 23 events from the northernmost microseismic cluster (about 2 km from the injection point) and takes advantage of both, surface and downhole, recordings. The resulting strike-slip east–west-oriented focal planes are all consistent with the east–west orientation of the cluster in map view. The injection-related increase of pore pressure is far below the formation fracture pressure; however, small stress-field changes associated with the pore-pressure increase may reach as far as to the investigated cluster location. Monte Carlo modeling of the slip reactivation potential within this cluster showed that the observed maximum stress-field orientation of N068° is the optimum orientation for fault reactivation of the east–west-oriented cluster. Our results suggest that the east–west orientation of the investigated cluster is the main reason for its activation, even though the cluster is about 2 km away from the low-pressure injection point.

Geochemistry & Geophysics↗
DOE OSTI2021

Diversity-oriented synthesis of polymer membranes with ion solvation cages

Microporous polymers feature shape-persistent free volume elements (FVEs), which are permeated by small molecules and ions when used as membranes for chemical separations, water purification, fuel cells, and batteries. It remains a significant challenge to identify FVEs with analyte specificity, due to difficulties in generating microporous polymer libraries with sufficient diversity for screening their properties. Here, we describe a diversity-oriented synthetic (DOS) strategy for microporous polymer membranes from which we identified those whose FVEs serve as solid solvation cages for lithium ions (Li + ). Furthermore, key elements of our strategy included diversification of bis(catechol)-type monomers via multi-component Mannich reactions to introduce Li+-coordinating functionality within individual FVEs, topology-enforcing polymerizations for generating macromolecular skeletal diversity for networking FVEs into different pore architectures, and several classes of on-polymer reactions for diversifying pore geometries and dielectric properties. Lead candidate polymer membranes featuring explicit ion solvation cages exhibited both higher ionic conductivity and higher cation transference number than control membranes where FVEs were aspecific, which indicates conventional bounds for membrane permeability and selectivity for ion transport can be overcome.4 These advantages are tied to enhanced Li + partitioning from the electrolyte when the cages are present, higher diffusion barriers for anions within the pores, and network-enforced restrictions on the number of solvent molecules bound to Li+ by comparison to the bulk electrolyte, which reduces the effective mass of the working ion. Such membranes show promise as anode-stabilizing interlayers in high-voltage lithium-metal batteries for electric mobility.

42 ENGINEERING↗
DOE OSTI2026

Mechanics of pore array collapse and interaction in shock-compressed polymethyl methacrylate (PMMA)

Recent studies on dynamic pore collapse have revealed significant development of shear localization, which can lead to material failure in porous structures and hot spot generation in energetic materials. These findings have dramatically improved the understanding of failure mechanisms during pore collapse but also prompt further investigation of realistic porous materials. In particular, porous media consist of many pores and porous networks. Even in low-porosity materials, pores can form in close proximity during the manufacturing process, leading to the critical question of pore–pore interaction during collapse under dynamic loading conditions. This study investigates, via plate impact experiments coupled with high-speed internal digital image correlation and shadowgraphy techniques, the collapse of two pores in shock-compressed PMMA at stresses between 0.4 and 1 GPa. The results of these experiments provide new insights into shear localization in pore collapse, in addition to distinct interactions between pores. Shadowgraphy measurements reveal novel, direct visualization of shear band development and crack evolution from pore surfaces. Spacing between adiabatic shear bands is measured over a range of impact stresses and is predicted accurately by the Grady–Kipp model. Pore interactions are found to effect a transition in the impact stress threshold at which different failure mechanisms initiate and are also found to possibly influence preferential sites for shear cracking. Throughout the study, numerical and theoretical models are leveraged to understand shear localization behavior. The role of baroclinicity and wave interactions between the pores is used to elucidate interaction mechanisms between pores.

Lawlor, Barry P. [California Institute of Technolo↗
DOE OSTI2022

Catalytic Conversion of Model Compounds of Plastic Pyrolysis Oil over ZSM-5

Mechanistic investigation of the catalytic conversion of model compounds for plastic pyrolysis oil (1-octene, octadiene, octane, and toluene) over ZSM-5 in a fixed-bed reactor was studied. 1-Octene breaks down into smaller olefins, which undergo further cracking, oligomerization, cyclization, and hydrogen transfer to eventually produce benzene, toluene, xylene (BTX), coke, and hydrogen. The effect of contact time on 1-octene conversion was further investigated and compared with thermodynamics analyses to elucidate the reaction network. Under the reaction conditions (500 oC, 1 atm), octadiene undergoes thermal coking, significantly contributing to reactor fouling. The products from octane cracking are similar to the products from 1-octene conversion whereas toluene undergoes disproportionation, dealkylation and coking. The analysis of spent catalyst showed long-chain hydrocarbons created by oligomerization reactions filled the pores and covered the surface of the catalyst. As a result, when mesoporous ZSM-5 is used instead of conventional, product selectivity is maintained for 70 hours in time-on-stream experiments.

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

The Influence of Aqueous Dispersivity on Transport of Radiological Material at LANL MDA G - 20450

Aqueous dispersivity is a phenomenon by which a solute moving in groundwater mixes with water that does not contain the solute and becomes diluted. Solutes take paths of varying lengths through pore networks or experience slower velocities due to interactions with pore walls. Aqueous dispersivity leads to breakthrough curves with earlier arrival than expected in purely advective systems and persistent 'tails,' or late-arriving, dilute concentrations of the solute. Dispersivity can be longitudinal (in the direction of flow) or transverse (orthogonal to flow). LANL has disposed of radioactive material at Material Disposal Area (MDA) G since the late 1950's. Most of the disposed materials at MDA G are low-level waste (LLW), but other types of radioactive wastes are disposed or stored at the site. For example, there is a collection of remote-handled transuranic waste (RH-TRU) waste in 33 shafts, which were intended to be stored retrievably. Aqueous-phase radionuclides included in models of contaminant transport at MDA G are subject to dispersive processes in porous media of the vadose zone and saturated zone. To model dispersivity at MDA G probabilistically, we created site-specific distributions of aqueous dispersivity. First, we performed a comprehensive review of peer-reviewed publications, reports from other performance assessments of MDA G, and documentation from sites that were considered similar based on expert opinion, such as Yucca Mountain. Data were included from a wide range of sources, so that distributions developed based on the data reflect the current state of knowledge in properties of the subsurface environment. Data were grouped based on value type (e.g., determined via experiment or used in a different model), rock type, zone type (saturated or unsaturated), and site, and were weighted in the distributions according to their relevance to the site and zone type. We combined Monte Carlo simulation and bootstrapping techniques to produce approximate distributions of mean aqueous dispersivity. Distributions of dispersivity for saturated and unsaturated zone transport are differentiated by data weight and the length scales over which transport occurs. We find that underestimating or disregarding transverse dispersivity may lead to overstating simulated well capture in the saturated zone at MDA G. Neglecting transverse dispersivity is a common assumption due in part to use of one-dimensional transport models that do not consider solute spreading orthogonal to flow. Also, we find that variability in longitudinal dispersivity is associated with substantial travel time alterations in the MDA G unsaturated zone and in simulated contaminant breakthrough times at a downstream observation plane in the MDA G saturated zone. Finally, at large scales (>1000 m), a linear trend between the dispersivity coefficient and length over which transport occurs may overestimate the dispersivity coefficient. Future modeling work with application to solute spreading could include interacting advective and diffusive zones in the highly fractured basalt below MDA G. (authors)

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

Characterization of Acoustic Emissions From Analogue Rocks Using Sparse Regression‐DMDc

Abstract Moisture loss in rock is known to generate acoustic emissions (AE). Phenomena that result in AE during drying are related to the movement of fluids through the pores and induced‐cracks that arise from differential mineral shrinkage, especially in clay‐bearing rock. AE from the movement of fluids occurs from the reconfiguration of fluid interfaces during drying, while AE from mineral shrinkage involves the debonding within or between minerals. Here, analogue rock samples were used to examine the differences in the AE signatures when one or both AE source‐types are present. An unsupervised sparse regression model, Dynamic Mode Decomposition with control, that extends Dynamic Mode Decomposition is used to characterize the AE signals recorded during the drying of porous analogue rock samples fabricated with ordinary Portland cement, with and without clay. This method can effectively and accurately reconstruct acoustic signals emitted from samples that only experience moisture loss without cracking. However, the method struggles to reconstruct signals from samples with intricate crack networks that formed during drying because AE generating mechanisms can emit contemporaneously, and the resulting waves propagate through drying‐induced cracks that can lead to multiple internal reflections. Thus, the differential reconstruction accuracy of time series generated by different underlying physical processes provides a robust filter for reducing large data catalogs. In general, both dynamics and sparse initiating events are learned directly from data and this method exposes a data hierarchy based on the complexity of the intrinsic dynamics.

58 GEOSCIENCES↗
DOE OSTI2026

SAM Code Enhancements for Fission Product Tracking of Noble Gases and Metals in MSRs

This report documents fiscal year 2026 enhancements to the System Analysis Module (SAM) for modeling fission product transport in liquid-fueled molten salt reactors (MSRs). The work advances three principal areas: noble gas transport, noble metal deposition, and user interface improvements. The noble gas transport capability integrates drift-flux gas transport, Henry’s law two-film interphase mass transfer with pressure-based nucleation suppression, Knudsen-regime pore diffusion into porous graphite with a conjugate salt-graphite interface constraint, built-in material properties, five Sherwood-number mass transfer correlations including three derived from high-fidelity NekRS simulations, and xenon-135 reactivity feedback through SAM’s point-kinetics model. This work also presents a comprehensive verification test suite, including new analytically verified cases for pressure-dependent onset of interphase gas transfer in a stagnant vertical pipe, a postulated FLiBe-graphite Xe extraction permeator, a gravity riser with a fission-product source, and a descending pipe with gas redissolution driven by hydrostatic pressure. A machine learning framework for bubble rise velocity prediction in molten salt systems is developed and benchmarked on molten-salt and diverse aqueous bubble datasets. The best-performing fine-tuned transfer-learning networks achieve an 82% reduction in RMSE relative to the Clift correlation, and is implemented directly in SAM. The noble metal transport capability is developed, including a liquid-wall deposition model and a gas-surface flotation mechanism that transfers insoluble particles entrained by sparging gas to wetted structures. Verification tests and demonstration cases cover the surface deposition, flotation efflux, and flotation shedding. Finally, a new [SpeciesTransport] input structure replaces positional global vectors with selfcontained, order-independent, named species blocks, simplifies the specification of multiphase species and decay chains, and remains fully compatible with existing SAM input files. Together, these developments improve the physical fidelity, verification basis, and usability of SAM for system-level analyses of fissionproduct behavior in MSRs.

Mui, Travis (ORCID:0000000303736470)↗
DOE OSTI2023

Defect engineering of porous aromatic frameworks via end capping improves dioxane removal from water

Amorphous porous organic polymers show promise for energy-efficient adsorptive separations, but it is difficult to understand or improve their performance through intentional structural modification. Herein, we report the synthesis of porous aromatic frameworks (PAFs) with pore structures tailored by the incorporation of a monofunctionalized end-capping monomer that disrupts framework topology. Combining experimental characterization with molecular simulations, we show that this defect engineering strategy yields less densely crosslinked networks, which leads to pore collapse and the presence of unique adsorption sites. These defect-engineered PAFs exhibit enhanced removal of 1,4-dioxane, an important environmental pollutant, from water. Increasing the concentration of end-capping monomers produces PAFs with narrower pore size distributions and improved 1,4-dioxane uptake. These results illustrate that defect engineering can effectively modulate polymer connectivity and porosity for applications in selective adsorptive separations. In conclusion, this technique avoids post-synthetic treatments and presents another approach to tailor amorphous polymeric adsorbents.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the role of Cr migration in Ni-Cr exposed to molten FLiNaK via multiscale characterization

Structural materials used in nuclear reactor environments are subjected to coupled extremes such as high temperature, irradiation, and corrosion which act in concert to degrade their functional performance. Connecting alloy microstructure such as grain boundaries, and accumulating point defects with corrosion attack and pore morphology is critical to understanding underlying mechanisms. We uncover the compositional variations and morphology at multiple length scales in corrosion-damaged Ni-Cr alloy after exposure to oxidants in molten fluoride salts. A complex network of dense corrosion pores is detected by surface-level SEM observations. The corrosion pores take on a 1-dimensional morphology and are enriched with Ni and depleted of Cr 1–5 µm from the pore surface. STEM-EDS and 4D-STEM strain maps acquired simultaneously highlight the local variations in composition and structure of a ≤ 200 nm Cr-rich layer identified from a cross-section taken at the bottom of an isolated corrosion pore between the Ni-Cr alloy matrix and the embedded salt. However, the absence of an observed interface between the Ni-Cr alloy matrix and the FCC-structured Cr-rich layer suggests that Cr plating from the salt did not transpire. These findings support a proposed Cr lattice diffusion mechanism rather than Cr-precipitation from the salt to accommodate temperature transient conditions during sample cooling. Through the development of a 1D phase field model, these results are rationalized by formation energies for the Ni- and Cr-oxidation into the molten salt. This study reveals the locally altered microstructure caused by high temperature corrosion in non-steady-state molten salt nuclear reactor environments.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimode Metastructures: Novel Hybrid 3D Lattice Topologies

With the rapid proliferation of additive manufacturing and 3D printing technologies, architected cellular solids including truss-like 3D lattice topologies offer the opportunity to program the effective material response through topological design at the mesoscale. The present report summarizes several of the key findings from a 3-year Laboratory Directed Research and Development Program. The program set out to explore novel lattice topologies that can be designed to control, redirect, or dissipate energy from one or multiple insult environments relevant to Sandia missions, including crush, shock/impact, vibration, thermal, etc. In the first 4 sections, we document four novel lattice topologies stemming from this study: coulombic lattices, multi-morphology lattices, interpenetrating lattices, and pore-modified gyroid cellular solids, each with unique properties that had not been achieved by existing cellular/lattice metamaterials. The fifth section explores how unintentional lattice imperfections stemming from the manufacturing process, primarily sur face roughness in the case of laser powder bed fusion, serve to cause stochastic response but that in some cases such as elastic response the stochastic behavior is homogenized through the adoption of lattices. In the sixth section we explore a novel neural network screening process that allows such stocastic variability to be predicted. In the last three sections, we explore considerations of computational design of lattices. Specifically, in section 7 using a novel generative optimization scheme to design novel pareto-optimal lattices for multi-objective environments. In section 8, we use computational design to optimize a metallic lattice structure to absorb impact energy for a 1000 ft/s impact. And in section 9, we develop a modified micromorphic continuum model to solve wave propagation problems in lattices efficiently.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FAST Irradiations, Postirradiation Examinations, and Modeling of U-Mo for Light Water Reactor Applications

Many next generation light water reactor (LWR) concepts, such as mobile small modular reactors, are seeking to use smaller core dimensions than conventional reactor types. Smaller reactor cores require an increase in fissile material to maintain reactivity. For non-proliferation purposes, enrichment increases are limited to less than 20% (high assay low enriched uranium, [HALEU]) and so higher uranium density fuels than UO 2 must be considered. To this end, uranium-molybdenum alloys were tested using the Fission Accelerated Steady-state Test (FAST) approach. The experiment test matrix is focused on identifying the temperature transition between low swelling and high fission gas retention to break away swelling and low fission gas retention. This paper documents the results of irradiation tests and post-irradiation examinations (PIE) including neutron radiography, rodlet profilometry, fission gas collection analysis, and optical metallography. The results of these tests showed that unconstrained U-Mo fuels (solid, Na-bonded rodlets) have a swelling threshold between 400-450°C with minimal fission gas release below this point. Higher temperature solid fuel showed microstructural zoning with small pore networks while lower temperature solid fuels have a uniform microstructure with large pore networks. U-Annular Mo fuels where swelling had some self-constraint imposed upon it, were shown to have much reduced swelling compared to their solid counterparts as well as very low fission gas release for irradiation temperatures up to 500°C. These initial results show that the use of U-Mo in constrained fuel geometries could be used as a high uranium density HALEU fuel for LWRs.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Experimental and Computational Analysis of NEAMS Pebble Bed Reactors

The Advanced High-Temperature Pebble Bed Reactor concept leverages a particle-based fuel format consisting of discrete spherical graphite pebbles arrayed in a packed bed architecture. Thermal regulation achieved via flow of gas (e.g., helium) or liquid (e.g., molten salt) coolants through the void spaces between pebbles in the bed (characteristic pebble diameters: ~ 6 cm, gas cooled; ~ 3 cm, liquid cooled). This design is of interest owing to inherent advantages including passive safety features and highly efficient heat transfer characteristics that enable high power densities to be achieved. Fully leveraging these advantages requires a fundamental understanding of the complex coolant flow structure within the pebble-shaped fuel bed. Unfortunately, complexities associated with non-invasively probing the micro-scale flow phenomena within the complex network of void spaces between pebbles have hindered efforts to characterize the underlying transport phenomena. Without this fundamental foundation, it becomes challenging to rationally construct macroscopic transport models based upon volume averages of micro-scale parameters that accurately capture the observed flow behavior. Consequently, this lack of fundamental knowledge has limited previous work to coarse comparisons between theory and extremely limited experimental data. Moreover, these comparisons have been made only in terms of integral or macroscopic results. Therefore, multipoint measurements with a high level of spatial and temporal resolution are critically needed to fully map the complex flow patterns and to provide data at high spatial density to permit accurate volume averaging in the pebble bed. For this project, we performed a coordinated experimental and computational effort to quantitatively map the full-field velocity and temperature fields in the interstitial spaces within the pebble bed. The major accomplishments of this project included the important knowledge acquired for the flow mixing and heat transfer in the interstitial pore scales, the effects of flow mixing in the near-wall and far-wall regions, and the by-pass flows of pebble bed systems. The produced high-resolution experimental data (not broadly available in existing literature) were then used for the numerical studies enabling the refinement and validation of standard simulation tools (Nek5000).

42 ENGINEERING↗
DOE OSTI2022

Analysis of Defects in Metal Additive Manufacturing with Augmented Data Generation

Laser powder bed fusion (LPBF) is a method of additive manufacturing (AM) that selectively melts and fuses together microscopic metallic powder. LPBF offers the benefit of producing custom structures out of high strength metals that can be difficult to fabricate with conventional methods. The challenge of LPBF is that 3D printed structures often have internal pores due to process flaws. Pulsed thermal tomography (PTT) is a method for reconstructing the depth profile of materials, allowing the visualization internal voids in solids. In prior work, we developed a convolutional neural network (CNN) which, having been trained on simulated 2D PTT images of subsurface elliptical defects, was able to classify the semi-major radii, semi-minor radii, and angular orientation of the best-fit ellipses in previously unseen PTT images. The unseen PTT images contained subsurface irregular defect shapes imported from scanning electron microscopy (SEM) images of metallic LPBF-printed specimens. Training the CNN on irregular defect shapes instead of on elliptical shapes would make the resulting classifications more descriptive of actual defect shapes. However, this requires a much higher volume of SEM images of material defects, which are difficult to obtain because of random occurrence of defects in LPBF. To address this challenge, we developed a generative adversarial network (GAN) to augment the existing dataset of SEM defect images. The GAN model is demonstrated to create novel yet realistic defect shapes that can be used as input for simulated PTT images to train CNN.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantifying Negative Effects of Carbon-Binder Networks from Electrochemical Performance of Porous Li-Ion Electrodes

Porous Li-ion electrodes contain active particles, ion transporting electrolyte, and carbon-binder networks. While macrohomogeneous models are often used to predict electrode behavior, accurate predictions remain challenging, owing to the incomplete understanding of the critical role of carbon-binder networks and how they affect the electrochemical response. The present study systematically characterizes these effects in terms of effective properties by utilizing macrohomogeneous models to analyze the measured responses for electrodes with different carbon-binder content, electrode thickness, and porosity but with identical materials. We find that the impact of the carbon-binder network is more severe than previously thought. Even for low carbon-binder content (5 %wt. dry electrode), the presence of the network decreases the reaction area and increases the ion transport resistance, negatively impacting electrode performance. These effects scale with not just porosity or active material volume but also with carbon-binder content. The findings underscore the importance of connecting all effective properties to electrode specifications in a full factorial sense to transform the electrode design paradigm.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Application of unsupervised deep learning to image segmentation and in-situ contact angle measurements in a CO 2 -water-rock system

Rock surface wettability is a critical property that regulates multiphase flows in porous media, which can be quantified using the surface contact angle (CA). X-ray micro-computed tomography (μCT) provides an effective approach to in-situ measurements of surface CAs. However, the CA measurement accuracy depends significantly on the quality of CT image segmentation, which is the clustering of CT pixels into separate phases. Inspired by this, we developed a deep learning (DL)-based CA measurement workflow. Motivated by the recent tremendous progress in unsupervised learning techniques and aiming to avoid expensive manual data annotations, an unsupervised DL pipeline for CT image segmentation was proposed and implemented, which includes unsupervised model training and post-processing. The unsupervised model training was driven by a novel loss function constrained with feature similarity and spatial continuity and implemented by iterative forward and backward paths; the former clustered the pixel-wise feature vectors extracted by convolution neural networks, whereas the latter updated the parameters using gradient descent. An over-segmentation strategy was adopted for model training. The post-processing steps based on agglomerative hierarchical clustering (AHC) were implemented to further merge the over-segmented model output to the desired cluster number, which is intended to improve the efficiency of image segmentation. The developed unsupervised DL pipeline was compared with other commonly-used image segmentation methods using pixel-wise and physics-based evaluation metrics on a synthetic raw-image dataset, which had a known ground truth. The unsupervised DL pipeline showed the best performance. Next, the segmented images were input to an automatic CA measurement tool, and the results were validated by comparisons with manual measurements. The CA values from the manual and automatic measurements showed similar distributions and statistical properties. The automatic measurement demonstrated a wider spectrum because of the much larger number of measurement data points. The primary novelty of the unsupervised DL pipeline developed in this study lies in the novel loss function and the over-segmentation strategy associated with AHC post-processing. Finally, the workflow has been proven an efficient tool for pore-scale wettability characterization, which has a wide range of applications in fundamental studies of multiphase flows in natural porous media, which have critical implications to geological carbon sequestration, hydrocarbon energy recovery, and contaminant transport in groundwater.

42 ENGINEERING↗
DOE OSTI2022

Deep-learning-based workflow for boundary and small target segmentation in digital rock images using UNet++ and IK-EBM

We report three-dimensional (3D) X-ray micro-computed tomography (μCT) has been widely used in petroleum engineering because it can provide detailed pore structural information for a reservoir rock, which can be imported into a pore-scale numerical model to simulate the transport and distribution of multiple fluids in the pore space. The partial volume blurring (PVB) problem is a major challenge in segmenting raw μCT images of rock samples, which impacts boundaries and small targets near the resolution limit. We developed a deep-learning (DL)-based workflow for accurate and fast partial volume segmentation. The DL model's performance depends primarily on the training data quality and model architecture. This study employed the entropy-based-masking indicator kriging (IK-EBM) to segment 3D Berea sandstone images as training datasets. The comparison between IK-EBM and manual segmentation using a 3D synthetic sphere pack, which had a known ground truth, showed that IK-EBM had higher accuracy on partial volume segmentation. We then trained and tested the UNet++ model, a state-of-the-art supervised encoder-decoder model, for binary (i.e., void and solid) and four-class segmentation. We compared the UNet++ with the commonly used U-Net and wide U-Net models and showed that the UNet++ had the best performance in terms of pixel-wise and physics-based evaluation metrics. Specifically, boundary-scaled accuracy demonstrated that the UNet++ architecture outperformed the regular U-Net architecture in the segmentation of pixels near boundaries and small targets, which were subjected to the PVB effect. Feature map visualization illustrated that the UNet++ bridged the semantic gaps between the feature maps extracted at different depths of the network, thereby enabling faster convergence and more accurate extraction of fine-scale features. The developed workflow significantly enhances the performance of supervised encoder-decoder models in partial volume segmentation, which has extensive applications in fundamental studies of subsurface energy, water, and environmental systems.

02 PETROLEUM↗