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NASA NTRS2003

Bubble Formation and Transport during Microgravity Materials Processing: Model Experiments on the International Space Station

Flow Visualization experiments on the controlled melting and solidification of succinonitrile were conducted in the glovebox facility of the International Space Station (ISS). The experimental samples were prepared on ground by filling glass tubes, 1 cm ID and approximately 30 cm in length, with pure succinonitrile (SCN) under 450 millibar of nitrogen. Porosity in the samples arose from natural shrinkage, and in some cases by direct insertion of nitrogen bubbles, during solidification of the liquid SCN. The samples were processed in the Pore Formation and Mobility Investigation (PFMI) apparatus that is placed in the glovebox facility (GBX) aboard the ISS. Experimental processing parameters of temperature gradient and translation speed, as well as camera settings, were remotely monitored and manipulated from the ground Telescience Center (TSC) at the Marshall Space Flight Center. During the experiments, the sample is first subjected to a unidirectional melt back, generally at 10 microns per second, with a constant temperature gradient ahead of the melting interface. The temperatures in the sample are monitored by six in situ thermocouples. Real time visualization of the controlled directional melt back shows bubbles of different sizes initiating at the melt interface and, upon dislodging from the melting solid, migrating at different speeds into the temperature field ahead of them, before coming to rest. The thermocapillary flow field set up in the melt, ahead of the interface, is dramatic in the context of the large bubbles, and plays a major role in dislodging the bubble. A preliminary analysis of the observed bubble formation and mobility during melt back and its implication to future microgravity experiments is presented and discussed.

Grugel, R. N.↗

NASA NTRS2003

Bubble Formation and Transport during Microgravity Materials Processing: Model Experiments on the Space Station

Grugel, R. N.↗

NASA NTRS2003

Porous cobalt spheres for high temperature gradient magnetically assisted fluidized beds

Porous metallic cobalt spheres have been prepared as high temperature capable media for employment in gradient magnetically assisted fluidization and filtration technologies. Cobalt impregnated alginate beads are first formed by extrusion of an aqueous suspension of Co3O4 into a Co(II) chloride solution. The organic polymer is thermally decomposed yielding cobalt oxide spheres, followed by reduction to the metallic state, and densification. Cobalt beads have been produced with porosities ranging between 10 and 50%, depending upon sintering conditions. The product media have been characterized by scanning electron microscopy (SEM), nitrogen adsorption porosimetry, and vibrating sample magnetometry. c2003 Elsevier Science Ltd. All rights reserved.

NASA Discipline Life Support Systems↗

DOE OSTI2025

Modeling Structured Electrodes and Graded Porosity for Improving Discharge Rate Capability in Ultra-Thick Graphite|LiNi 0.6 Mn 0.2 Co 0.2 O 2 Batteries

Long-range electric vehicles (EVs) require high-energy-density batteries that also meet the power demands of high current charge and discharge. Ultra-thick (>100 μm) Lithium-ion battery electrodes are critical to enable this need, but slow ion transport in conventional uniform electrodes (UEs) reduces battery capacity at increasing charge/discharge rates. We present a 3D computational analysis on the impact of structured electrode (SE) and graded electrode (GE) geometries on the discharge rate capability of ultra-thick graphite|LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC-622) battery cells based on the footprint of a commercial EV pouch cell. SE cathodes with either a “grid” or “line” geometry and GEs with two layers of porosity were modeled. Based on the results of 230 models, we found that the electrolyte volume fraction is a key parameter that impacts capacity improvements in UEs, GEs, and SEs at 2 C–6 C discharge rates. SEs have the greatest discharge rate capability, outperforming GEs and UEs due to reduced Lithium-ion concentration gradients across the electrode thickness, which mitigates electrolyte depletion at high rates. The best SE model has a “grid” geometry with gravimetric and volumetric energy density improvements of 0.9%–4% at C/2–2 C and 18%–24% at 4 C–6 C relative to UEs.

25 ENERGY STORAGE↗

NASA NTRS2001

Thermal Conductivity of Ceramic Thermal Barrier and Environmental Barrier Coating Materials

Thermal barrier and environmental barrier coatings (TBC's and EBC's) have been developed to protect metallic and Si-based ceramic components in gas turbine engines from high temperature attack. Zirconia-yttria based oxides and (Ba,Sr)Al2Si2O8(BSAS)/mullite based silicates have been used as the coating materials. In this study, thermal conductivity values of zirconia-yttria- and BSAS/mullite-based coating materials were determined at high temperatures using a steady-state laser heat flux technique. During the laser conductivity test, the specimen surface was heated by delivering uniformly distributed heat flux from a high power laser. One-dimensional steady-state heating was achieved by using thin disk specimen configuration (25.4 mm diam and 2 to 4 mm thickness) and the appropriate backside air-cooling. The temperature gradient across the specimen thickness was carefully measured by two surface and backside pyrometers. The thermal conductivity values were thus determined as a function of temperature based on the 1-D heat transfer equation. The radiation heat loss and laser absorption corrections of the materials were considered in the conductivity measurements. The effects of specimen porosity and sintering on measured conductivity values were also evaluated.

Zhu, Dong-Ming↗

NASA NTRS2018

Freeze Casting of LAGP for 3D Textured Solid-State Structural Electrolytes

In this study, all-solid-state structural lithium-ion batteries, a type of load bearing electrochemical energy storage that provides systems-level weight savings is being pursued for the realization of inherently safe next generation hybrid-electric and all-electric green aerospace propulsion systems. Currently investigated all-solid-state batteries do not meet the requirements for specific power and mechanical stability. To address these issues, freeze casting of lithium aluminum germanium phosphate (LAGP) electrolyte material has been explored for the creation of a textured 3D electrolyte scaffold with large interfacial surface area for high power discharge and hierarchical porosity for accommodation of active material volume changes during electrochemical cycling. We report the effects of freeze casting processing parameters on the microstructural development and mechanical performance of the scaffolds, characterized through scanning electron microscopy and ring-on-ring mechanical testing. Slurry composition and casting parameters such as solids loading, casting speed, tape angle, and temperature gradients have been modified to determine the impact on density, lamellar morphology, and final load-bearing performance.

ceramics↗

DOE OSTI2023

Bentonite buffer under high temperature: laboratory experiments and coupled process modeling

Abstract. Bentonite buffer in a geological repository will be simultaneously heated from decaying radioactive waste and hydrated from the surrounding host rock, triggering complex and coupled THMC (thermal–hydrological–mechanical–chemical) processes. Understanding the THMC behavior of bentonite-based engineered barrier system (EBS) is key to the evaluation and prediction of its long-term performance. Studies on the THMC process have been focused on conditions under 100 ∘C, as most design concepts impose a thermal limit of 100 ∘C in bentonite. Recently, studies under high-temperature conditions have been conducted to evaluate the possibility of raising the thermal limit and expanding the data/knowledge base to increase the confidence level. In this abstract, we present a series of bench-scale laboratory experiments at high temperatures (up to 200 ∘C) and the corresponding modeling work. Two sets of column tests were conducted, and each set consisted of two test columns: a control column undergoing only hydration (non-heated) and an experiment column experiencing both heating and hydration (heated). During the experiment, frequent X-ray computed tomography (CT) images were collected to provide a 3D visualization of the density distribution and present the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. The two sets of tests differ with respect to several experimental conditions, such as bentonite type, compacted density and water content, water chemistry, and hydration pressure, but the important difference is that the first set used bentonite powder with a dry density of 1.28 g cm−3, whereas the second set used granulated bentonite (mixture of pellets and powder) with a dry density of 1.45–1.5 g cm−3. In both sets of experiments, a comprehensive post-dismantling characterization of bentonite samples was carried out after the column tests had been running for 1.5 years. Comparing non-heated and heated columns, the temperature gradient led to lower degree of homogenization of bentonite after bentonite became fully saturated; comparing the first and second sets, granulated and powdered bentonite exhibited drastically different hydration behavior. A THM model with a 2D axisymmetric grid system was used to interpret the data from the first set of tests. The model considers the combined impact of saturation, fluid pressure, and porosity change due to swelling/compression on the spatiotemporal distribution of bulk density and movement of the thermocouple modules. Observations from the tests help us understand the early perturbation of bentonite buffer under high temperature, and data from these tests improve the calibration of key constitutive hydrological and mechanical models and, therefore, enhance the modeling capability with respect to calculating the long-term evolution of bentonite buffer.

Zheng, Liange↗

NASA NTRS1985

High-thermal-gradient Superalloy Crystal Growth

Single, (001)-oriented crystals of PWA 1480 were processed in alumina/silica shell molds in a laboratory high gradient furnace. The furnace employs a graphite resistance heated element, a radiation baffle, and a water cooled radiation trap below the baffle. All crystals were grown in vacuum (10 torr) and all heat transfer was radiative. The element is constructed with a variable cross section that is tapered just above the baffle to maximize heat input and therefore thermal gradient. A maximum alloy temperature of 1600 C was used. A thermal gradient of 130 deg C/cm was recorded at 1370 C just above the solidus of the PWA 1480 alloys. Crystal bars with 14.4 and 17.5 mm diameters were grown in alumina/silica shell molds. Each crystal was started from a 1.6 mm pencil seed at a rate of 76 mm/hr and slowly accelerated to a rate of 200 mm/hr under computer control. Volume percent porosity and average pore size were measured as functions of distance in representative bars. Low cycle fatigue behavior and stress rupture properties were determined.

Pearson, D. D.↗

DOE OSTI2024

Quantitative Insight to Fission Gas Pores Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Machine Learning

Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Integrating Experiments and Well Logs to Predict Caney Shale Static Mechanical Properties During Production with Supervised Machine Learning

Caney shale is one of the emerging oil reservoirs in Oklahoma. Understanding the impact of effective stress on its mechanical properties is critical for predicting hydraulic fracture geometry and overall hydrocarbon production. The objective of our study is to evaluate the impact of effective stress on the dynamic Young’s modulus using ultrasonic velocity measurements for Caney shale samples. A triaxial cell was utilized to measure ultrasonic (P-wave and S-wave) velocities for ten downhole Caney shale samples under various effective stresses to indirectly assess the impact of pore pressure change. The dynamic Young’s moduli estimated from these measurements were integrated with available conventional well logs (excluding sonic logs) and triaxial test results from Benge et al. (2021) to predict the static Young’s modulus using Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. The results showed that the estimated dynamic Young’s moduli from ultrasonic measurements were higher than the corresponding static Young’s modulus of cores from the same vertical well at similar depths. With increasing effective stress, the dynamic Young’s modulus increased for all samples. The estimated dynamic-to-static correction factor tended to be higher in zones of high neutron porosity (PHIN) and low density compared to other zones. Finally, SHapley Additive exPlanations (SHAP) for RF and XGBoost models identified depth, gamma ray (GR), and PHIN as key features for predicting the static Young’s modulus. This study enhances our understanding of the dynamic and static Young’s moduli for the Caney shale interval, as a function of effective stress and conventional well logs. The findings from this study can improve predictions of production throughout the well's lifespan by offering insights into the mechanical property degradation resulting from pore pressure depletion.

Kholy, Sherif M.↗

DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗

NASA NTRS1996

Advancement of Solidification Processing Technology Through Real Time X-Ray Transmission Microscopy: Sample Preparation

Two types of samples were prepared for the real time X-ray transmission microscopy (XTM) characterization. In the first series directional solidification experiments were carried out to evaluate the critical velocity of engulfment of zirconia particles in the Al and Al-Ni eutectic matrix under ground (l-g) conditions. The particle distribution in the samples was recorded on video before and after the samples were directionally solidified. In the second series samples of the above two type of composites were prepared for directional solidification runs to be carried out on the Advanced Gradient Heating Facility (AGHF) aboard the space shuttle during the LMS mission in June 1996. X-ray microscopy proved to be an invaluable tool for characterizing the particle distribution in the metal matrix samples. This kind of analysis helped in determining accurately the critical velocity of engulfment of ceramic particles by the melt interface in the opaque metal matrix composites. The quality of the cast samples with respect to porosity and instrumented thermocouple sheath breakage or shift could be easily viewed and thus helped in selecting samples for the space shuttle experiments. Summarizing the merits of this technique it can be stated that this technique enabled the use of cast metal matrix composite samples since the particle location was known prior to the experiment.

Stefanescu, D. M.↗

DOE OSTI2025

In-situ neutron radiography of pore-water movement in portland cement mortar under subzero temperature exposure: Insights from D 2 O-H 2 O systems

Neutron radiography was employed in this study to monitor real-time pore water movement in portland cement mortar exposed to subzero temperatures, providing novel insights into freeze-thaw (F-T) damage mechanisms. Deuterated water (D 2 O) was used to hydrate the cement in the mortar specimens, and the pore structure was saturated with water (H 2 O), allowing for a contrast that enabled detailed tracking of water movement predominantly in the pore structure. Furthermore, the mortar sample was placed on a chiller plate set to subzero temperatures, thereby providing a one-dimensional temperature gradient vertically in the sample. Neutron radiography revealed that unfrozen water migrated upward as the freezing front advanced, which can create hydraulic pressure within the mortar microstructure and may contribute to the initiation and propagation of microcracks during F-T cycles. Additional characterization of the concrete made with D 2 O showed a marked contrast to the samples made with H 2 O, including delayed hydration, diminished compressive strength, and greater porosity. Therefore, while mortar made with D 2 O allowed for greater neutron contrast to the pores filled with H 2 O, further study is required to produce mortars with similar properties to those made with H 2 O. In addition, further refinement is needed to the radiography experiment to allow for greater control of temperatures and potential quantification of the water concentration.

Air-void system↗

NASA NTRS2006

Improving Thermomechanical Properties of SiC/SiC Composites

Today, a major thrust toward improving the thermomechanical properties of engine components lies in the development of fiber-reinforced silicon carbide matrix composite materials, including SiC-fiber/SiC-matrix composites. These materials are lighter in weight and capable of withstanding higher temperatures, relative to state-of-the-art metallic alloys and oxide-matrix composites for which maximum use temperatures are in the vicinity of 1,100 C. In addition, the toughness or damage tolerance of the SiC-matrix composites is significantly greater than that of unreinforced silicon-based monolithic ceramics. For successful application in advanced engine systems, the SiC-matrix composites should be able to withstand component service stresses and temperatures for the desired component lifetimes. Inasmuch as the high-temperature structural lives of ceramic materials are typically limited by creep-induced growth of flaws, a key property required of such composite materials is high resistance to creep under conditions of use. Also, the thermal conductivity of the materials should be as high as possible so as to minimize component thermal gradients and thermal stresses. A state-of-the-art SiC-matrix composite is typically fabricated in a three-step process: (1) fabrication of a component-shaped architectural preform reinforced by thermally stable high-performance fibers, (2) chemical-vapor infiltration (CVI) of a fiber-coating material such as boron nitride (BN) into the preform, and (3) infiltration of an SiC-based matrix into the remaining porosity in the preform. Generally, the matrices of the highest-performing composites are fabricated by initial use of a CVI SiC matrix component that is typically more thermally stable and denser than matrix components formed by processes other than CVI. As such, the initial SiC matrix component made by CVI provides better environmental protection to the coated fibers embedded within it. Also, the denser CVI SiC imparts to the composite better resistance to propagation of cracks, enhanced thermal conductivity, and higher creep resistance.

DiCarlo, James A.↗

DOE OSTI2022

Investigation of Scanning Droplet Cell Technology for Electrochemical Deposition of Custom Three-Dimensional Alloys

The goal of this project was to assess the feasibility of a new method of electroplating, which we have termed “electroprinting”, for fabricating millimeter to centimeter metal parts with full density, and eventually, with bespoke 3D internal density patterns. Alloys, gradients and varying density is beyond the scope of this work, and efforts were focused on developing the technology to be capable of printing solid parts, beyond the lines and columns previously reported in the literature. Enabling this technology would expand the design space possible for the WPD program, allowing for smooth and complex density gradients in parts rather than discrete density steps between multilayers. We successfully designed and built an electroprinting apparatus, capable of printing copper in customizable 1-D patterns, which can be printed in stacked layers to form 3D parts. We successfully characterized the flat printed patterns, however, have encountered difficulty in characterizing multilayer prints. We have partially addressed the feasibility question, by developing the method for electroprinting 3D parts, however, some questions about the internal porosity and density of these parts still remain.

36 MATERIALS SCIENCE↗

NASA NTRS2002

Optimal Design of Functionally Graded Metallic Foam Insulations

The focus of our work has been on developing an insight into the physics that govern the optimum design of thermal insulation for use in thermal protection systems of launch vehicle. Of particular interest was to obtain optimality criteria for designing foam insulations that have density (or porosity) distributions through the thickness for optimum thermal performance. We investigate the optimum design of functionally graded thermal insulation for steady state heat transfer through the foam. We showed that the heat transfer in the foam has competing modes, of radiation and conduction. The problem assumed a fixed inside temperature of 400 K and varied the aerodynamic surface heating on the outside surface from 0.2 to 1.0 MW/sq m. The thermal insulation develops a high temperature gradient through the thickness. Investigation of the model developed for heat conduction in foams showed that at high temperatures (as on outside wall) intracellular radiation dominates the heat transfer in the foam. Minimizing radiation requires reducing the pore size, which increases the density of the foam. At low temperatures (as on the inside wall), intracellular conduction (of the metal and air) dominates the heat transfer. Minimizing conduction requires increasing the pore size. This indicated that for every temperature there was an optimum value of density that minimized the heat transfer coefficient. Two optimization studies were performed. One was to minimize the heat transmitted though a fixed thickness insulation by varying density profiles. The second was to obtain the minimum mass insulation for specified thickness. Analytical optimality criteria were derived for the cases considered. The optimality condition for minimum heat transfer required that at each temperature we find the density that minimizes the heat transfer coefficient. Once a relationship between the optimum heat transfer coefficient and the temperature was found, the design problem reduced to the solution of a simple nonlinear differential equation. Preliminary results of this work were presented at the American Society of Composites meeting, and the final version was submitted for publication in the AIAA Journal. In addition to minimizing the transmitted heat, we investigated the optimum design for minimum weight given an acceptable level of heat transmission through the insulation. The optimality criterion developed was different from that obtained for minimizing beat transfer coefficient. For minimum mass design, we had to find for a given temperature the optimum density, which minimized the logarithmic derivative of the insulation thermal conductivity with respect to its density. The logarithmic derivative is defined as the ratio of relative change in the dependent response (thermal conductivity) to the relative change in the independent variable (density). The results have been documented as a conference paper that will be presented at the upcoming AIAA.

Haftka, Raphael T.↗

DOE OSTI2024

Sequential formulation of all‐way coupled finite strain thermoporomechanics for largely deformable gas hydrate deposits

We develop a numerically stable sequential formulation of thermoporomechanics for largely deformable gas hydrate deposits, extended from the fixed stress split of infinitesimal transformation. Constitutive equations are based on the total Lagrangian approach for both flow and geomechanics, including dynamic full tensor permeability and thermal conductivity updated from the deformation gradient. For space discretization, we take the cell-centered finite volume and node-based finite element method for flow and geomechanics, respectively. Then, we propose a sequential implicit method for all-way coupled thermoporomechanics, where the nonisothermal multiphase flow problem of gas hydrates is solved implicitly first and then the geomechanics problem is solved implicitly at the next step. During solution of the flow problem, we fix the rate of first Pioal total stress for numerical stability as well as apply porosity correction and entropy correction to account for geomechanical effects. We test numerical examples where flow and geomechanics parameters are based on deep oceanic gas hydrate deposits. When applying depressurization, even though the results between the infinitesimal transformation and finite strain geomechanics are similar in the early stages due to small deformation, we find differences between them in the late times as deformation becomes large. Accordingly, permeability and thermal conductivity tensors become nonisotropic full tensors although they are initially isotropic. Furthermore, we identify numerical stability of the developed sequential method from the test cases that exhibit the highly complex coupled gas hydrate systems with large deformation. Thus, the proposed sequential formulation can be applied in largely deformable gas hydrate systems.

42 ENGINEERING↗

DOE OSTI2024

Segmentation and Classification of Fission as Pores in Reactor Irradiated Annular U–10Zr Metallic Fuel Using Machine Learning Models

Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.

36 MATERIALS SCIENCE↗