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At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improvement of the $\mathrm{BISON U_3Si_2}$ modeling capabilities based on multiscale developments to modeling fission gas behavior

Uranium silicide (U 3 Si 2 ) is a concept explored as a potential alternative to UO 2 fuel used in light water reactors (LWRs) since it may improve accident tolerance and economics due to its higher thermal conductivity and increased uranium density. U 3 Si 2 has been previously used in research reactors in the form of dispersion fuel, but operated at lower temperatures than commercial LWRs. The research reactor data illustrated that significant gaseous swelling occurs as the fuel burnup increases. Therefore, it is imperative to understand the fission gas behavior of U 3 Si 2 under higher temperature LWR operating conditions. In this work, molecular dynamics and phase-field modeling techniques are used to reduce the uncertainty in select modeling assumptions made in developing the fission gas behavior model for U 3 Si 2 in the BISON fuel performance code. These lower length scale informed models are then utilized in the validation of BISON U 3 Si 2 modeling capabilities to simulate the ATF-1 experiments irradiated in the Advanced Test Reactor (ATR). Sensitivity analysis (SA) and uncertainty quantification (UQ) are included as part of the validation process to identify where further experiments and lower length scale modeling would be beneficial. Here, the multiscale modeling approach utilized in this work can be applied to new fuel concepts being explored for both LWRs and advanced reactors (e.g., uranium nitride, uranium carbide).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Documentation, assessment, and improvements of fission gas modeling capabilities in BISON

Fission gas behavior critically affects nuclear fuel performance, influencing thermal conductivity, microstructure, swelling, and fuel-cladding interactions. The Simple Integrated Fission Gas Release and Swelling (Sifgrs) model, implemented within the BISON fuel performance code, provides advanced capabilities for modeling these phenomena. This manuscript reviews and assesses Sifgrs’ modular structure and submodels describing intragranular, intergranular, and release mechanisms. The modular implementation enables rapid improvements as our understanding advances and new fuel forms emerge. After an overview of capabilities, we present a comprehensive validation suite, comparing predictions to experimental data from tests under normal and transient conditions, including reproduction of the Vitanza curve. Results demonstrate Sifgrs’ accuracy, robustness, and applicability. Current limitations and ongoing efforts are discussed, including mechanistic, multiscale modeling, uncertainty quantification, and validation within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, supporting the safe and efficient use of nuclear fuels.

BISON↗
DOE OSTI2025

A low-cost, fluorine-free electrolyte for improved sodium batteries

Sodium batteries are an attractive alternative to lithium-ion technologies due to sodium’s lower cost and natural abundance—over 1,000 times greater than lithium, comprising approximately 2.4% of the Earth’s crust. However, most current electrolytes rely on fluorine-containing components, which raise economic and environmental concerns. This study explores fluorine-free, borate-based electrolytes that offer improved cycling stability and significant cost and sustainability benefits. We demonstrate stable sodium metal stripping and plating on aluminum foil, enabling anode-free cell configurations with over 50% capacity retention after 700 cycles. Spectroscopic and electrochemical analyses reveal the effects of solvents and salt composition on solvation, ionic conductivity, and oxidative stability. In full-cell configurations, the fluorine-free electrolyte maintains more than 98% capacity retention after 400 cycles. Furthermore, these findings represent a critical step toward the development of cost-effective, environmentally friendly, and high-performance sodium battery systems suitable for future electrification.

anode-free sodium batteries↗
DOE OSTI2021

Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency

Synchronous SGD with data parallelism, the most popular parallelization strategy for CNN training, suffers from the expensive communication cost of averaging gradients among all workers. The iterative parameter updates of SGD cause frequent communications and it becomes the performance bottleneck. In this paper, we propose a lazy parameter update algorithm that adaptively adjusts the parameter update frequency to address the expensive communication cost issue. Our algorithm accumulates the gradients if the difference of the accumulated gradients and the latest gradients is sufficiently small. Here, the less frequent parameter updates reduce the per-iteration communication cost while maintaining the model accuracy. Our experimental results demonstrate that the lazy update method remarkably improves the scalability while maintaining the model accuracy. For ResNet50 training on ImageNet, the proposed algorithm achieves a significantly higher speedup (739.6 on 2048 Cori KNL nodes) as compared to the vanilla synchronous SGD (276.6) while the model accuracy is almost not affected (<0.2% difference).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗
DOE OSTI2025

Improved creep performance of CoCrFeNi high entropy alloys by Mo addition

The CoCrFeNi family of high entropy alloys has good mechanical and corrosion properties, performing like stainless steels with low yield stresses. They could, however, benefit from hardening methods for structural and high temperature applications. Here, this work investigates the potential for the CoCrFeNi alloy to be hardened with the addition of Mo. A series of CoCrFeNiMo alloys and their CoCrFeNi counterparts were subsequently manufactured at NETL and then mechanically creep tested at 650°C. The alloys were designed to possess a range of stacking fault energies which influence the mechanical properties of the alloys via the motion of their dislocations under stress. Results show that Mo-containing variants have creep rupture lives ranging from 4000 to 8000 hours, i.e., about two orders of magnitude longer than those for Mo-free variants. The significantly improved creep performance of the Mo-containing alloys is due to a combination of solid solution strengthening and precipitation strengthening from µ-phase and carbides.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Super-resolution model for overlapping peak detection and improved spatial resolution in high-energy diffraction microscopy

Reconstruction quality in Far-field High-Energy Diffraction Microscopy (FF-HEDM) is limited by the spatial resolution of area detectors and the frequent occurrence of overlapping diffraction spots. To address these challenges, we developed a super-resolution (SR) framework using convolutional neural networks (CNNs) to recreate 2D diffraction peaks at up to ×8 resolution from raw detector data. A specialized simulation tool was created to generate synthetic training datasets with varying degrees of peak overlap. Integrated into the Microstructural Imaging using Diffraction Analysis Software (MIDAS), the SR model improves the spatial accuracy and precision of 3D grain reconstruction by an order of magnitude. This approach provides a robust solution for investigation of complex micromechanical states and material classes where the analysis is limited by the presence of overlapping peaks. Furthermore, the methodology developed here can potentially be extended to other techniques that require sub-pixel accuracy for high-fidelity data analysis.

High-energy diffraction microscopy↗
DOE OSTI2024

Electrophoretic deposition for improved trace element homogeneity in silica reference materials

Spatially-resolved analysis requires homogeneous reference materials (RMs) to make reliable quantitative measurements. Here we previously developed an electrophoretic deposition (EPD) method for fabrication of glassy microanalytical RMs with superior platinum-group element homogeneity **[1], and now include further dopants. For 39 trace elements, we analyzed dopant homogeneity in sintered silica (SiO 2 ) samples consolidated either mechanically by die-pressing (DP) or by EPD. A set of EPD and DP samples was made from each of two nanoparticle feedstocks produced by variations of the Stöber process **[2]. Spatially-resolved dopant distribution was characterized in all samples by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS). In both sample sets, homogeneity of most trace elements was substantially improved by EPD relative to their DP counterpart.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A systematic study and framework of fringe projection profilometry with improved measurement performance for in-situ LPBF process monitoring

Fringe Projection Profilometry (FPP) is a cost-effective and non-invasive technology that has been shown to measure finer features. Here, in this work, we developed an in-situ FPP method to measure the dynamic topography of powder bed and printed layer during Laser Powder Bed Fusion (LPBF) additive manufacturing (AM) process. A systematic study towards developing a comprehensive framework of LPBF-specific FPP is demonstrated to enhance and evaluate the performance of applying FPP for in-situ LPBF monitoring, including 1) a modified sensor model with localized correction; 2) improved phase unwrapping with FFT filtering 3) quantitative uncertainty analysis; and 4) experimental validation with ex-situ characterization. The developed LPBF-specific FPP system and methods are implemented on a commercial LPBF-AM machine, achieving better accuracy, more robustness, and increased field of view while maintaining sufficient measurement range and decent resolution, in contrast to literature methods. The established FPP framework will facilitate the development of closed-loop control strategies for advancing LPBF based AM.

42 ENGINEERING↗
DOE OSTI2025

Surface engineering of polymeric membranes with metal oxides for improved fouling resistance and superior oil-water separation

Rapid industrial growth has increased the need for efficient membranes to separate oil-water emulsions. Polyvinylidene fluoride (PVDF) membranes, although widely used due to their chemical inertness and favorable mechanical properties, suffer from fouling due to their intrinsic hydrophobicity. Modifying these membranes after fabrication offers a practical solution as it easily fits into existing large-scale manufacturing processes. Atomic layer deposition (ALD), an atomically-precise vapor phase surface modification technique, can create ultrathin metal oxide layers that greatly improve membrane hydrophilicity without significantly affecting the original pore size. However, PVDF's lack of reactive chemical moieties makes ALD challenging. Here, in this study, we present a simple alkali treatment that greatly enhances ALD nucleation and growth on PVDF membranes. This treatment imparts exceptional oil-water emulsion separation capabilities and antifouling behavior in PVDF membranes after just a few ALD cycles, surpassing the performance of PVDF membranes coated with hundreds of ALD cycles. This dramatic reduction in the number of ALD cycles required could enable cost-effective modification of commercial PVDF membranes at scale using spatial, roll-to-roll ALD. These modified membranes outperform reported modified PVDF membranes, with >99 % permeance recovery and <1 % irreversible loss of permeance and >98 % oil rejection from oil-water emulsions over 100 h continuous operation, making them promising for advanced water purification technologies.

Atomic layer deposition↗
DOE OSTI2026

Improved manufacturability of vacuum thermoforming molds by segmentation

Thin polymer shell components used in consumer goods, the automotive industry, and marine applications are often produced by thermoforming. Depending on the quantity of parts, molding processes can increase throughput significantly when compared to processes such as milling or additive manufacturing. One disadvantage to mold-based manufacturing methods, however, is that molds must be produced to form the required geometry. Mold production is often complex and requires a large capital investment. For large-scale molding processes, mold production is sometimes complicated by the requirement for large working volume machine tools which are capable of machining the cavities to the appropriate tolerances for the selected molding process. Here, this paper focuses on a segmented design for a large draw ratio (>3:1 molded surface area to sheet surface area) thermoforming mold and a cost-benefit analysis of segmentation versus monolithic design. Discussion of the thermoforming tests are included. The primary goals were to improve manufacturability and reduce the overall cost of the mold.

Manufacturability↗
DOE OSTI2021

A multistep enrichment process with custom growth medium improves resuscitation of chlorine-stressed coliforms from secondary sewage effluents

Resuscitation and detection of stressed coliforms in treated water samples is needed to assess and prevent health effects from adverse exposure. Herein, we developed a custom growth media that improved total coliform resuscitation from chlorine-stressed samples from secondary effluent to see if detection time could be reduced from 18 to 8 hours.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

AA2024/SiC metal matrix composites simultaneously improve ductility and cracking resistance during elevated temperature deformation

Here this study uses the stir-casting technique to combine a semi-solid AA2024 alloy directly with finely-sized β-SiC p embedded as a powder or with mechanically alloyed granules as a delivery agent. Liquid-state primary fabrication tends to form agglomerates of reinforcement particles, whereas rolling better distributes the composite constituents. Sub-micron reinforcements of low volume fractions do not significantly increase the hardness of the composite materials. Uniaxial tensile testing at elevated temperatures over a wide range of strain rates showed simultaneous increases in the ductility and crack resistance of AA2024 + SiC p granules embedded as a powder when compared to the non-reinforced control material at lower strain rates, with the same toughness as the control material. The maximum engineering strain of 252.7 ± 19.2% was observed in AA2024/SiCp at a strain rate of 10 -4 s -1 . This improvement in properties is attributed to grain refinement in the MMCs, leading to pinning events during the straining and ductility increases. The resultant impediments to grain growth and crack propagation allow the fine-sized reinforcements to control dynamic microstructural changes during fatigue. Cube {001}<100> is a dominant texture component in AA2024, whereas the Goss {011}<100> and S {123}<634> components mainly represent the texture of the discontinuously reinforced aluminum matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simultaneously improved electrical and mechanical performance of hot-extruded bulk scale aluminum-graphene wires

Aluminum-based alloys are highly sought after as lightweight alternatives in electric grid applications. Improving the electrical conductivity of aluminum alloys has the potential to increase the energy efficiency of power transport. Here we used a hot extrusion process to synthesize AA1100 alloy with low-cost reduced graphene oxide nanoparticles to manufacture ultra-conductive aluminum composites in this study. The effects of graphene content on the electrical and mechanical performance of the composites were evaluated. The macroscale AA1100/graphene wires demonstrated a 2.1% enhancement in electrical conductivity at 20 °C, while the ultimate tensile strength increased by 6.1%. A Zener-Hollomon model was used to confirm the in-process exfoliation of the agglomerated graphene nanoparticle feedstock into high electrical conductivity graphene-like flakes during extrusion. They may have provided high-velocity carrier pathways leading to the enhanced electrical performance of the alloy. Transmission electron microscopy at aluminum-graphene interfaces ensures the preclusion of detrimental carbide formation during composite synthesis while confirming the structure graphene-like flakes. The in-process exfoliation provides an economically viable technique to produce bulk scale graphinated aluminum composites for advanced application and can be applied more generally to other alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Improvement of interfacial adhesion of unidirectional textile grade carbon fiber (TCF) with unsized, epoxy and urethane sizing reinforced in thermoset urethane composites

This work considers a unique wide tow (450k filaments) form of low-cost carbon fiber intended for non-aerospace applications. TCF is currently produced in epoxy and urethane sizing, and there is a need to understand its resulting composite properties. In this work, the interfacial adhesion of sized TCF reinforced in thermoset urethane (TSU) composites are examined through the surface, thermal and mechanical characterization techniques. Atomic force microscopy (AFM) results showed an increase in surface roughness for urethane (276%) and epoxy sized (78%) versus unsized TCF. XPS results showed 531% increase in O in epoxy sized TCF and 250% N content in urethane sized TCF compared to unsized TCF. The surface energy of epoxy and urethane sized TCF is enhanced by 78% and 96%, respectively compared to unsized TCF. The storage modulus showed improvement for urethane (23%) and epoxy (21%) sized than unsized TCF-TSU composites. The flexural, interlaminar shear strength (ILSS), and impact properties of urethane sized TCF increase by 24%, 50%, and 273%, respectively, than unsized TCF. The results demonstrate that the surface and thermal properties correlate with the mechanical properties of TCF-TSU composites and sizing enhances the wettability of the composites.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improving the bulk gas transport of Fe-N-C platinum group metal-free nanofiber electrodes via electrospinning for fuel cell applications

To overcome transport limitations associated with thicker platinum group metal-free (PGM-free) electrodes, it's essential to investigate and tailor alternative electrode architectures to maximize the bulk electrode transport properties, whilst not significantly impeding electrocatalyst active site accessibility and electrode proton resistance. In this work, PGM-free nanofiber electrode mats, prepared by electrospinning a mixture of pyrolyzed Fe-N-C catalyst, Nafion ionomer and a carrier polymer Poly Acrylic acid (PAA), were compared to traditionally prepared electrodes. The morphological properties and elemental distribution of the fabricated nanofiber electrodes showed that the exterior surface of the PGM-free nanofibers was conformally covered with a thin ionomer film. Electrochemical diagnostics performed utilizing cyclic voltammetry, electrochemical impedance spectroscopy (EIS) and H 2 -limiting current measurements, revealed an increase in electric double layer capacitance, reduction in electrode proton transport and a significant improvement in bulk-electrode gas transport properties for the nanofiber electrodes, supporting the observed performance increase from electrochemical polarization data obtained in H 2 -O 2 /Air fuel cells. At 100% RH in H2/Air, the power density of the nanofiber electrodes increased ca. 50% vs. the traditionally prepared electrodes (ca. 260 vs 175 mW cm -2 ), which was attributed to a less tortuous molecular diffusion pathway and an associated reduction in the pressure dependent and independent gas transport resistances in the nanofiber electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

Improved Cumulative Fission Yield Measurements with Fission Spectrum Neutrons on 238U

Two isotopically-characterized targets of high-purity (99.961±0.002%) 238U metal and a neutron dosimetry package were exposed to a pulsed neutron irradiation using the Godiva IV critical assembly at the National Criticality Experiments Research Center to measure the integral fission product yields produced in a Watt fission neutron-energy spectrum. Following irradiation, the fissioned targets were counted with two broad energy germanium detectors. Estimates of the total fissions for each target were made using measurements of well-known fission products. The total number of fissions in each target were then used to measure the fission yields of 33 fission products, 18 of which are published as new measurements. Twelve of the 18 isotopes were in agreement with ENDF/B- VIII.0 using the zeta test and treating ENDF/B-VIII.0 as the true mean: 84mBr, 93Y, 94Y, 104Tc, 134I, 135I, 138Xe, 141Ba, 142Ba, 142La, 146Ce, and 149Nd. The ENDF/B-VIII.0 fission yield for 139Ba was only slightly outside of 1-s relative to the uncertainty measured in this work. Uncertainties for 5 fission product yields were markedly improved over those reported in ENDF/B-VIII.0: 128Sn, 129Sb, 130gSb, 131mTe, and 133I. The fission yields measured for 128Sn, 131mTe, and 133I were in good agreement with the ENDF/B-VIII.0 predicted fission yields. However, fission yields measurements of the isotopes 129Sb, and 130gSb deviated from the ENDF/B-VIII.0 value. Both of these isotopes reside near the doubly magic nuclear shell closures of 50 protons and 82 neutrons. These fission yields may be suppressed because of the odd-odd nuclear structure of 130gSb and the odd-even structure of 129Sb and isomer splitting. Comparisons to other recent work and a brief review with citations are included to support this conclusion.

Fission Yields↗
DOE OSTI2023

Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm

The Locus Algorithm is a new technique to improve the quality of differential photometry by optimising the choices of reference stars. At the heart of this algorithm is a routine to assess how good each potential reference star is by comparing its SDSS magnitude values to those of the target star. In this way, the difference in wavelength-dependent effects of the Earth’s atmospheric scattering between target and reference can be minimised. This paper sets out a new way to estimate the quality of each reference star using machine learning. A random subset of stars from SDSS with spectra was chosen. For each one, a suitable reference star, also with a spectrum, was chosen. The correlation between the two spectra in the SDSS r band (between 550 nm and 700 nm) was taken to be the gold-standard measure of how well they match up for differential photometry. The five SDSS magnitude values for each of these stars were used as predictors. A number of supervised machine learning models were constructed on a training set of the stars and were each evaluated on a testing set. The model using Support Vector Regression had the best performance of these models. It was then tested on a final, hold-out, validation set of stars to get an unbiased measure of its performance. With an R 2 of 0.62, the SVR model presents enhanced performance for the Locus Algorithm technique.

79 ASTRONOMY AND ASTROPHYSICS↗