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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Synthesis of precipitation-strengthened Al-Sc, Al-Zr and Al-Sc-Zr alloys via selective laser melting of elemental powder blends

Selective laser melting is used to create Al-1.5Sc, Al-1.5Zr and Al-0.75Sc-0.75Zr (at.%) alloys from blends of elemental Al, Sc, and Zr powders. This study investigates elemental alloying elements (Sc and Zr) which are high-melting and highly reactive, unlike previous work which focused on more concentrated elemental additions of lower-melting, lower-reactivity Cu and Si to aluminum. High-speed in situ synchrotron x-ray imaging and diffraction show that the 20–30 μm Al, Sc, and Zr powders fully melt and sufficiently mix in the molten state to create, on solidification, a homogeneous distribution of primary, micron-size L12 precipitates (Al 3 Sc, Al 3 Zr, and Al 3 (Sc,Zr), respectively) and nucleate micron-size Al matrix grains, as confirmed by SEM imaging of cross-sections. Here, a second laser pass, simulating a realistic additive-manufacturing build condition, fully remelts the initial volume which shows, after solidification, the same Al 3 (Sc,Zr) L1 2 primary micro-precipitates and very fine Al grains. After aging at 300–400°C, the alloys show large increases in hardness, consistent with an exceptionally high number density (1.4 × 10 24 m –3 ) and volume fraction (2.5%) of secondary Al 3 (Sc,Zr) nano-precipitates with a Sc-rich core and Zr-rich shell, as measured via atom-probe tomography.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Harvesting 88 Zr from heavy-ion beam irradiated tungsten at the National Superconducting Cyclotron Laboratory

Tungsten is a commonly used material at many heavy-ion beam facilities, and it often becomes activated due to interactions with a beam. Many of the activation products are useful in basic and applied sciences if they can be recovered efficiently. In order to develop the radiochemistry for harvesting group (IV) elements from irradiated tungsten, a heavy-ion beam containing 88 Zr was embedded into a stack of tungsten foils at the National Superconducting Cyclotron Laboratory and a separation methodology was devised to recover the 88 Zr. The foils were dissolved in 30% hydrogen peroxide, and the 88 Zr was chemically purified from the tungsten matrix and from other co-implanted radionuclides (such as 85 Sr and 88 Y) using strong cation-exchange (AG MP-50) chromatographic resin in sulfuric acid media. The procedure provided 88 Zr in approximately 60 mL 0.5 M sulfuric acid with no detectable radio-impurities. The overall recovery yield for 88 Zr was (92.3 ± 1.2)%. In conclusion, this proof-of-concept experiment has facilitated the development of methodologies to harvest from tungsten and tungsten-alloy parts that are regularly irradiated at heavy-ion beam facilities.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

A High-Throughput Solver for Marginalized Graph Kernels on GPU

Here, we present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs. To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory. This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse. Warps across a thread block can further share tiles via the shared memory to increase data reuse. We exploit the sparsity of the graphs hierarchically by storing only non-empty tiles using a coordinate format and nonzero elements within each tile using bitmaps. We propose a new partition-based reordering algorithm for aggregating nonzero elements of the graphs into fewer but denser tiles to further exploit sparsity. We carry out extensive theoretical analyses on the graph tensor product primitives for tiles of various density and evaluate their performance on synthetic and real-world datasets. Our solver delivers three to four orders of magnitude speedup over existing CPU-based solvers such as GraKeL and GraphKernels. The capability of the solver enables kernel-based learning tasks at unprecedented scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Coupling buried etalon layers to an engraved metasurface for durable and large-aperture meta-optics

Many optical applications that could potentially benefit from the design flexibility provided by the metasurface approach are being prohibited by the limited scalability of the fabrication and the robustness of the end-result structures when using a resonant meta-elements-based approach. In this study, an alternative demonstrated approach with superior scalability and robustness is substrate-engraved metasurfaces, based on medium mixing homogenization, yet it suffers from very limited optical response. Here we propose advancing this approach by coupling the metasurface with buried etalon layers, leading to enhancement in the optical response. A transfer matrix analysis is used to study the parameter space, predicting that the patterned reflectance values range of a beam shaper could be raised from only 4% to 30% when the metasurface is engraved in silica, and even up to 66% when engraved into higher-index oxides. Using the method proposed here, the phase difference range across the metasurface could be increased by 0.4 radians beyond the range achievable by a metasurface engraved in silica and could reach even higher values when embedded in higher-index materials. Full-wave numerical simulations are used to demonstrate a cylindrical metareflector and a metalens, further validating the analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Delamination-informed lifecycle decisions: A dielectric and machine learning framework for composite sorting and recycling

Composite materials are widely used in aerospace, marine, and automotive sectors due to their high strength-to-weight ratio and durability. However, their long-term reliability can be compromised by damage accumulation. Specifically, delamination initiation serves as a precursor to structural failure, which is often difficult to detect during damage inspection. Identifying and sorting delamination initiation in samples not only increases operational safety while providing critical information for end-of-life decisions, which influences both the service life extension value and the efficiency of fiber extraction during recycling. This research addresses two challenges: (1) developing a nondestructive, ex-situ framework to sort composite materials based on damage severity, particularly delamination, and (2) understanding how damage in composites influences resin removal during pyrolysis. Both experimental work and finite element analysis were performed to predict critical stress levels that are associated with delamination onset. Based on these results, three loading levels 50 %, 75 %, and 90 % of maximum stress, were selected for controlled experiments, generating composite samples with varying extents of damage for machine learning model training. Microscopic imaging of these samples confirmed the damage progression from matrix cracking to delamination, validating the computational predictions. We explored supervised machine learning using dielectric measurements to classify damage states. Preliminary results show an artificial neural network can identify early delamination which is a potential precursor to failure, with 94.44 % accuracy on our dataset. A parallel investigation into the effect of damage severity on pyrolysis recycling showed that heavily delaminated samples required significantly less energy for comparable matrix removal than undamaged samples.

dielectric variables↗
DOE OSTI2023

Creep-Resistant Ferritic-Martensitic Steels for Power Plant Applications

Creep-resistant ferritic-martensitic steels are essential materials used in power plants for electricity generation. The efficiency and emissions of the plant are closely related to the operating temperature. Materials used in the hottest sections are one of the main limiting factor for plant operation at 650 °C. This review describes the recent development of creep-resistant ferritic-martensitic steels from a large dataset of in-house research. The findings are tied to available literature for discussion. A first steel, CPJ-7, was developed with an operating temperature approaching 650 °C, and a refined design, JMP, showed the potential to operate at 650 °C. Approximately fifty different ingots, each weighing ~ 7 kg, were vacuum induction melted and processed. Overall, wrought and cast versions of CPJ-7 present superior creep properties when compared to wrought and cast versions of COST alloys for steam turbine and P91/92 for boiler applications. The prolonged creep life was attributed to slowing down the process of the destabilization of the MX and M 23 C 6 precipitates at 650 °C. The cast version of CPJ-7 also revealed superior mechanical performance, above commercially available cast 9% Cr martensitic steel or derivatives. Following the work on CPJ-7, the JMP steels were designed with higher Co for increased solid solution strengthening, Si for oxidation resistance and increased W for matrix strength and stability. The JMP steels showed increases in creep life compared to CPJ-7 between 118 and 150% at 650 °C for testing at various stresses between 138 and 207 MPa. On a Larson–Miller plot, the performance of the JMP steels surpasses that of state-of-the-art MARBN steel. The influence of various elements within the composition of the alloys on the microstructure and mechanical properties is discussed. This review presents approximately 420,000 h of in-house creep testing, the equivalent of almost 50 years of cumulative creep tests.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Exponential time differencing scheme for mass transport and depletion in molten salt reactors

This work extends the capability previously shown for addressing the problem of computing depletion and mass transport calculations in molten salt reactors (MSRs) by calculating matrix exponentials. Additional algorithms are implemented to compute the matrix exponential and the action of the matrix exponential on a matrix. These algorithms include two methods based on the Pade approximation, a Taylor series method, and three methods based on Cauchy's integral formula. In addition to the added matrix exponential solvers, a variable-order total variation diminishing scheme is applied to the convective flux approximation to provide enhanced accuracy. Finally, a simplified MSR problem is shown for each of the exponential time differencing solvers along with classical backwards differencing integrators. The results show excellent convergence for exponential time differencing methods. Computation time is a key element for selecting the optimal solver in these problems, and this work shows that Pade and Cauchy-based solvers may provided the fastest and most accurate solutions. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Cs 3 Bi 2 I 9 -hydroxyapatite composite waste forms for cesium and iodine immobilization

Perovskite-based ceramic composites were developed as potential waste form materials for immobilizing cesium (Cs) and iodine (I) with high waste loadings and chemical durability. The perovskite Cs 3 Bi 2 I 9 has high Cs (22 wt%) and I (58 wt%) content, and thus can be used as a potential host phase to immobilize these critical radionuclides. In this work, the perovskite Cs 3 Bi 2 I 9 phase was synthesized by a cost effective solution-based approach, and was embedded into a highly durable hydroxyapatite matrix by spark plasma sintering to form dense ceramic composite waste forms. The chemical durabilities of the monolithic Cs 3 Bi 2 I 9 and Cs 3 Bi 2 I 9 -hydroxyapatite composite pellets were investigated by static and semi-dynamic leaching tests, respectively. Cs and I are incongruently released from the matrix for both pure Cs 3 Bi 2 I 9 and composite structures. The normalized Cs release rate is faster than that of I, which can be explained by the difference in the strengths between Cs-I and Bi-I bonds as well as the formation of insoluble micrometer-sized BiOI precipitates. The activation energies of elemental releases based on dissolution and diffusion-controlled mechanisms are determined with significantly higher energy barriers for dissolution from the composite versus that of the monolithic Cs 3 Bi 2 I 9 . The ceramic-based composite waste forms exhibit excellent chemical durabilities and waste loadings, commensurate with the state-of-the-art glass-bonded perovskite composites for I and Cs immobilization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Rejuvenation as the origin of planar defects in the CrCoNi medium entropy alloy

Abstract High or medium- entropy alloys (HEAs/MEAs) are multi-principal element alloys with equal atomic elemental composition, some of which have shown record-breaking mechanical performance. However, the link between short-range order (SRO) and the exceptional mechanical properties of these alloys has remained elusive. The local destruction of SRO by dislocation glide has been predicted to lead to a rejuvenated state with increased entropy and free energy, creating softer zones within the matrix and planar fault boundaries that enhance the ductility, but this has not been verified. Here, we integrate in situ nanomechanical testing with energy-filtered four-dimensional scanning transmission electron microscopy (4D-STEM) and directly observe the rejuvenation during cyclic mechanical loading in single crystal CrCoNi at room temperature. Surprisingly, stacking faults (SFs) and twin boundaries (TBs) are reversible in initial cycles but become irreversible after a thousand cycles, indicating SF energy reduction and rejuvenation. Molecular dynamics (MD) simulation further reveals that the local breakdown of SRO in the MEA triggers these SF reversibility changes. As a result, the deformation features in HEAs/MEAs remain planar and highly localized to the rejuvenated planes, leading to the superior damage tolerance characteristic in this class of alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Growth of Cd 0.9 Zn 0.1 Te 1-y Se y Single Crystals for Room Temperature Gamma-Ray Detection

Quaternary Cd 0.9 Zn 0.1 Te 1-y Sey (CZTS) single crystals, a novel room-temperature nuclear radiation detector semiconductor material, have been grown using a modified vertical Bridgman method (VBM) and a travelling heater method (THM). The percentage concentration of selenium in the VBM-grown crystal was 3% and that in the THM-grown crystal was 2%. While the THM Frisch collar detector (4.4 × 4.4 × 10.7 mm 3 ) produced a highly resolved pulse height spectra (PHS) with a resolution of ~1.06% for 662-keV gamma rays without any correction, the VBM-grown detector (10 × 10 × 2 mm 3 ) offered a high energy resolution of ~2% after application of a digital biparametric correction. The high-resolution performance of these detectors has been attributed to the addition of Se in the Cd 0.9 Zn 0.1 Te (CZT) matrix. Ab-initio calculations based on density functional theory (DFT) also confirmed that the addition of Se in the CZT matrix helps to reduce the formation of TeCd and the TeZn anti-sites. The VBM-grown crystals were characterized using powder x-ray diffraction (XRD) and energy dispersive x-ray spectroscopy (EDS). While XRD results revealed sharp diffraction peaks confirming the crystalline nature of the grown crystal, the EDS results confirmed the targeted stoichiometry of the elemental composition. The bulk resistivity of the grown crystal was calculated to be ~3 × 10 10 Ω-cm from current-voltage characteristics recorded at room temperature in a planar configuration, ensuring that the grown CZTS crystals have low dark current as required for detector-grade crystals.

42 ENGINEERING↗
DOE OSTI2022

Deep Neural Network Algorithm for CMC Microstructure Characterization and Variability Quantification

Microstructure characterization and variability quantification are crucial for understanding ceramic matrix composites (CMCs) mechanical behavior and deformation mechanisms across length scales. Traditionally, analyses of the micrographs obtained from microscopy are labor-intensive. However, with the vast improvement in computer vision (CV) and deep learning (DL), an automated algorithm can be designed to extract essential microstructure variability from micrographs which can then be used to construct a statistically representative volume element (SRVE). The DL-based algorithm spans the taxonomy of microstructure analyses, including semantic segmentation of microstructure constituents, secondary phases, matrix/fiber interface, and defects, and quantifying the microstructure variability in terms of probability distributions. In this work, C/SiNC and SiC/SiNC CMCs microstructures are semantically segmented through a deep convolutional neural network, followed by variability quantification through the implementation of a fully connected regression layer, hence forming a deep regression network. The deep regression network operates in a feedforward regime, in which the neuron output signal traverses through the network in a unidirectional manner. The weight tensor associated with each layer is updated through a backpropagation stochastic gradient descent approach. The input gray-scale image obtained through in-house scanning electron microscope and confocal microscope micrographs is augmented through affine transformations to increase the training set size, which is then processed through four strided convolutional layers. This compresses the image resolution by half at each layer while increasing the image depth by applying different filters (image encoding). The class activation maps (CAMs) corresponding to the applied filters highlight the key architectural features and assist with the semantic segmentation of the microstructure.

Hamza, Mohamed H.↗
DOE OSTI2025

Developing Fluorescence-Based Sensors to Support Rare Earth Element Separation

Rare earth elements (REEs) are essential to most renewable energy technologies. Unfortunately, as we transition to sustainable energy production, the demand for REEs is rapidly growing well beyond current rates of production. As a result, novel means of efficient, scalable, and easily adaptable methods for processing primary and recycle feedstocks are needed. Development and integration of sensors for highly selective in-line monitoring can support more efficient design and testing of such novel separation processes, as well as more cost-effective deployment of those separation flowsheets. Work here will explore the application of fluorescence spectroscopy, a highly sensitive and selective technique, to quantify multiple lanthanides in complex mixtures including known interferents or quenching agents. Results include identification of the optimal excitation wavelength and the limit of detection of various rare earth elements as well as the performance of data-science-based quantification approaches in streams where “unknowns” are present. Overall, the data science tools in conjunction with optical sensor data were able to quantify analytes in the presence of other lanthanides which can be anticipated in the actual industrial stream. Here we include characterization of lanthanides in a microfluidic device similar to those used in new process development. This study demonstrates the capability of utilizing fluorescence spectroscopy to quantify analytes in a complicated solution matrix, suggesting this is a successful approach for in-line monitoring to optimize the separation efficiency in an industrial stream.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning constitutive relations using symmetric positive definite neural networks

In this work, we present a new neural-network architecture, called the Cholesky-factored symmetric positive definite neural network (SPD-NN), for modeling constitutive relations in computational mechanics. Instead of directly predicting the stress of the material, the SPD-NN trains a neural network to predict the Cholesky factor of the tangent stiffness matrix, based on which the stress is calculated in incremental form. As a result of this special structure, SPD-NN weakly imposes convexity on the strain energy function, satisfies the second order work criterion (Hill's criterion) and time consistency for path-dependent materials, and therefore improves numerical stability, especially when the SPD-NN is used in finite element simulations. Depending on the types of available data, we propose two training methods, namely direct training for strain and stress pairs and indirect training for loads and displacement pairs. We demonstrate the effectiveness of SPD-NN on hyperelastic, elasto-plastic, and multiscale fiber-reinforced plate problems from solid mechanics. The generality and robustness of SPD-NN make it a promising tool for a wide range of constitutive modeling applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Spectrally Stabilized Interface Capturing Formulation and Implementation in Nek5000/NekRS

This report documents the formulation of a novel level-set method for incompressible two-phase flows in the continuous Galerkin (CG) high order spectral element framework. The overall method hinges on a novel implementation of the spectral vanishing viscosity (SVV) operator for the stabilization of linear/non-linear hyperbolic problems. The multidimensional SVV convolution kernels, which in essence, have a similar effect as a high pass filter applied to the derivatives, are formulated by exploiting the tensor product form, analogous to the construction of the usual stiffness matrix system. The resulting kernels are directionally decoupled and ensure a linear, symmetric positive definite, elliptic matrix operator. The SVV formulation is demonstrated to provide a robust stabilizing mechanism through challenging linear and non-linear hyperbolic problems, including problems pertinent to the level-set formulation. The two-phase framework conceptualized herein is based on the conservative level-set (CLS) method which represents the interface between the fluids by the 0.5 iso-contour of the smoothed Heaviside function. The CLS method is augmented with a preconditioning procedure for interface normals using the signed distance function which precludes the manifestation of spurious oscillations in the vicinty of the interface. Further, the existing mixed explicit-implicit approach for the solution of Navier-Stokes equations in Nek5000, as described in Tomboulides et al, is augmented with a pressure coefficient splitting approach for the Poisson equation, which greatly accelerated the convergence of pressure solver for two-phase systems with large density ratio. The robustness and accuracy of the overall two-phase method is demonstrated through canonical challenging problems involving high density and viscosity ratios, with and without surface tension. The two-phase formulation is wholly implemented in Nek5000 and the SVV stabilization method is implemented in NekRS, which is the essential precursor to the two-phase framework, undergoing active development.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Carbide-based fuel assembly for thermal propulsion applications

Carbide-based fuel assembly includes outer structural member of ceramic matrix composite material (e.g., SiC—SiC composite), insulation layer of porous refractory ceramic material (e.g., zirconium carbide with open-cell foam structure or fibrous zirconium carbide), and interior structural member of refractory ceramic-graphite composite material (e.g., zirconium carbide-graphite or niobium carbide-graphite). Spacer structures between various layers provide a defined and controlled spacing relationship. A fuel element bundle positioned between support meshes includes a plurality of distributively arranged fuel elements or a solid, unitary fuel element with coolant channels, each having a fuel composition including high assay, low enriched uranium (HALEU). Fuel assemblies are distributively arranged in a moderator block and the upper end of the outer structural member is attached to a metallic inlet tube for hydrogen propellant and the lower end of the outer structural member is interfaced with a support plate, forming a nuclear thermal propulsion reactor.

Barringer, Eric A.↗
DOE OSTI2023

Carbide-based fuel assembly for thermal propulsion applications

Carbide-based fuel assembly includes outer structural member of ceramic matrix composite material, the interior surface of which is lined in higher temperature regions with an insulation layer of porous refractory ceramic material. Continuous insulation layer extends the length of the fuel assembly or separate insulation layer sections have a thickness increasing step-wise along the length of the fuel assembly from upper (inlet) section towards bottom (outlet) section. A fuel element positioned inward of the insulation layer and between support meshes has a fuel composition including HALEU and the form of a plurality of individual elongated fuel bodies or one or more fuel monolith bodies containing coolant flow channels. Fuel assemblies are distributively arranged in a moderator block, with upper end of the outer structural member attached to an inlet for propellant and lower end of the outer structural member operatively interfaced with a nozzle forming a nuclear thermal propulsion reactor.

Barringer, Eric A.↗
DOE OSTI2020

Modeling The Effects of Loading Scenario and Thermal Expansion Coefficient on Potential Failure of Cryo-compressed Hydrogen Vessels

A multiscale thermomechanical model for a simplified Type-3 cryogenic compressed hydrogen (H2) storage vessel is developed in this paper. The model accounts for the temperature-dependent elastic-plastic behavior of the vessel carbon/epoxy composite overwrap and aluminum alloy liner. The homogenized thermo-elastic-plastic behavior for the individual laminas of the vessel layup is obtained by an incremental Eshelby-Mori-Tanka approach associated with a micromechanical failure criterion to predict lamina failure while a standard elastic-plastic constitutive model is used to describe the behavior of a typical aluminum alloy assumed for the liner. The vessel response to external loadings is achieved by a finite element method. Four loading scenarios representing four thermomechanical cycles applied to the vessel are analyzed to evaluate constituent and lamina stresses as well as the associate failure criterion during the cycle according to these scenarios. The model can provide helpful guidance to mitigate thermal stresses by an adequate selection of loading scenario, optimizing the layup and by tailoring thermomechanical properties of the resin matrix.

36 MATERIALS SCIENCE↗