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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Computational micromechanics model based failure criteria for chopped carbon fiber sheet molding compound composites

Chopped carbon fiber sheet molding compound has a great potential in lightweight automotive, marine, and aerospace applications. One of the most challenging tasks is to predict the failure strength of the material due to its anisotropy and heterogeneity, as well as complex stress states in real-world working conditions. In this work, a novel constitutive model of carbon fiber chip is proposed to capture the pre- and post-failure behaviors under different loading modes. On this basis, we propose a new computational micromechanics model, which is calibrated and validated by uniaxial tensile, compressive, and in-plane shear experiments. Furthermore, a set of microstructures representative volume element (RVE) models under complex loading conditions are reconstructed to understand the relationship between the microstructure characteristics and the failure envelopes. Finally, several modified versions of classical failure criteria are proposed for anisotropic materials with consideration of the fiber orientation tensor. The modified Tsai-Wu failure criterion, which shows the best accuracy among all failure criteria, is highlighted in the comparative study.

36 MATERIALS SCIENCE↗
DOE OSTI2021

25-Pin metallic fuel performance benchmark case based on the EBR-II X430 experiments series

A metallic fuel benchmark case was developed for the fuel performance code BISON based on 25 uranium-zirconium and uranium-plutonium-zirconium pins of the Experimental Breeder Reactor II X430 experiment series. Results of the benchmarks were compared with measurements and calculations made at the time of the experiment as well as subsequent measurements reported in 2019. The comparisons were used to quantify the accuracy of the BISON predictions and to identify patterns in the BISON differences.BISON predicted burnup, plenum pressure, and fission gas release fractions accurately. BISON temperature predictions were somewhat cooler than the temperatures determined at the time of the experiment but appeared to be reasonably accurate considering uncertainties in the legacy temperature calculations, uncertainties in the legacy linear heat rate calculations, and the high sensitivities of the BISON-predicted temperatures to BISON inputs. Fuel axial elongation predictions had errors correlated to fuel composition; BISON tended to underpredict the elongation of binary fuels and overpredict the elongation of ternary fuels. BISON cladding radial dilation predictions were also significantly lower than legacy PIE measurements.Recommendations were made to improve the BISON fuel gaseous swelling model to account for fuel composition, to add additional capabilities to the coolant channel temperature model to ease benchmark development, and to continue developing benchmark cases based on a wide range of experiments in several reactors. Once a wide array of benchmarks is developed, an attempt can be made to enhance or calibrate BISON models to improve the cladding dilation predictions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Insights into the thermal history of carbon fibers using Raman spectroscopy and a novel kinetic model

Carbon fibers and carbon fiber composites are applied in high-performance applications, but a key consideration for application is their relative sensitivity to oxidative environments. To enable in-situ characterization of carbon fibers exposed to oxidative conditions, the Raman spectral response of T700 carbon fibers that have been exposed to a variety of dwell temperatures is reported herein with dwell times reaching up to 1 month. Here, we evaluate the spectra holistically by using integrated absolute difference analysis. By combining this analysis with straightforward kinetic models, we connect the total Raman spectral response to the temperature-time curve that could yield such a shift in spectral parameters. Our work connects the Raman spectral response of carbon fibers to their thermal history and can easily be extended to other graphitic materials, such as nuclear graphite.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Formation, cooling history and age of impact events on the IIE iron parent body: Evidence from the Miles meteorite

Most iron meteorites formed in planetary cores during differentiation, but the IIE iron meteorites have chemical and physical features that are inconsistent with this origin. By combining mineral chemistry, mineral modes and three-dimensional petrography, we reconstruct the bulk chemistry of the felsic silicate-bearing Miles IIE iron meteorite and demonstrate that the silicate inclusion compositions are similar to partial melts produced experimentally from an H chondrite composition. We use the reconstructed bulk composition, mineralogy and thermodynamic modelling to show that melting above ~ 1200°C under reducing conditions formed metal (Fe-Ni alloy) and felsic silicate partial melts. Upon cooling, the melts crystallized Mg-rich pyroxenes, Na- and K-rich feldspars, and tridymite. Importantly, this mechanism enriches cosmochemically volatile elements (i.e., those with a 50% condensation temperature of ~430–830°C, like Na and K) to the level found in the felsic silicate inclusions. The presence of crystallographically disordered srilankite (only stable above 1160°C) and an absence of Widmanstätten texture require both high peak temperatures and rapid cooling, which cannot be explained by core formation. Instead, they point to small melt volumes, a transient heat pulse, and small thermal mass, and imply efficient physical segregation of silicate and metallic melts through buoyancy separation followed by rapid cooling that arrested the separation of metal and silicate liquid phases. In situ 207 Pb/ 206 Pb age of 4542.3 ± 4.0 Ma in Zr-oxide and phosphate minerals dates the melting event that formed the silicate inclusions. This age aligns with the earliest ages found in other IIE iron meteorite silicates and requires a heating event ~25 million years after the solar system formed. We found 39 Ar/ 40 Ar ages of 3495 ± 52 Ma (low-T) and 4303 ± 7 Ma (high-T) in a K-feldspar grain, with the 3495 Ma age aligning with later thermal events recorded in other IIE iron meteorites. Dating reveals the complex petrogenetic and thermal history of Miles and the IIE iron meteorites. This is the first IIE iron meteorite found to record evidence of heating at 4.5 and 3.5 Ga likely from impact events. We propose that high-velocity impact(s) into an iron-rich, porous chondritic parent body at ~4.54 Ga produced immiscible metal and silicate melts that cooled rapidly and trapped low density silicate inclusions within high density metal. Other IIE irons that formed at lower peak temperatures (900–1000°C) contain chondritic silicate inclusions and relict chondrules, supporting this conceptual model. In conclusion, this petrogenesis is consistent with thermodynamic modelling, experimental data and the wide range of peak temperatures and cooling rates observed in the IIE iron meteorites.

58 GEOSCIENCES↗
DOE OSTI2024

Effect of glass forming additives on low-activity waste feed conversion to glass

A significant effort was invested in the past to develop and refine mathematical models that relate the composition of nuclear waste glasses with their properties, such as viscosity, electrical conductivity, or chemical durability. However, less attention has been paid to the formulation of the melter feed itself, such as the chemical form and the particle size of the glass forming and modifying additives (GFMA), which have a significant effect on the feed-to-glass conversion process during melting. To address this issue, we systematically changed the mineral composition of a simulated low-activity waste melter feed and inspected its melting behavior. When substituting minerals with corresponding oxides and hydroxides, we found that different alumina sources (kyanite, gibbsite, boehmite, or corundum) had the strongest effect on the feed melting process, whereas the sources of Ca, Mg, and Zr had little effect. Further, the x-ray diffraction analysis showed that the alumina sources differ in their dissolution kinetics: early dissolving alumina sources, such as gibbsite (Al(OH) 3 ) and boehmite (AlO(OH)), increase the transient glass-forming melt viscosity at early stages, when gases still evolve, causing extended foaming, whereas alumina sources that dissolve at high temperatures, such as kyanite (Al 2 SiO 5 ) and corundum (Al 2 O 3 ), keep the transient glass-forming melt viscosity low and lead to a faster foam collapse. Using viscosity-composition relationship to estimate the viscosity of transient glass-forming melts in the primary foaming range, we found that the primary foam began to collapse at 360 to 800 Pa s, and fully collapsed between 65 and 260 Pa s. This result agrees with our previous studies, according to which the glass-forming melt viscosity at the cold cap foam bottom ranged from 24 to 85 Pa s.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Effect of alloying on intrinsic ductility in WTaCrV high entropy alloys

Tungsten (W) exhibits desirable properties for extreme applications, such as the divertor in magnetic fusion reactors, but its practicality remains limited due to poor formability and insufficient irradiation resistance. In this work, we study the intrinsic ductility of body-centered cubic WTaCrV based high entropy alloys (HEAs), which are known to exhibit excellent irradiation resistance. The ductility evaluations are carried out using a criterion based on the competition between the critical stress intensity factors for emission (K Ie ) and cleavage (K Ic ) in the {110} slip planes and {110} crack planes, which are evaluated within the linear elastic fracture mechanics framework and computed using density functional theory. The results suggest that increasing the alloying concentrations of V and reducing the concentrations of W can significantly improve the ductility in these HEAs. The elastic anisotropy for these HEAs is analyzed using the Zener anisotropy ratio and its correlation with the concentration of W in the alloys is studied. Results indicate that these alloys tend to be fairly isotropic independently from the concentration of W in them. The computed data for the elastic constants of these HEAs is also compared against available experimental data. The results are in good agreement, validating the robustness and accuracy of the computational methods. Multiple phenomenological ductility metrics were also computed and analyzed against the analytical model. Some metrics, mainly the surrogate D parameter, show a good correlation with the Rice model. The potential of these empirical metrics to serve as surrogate screening models for optimizing the compositional space is also discussed.

Anisotropy↗
DOE OSTI2023

Evaluation of SNOLAB background mitigation procedures through the use of an ICP-MS based dust monitoring methodology

Dust particulate fallout on materials in use for rare-event searches is a concerning source of radioactive backgrounds due to the presence of the naturally occurring radionuclides 40 K, 232 Th, 238 U, and their progeny in dust. Much effort is dedicated to inform radioactive backgrounds from dust and evaluate the efficacy of mitigation procedures. A great portion of such effort relies on fallout models and assumed dust composition. Here, in this work, an ICP-MS based methodology was employed for a direct determination of fallout rates of radionuclides and stable isotopes of interest from dust particulate at the SNOLAB facility. Hosted in an active mine, the SNOLAB underground laboratory strives to maintain experimental areas at class 2000 cleanroom level. This work validates the mitigation procedures in place at SNOLAB and informs dust backgrounds during laboratory activities. Fallout rates of major constituents of the local rock were measured two to three orders of magnitude lower in the clean experimental areas compared to non-clean transition areas from the mine to the laboratory. An increase of approximately two orders of magnitude in the fallout rate of stable Pb isotopes was determined in an experimental area during activities involving handling of Pb bricks. Increased 40 K, 232 Th, and 238 U fallout rates were measured in clean experimental areas during activities generating particulate.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Mechanistic Analysis of Microstructural Attributes to Lithium Plating in Fast Charging

Metallic lithium deposition on graphite anodes is a critical degradation mode in lithium-ion batteries, which limits safety and fast charge capability. A conclusive strategy to mitigate lithium deposition under fast charging yet remains elusive. Herein, we examine the role of electrode microstructure in mitigating lithium plating behavior under various operating conditions, including fast charging. The multilength scale characteristics of the electrode microstructure lead to a complex interaction of transport and kinetic limitations that significantly governs the cell performance and the occurrence of Li plating. We demonstrate, based on a comprehensive mesoscale analysis, that the performance and degradation can be significantly modulated via systematic design improvements at the hierarchy of length scales. It is found that the improvement in kinetic and transport characteristics achievable at disparate scales can dramatically affect Li plating propensity.

25 ENERGY STORAGE↗
DOE OSTI2021

Tree Canopies Reflect Mycorrhizal Composition

Mycorrhizae alter global patterns of CO 2 fertilization, carbon storage, and elemental cycling, yet knowledge of their global distributions is currently limited by the availability of forest inventory data. Here, we show that maps of tree-mycorrhizal associations (hereafter “mycorrhizal maps”) can be improved by the novel technology of imaging spectroscopy because mycorrhizal signatures propagate up from plant roots to impact forest canopy chemistry. We analyzed measurements from 143 airborne imaging spectroscopy surveys over 112,975 individual trees collected across 13 years. Results show remarkable accuracy in capturing ground truth observations of mycorrhizal associations from canopy signals across disparate landscapes (R 2 = 0.92, p < 0.01). Additionally, upcoming imaging spectroscopy satellite missions can reveal new insights into landscape-scale variations in water, nitrogen, phosphorus, carotenoid/anthocyanin, and cellulose/lignin composition. Applied globally, this approach could improve the spatial precision of mycorrhizal distributions by a factor of roughly 104 and facilitate the incorporation of dynamic shifts in forest composition into Earth system models.

58 GEOSCIENCES↗
DOE OSTI2020

Microbial diversity drives carbon use efficiency in a model soil

Abstract Empirical evidence for the response of soil carbon cycling to the combined effects of warming, drought and diversity loss is scarce. Microbial carbon use efficiency (CUE) plays a central role in regulating the flow of carbon through soil, yet how biotic and abiotic factors interact to drive it remains unclear. Here, we combine distinct community inocula (a biotic factor) with different temperature and moisture conditions (abiotic factors) to manipulate microbial diversity and community structure within a model soil. While community composition and diversity are the strongest predictors of CUE, abiotic factors modulated the relationship between diversity and CUE, with CUE being positively correlated with bacterial diversity only under high moisture. Altogether these results indicate that the diversity × ecosystem-function relationship can be impaired under non-favorable conditions in soils, and that to understand changes in soil C cycling we need to account for the multiple facets of global changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Benchmarking graph neural networks for materials chemistry

Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mechanism of methanol synthesis on Ni(110)

Planewave density functional theory (DFT-PW91) calculations are employed to study the methanol synthesis through CO 2 and CO hydrogenation, as well as the two side reactions: the water gas shift (WGS) reaction and formic acid formation, on Ni(110). For the WGS reaction on Ni(110), we find that the redox mechanism is favored over the carboxyl-mediated mechanism. We show that the formate pathway is the dominant one for formic acid formation. For methanol synthesis through CO 2 and CO hydrogenation on Ni(110), our results reveal that the formic-acid- and dioxymethylene-mediated pathways coexist, in contrast to methanol synthesis on Cu(111) where the formic-acid-mediated pathway dominates. We also find that on Ni(110), hydrogenation of CH 2 O* to CH 3 O* and that to CH 2 OH* both contribute to MeOH synthesis. Based on the derived energetics, we ascertain that CH 3 O* hydrogenation to CH 3 OH* is likely the rate-determining step along the CH 3 O* pathway on Ni(110). Remarkably, CH 3 O* hydrogenation can be facilitated by the presence of HCO*, demonstrating the promotional effect of CO. Furthermore, we show that CO also participates in methanol synthesis directly via its hydrogenation to HCO* and further to CH 2 O*. Additional microkinetic modeling by considering feed composition and reaction conditions would provide further mechanistic insights into methanol synthesis on Ni(110).

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

Computational design of thermoelectric alloys through optimization of transport and dopability

Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Active meta-learning for predicting and selecting perovskite crystallization experiments

Autonomous experimentation systems use algorithms and data from prior experiments to select and perform new experiments in order to meet a specified objective. In most experimental chemistry situations, there is a limited set of prior historical data available, and acquiring new data may be expensive and time consuming, which places constraints on machine learning methods. Active learning methods prioritize new experiment selection by using machine learning model uncertainty and predicted outcomes. Meta-learning methods attempt to construct models that can learn quickly with a limited set of data for a new task. Here in this paper, we applied the model-agnostic meta-learning (MAML) model and the Probabilistic LATent model for Incorporating Priors and Uncertainty in few-Shot learning (PLATIPUS) approach, which extends MAML to active learning, to the problem of halide perovskite growth by inverse temperature crystallization. Using a dataset of 1870 reactions conducted using 19 different organoammonium lead iodide systems, we determined the optimal strategies for incorporating historical data into active and meta-learning models to predict reaction compositions that result in crystals. We then evaluated the best three algorithms (PLATIPUS and active-learning k-nearest neighbor and decision tree algorithms) with four new chemical systems in experimental laboratory tests. With a fixed budget of 20 experiments, PLATIPUS makes superior predictions of reaction outcomes compared to other active-learning algorithms and a random baseline.

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

Fringing Analysis and Simulation for the Vera C. Rubin Observatory’s Legacy Survey of Space and Time

Abstract The presence of fringing in astronomical Charge-Coupled Device (CCD) images will have an impact on photometric quality and measurements. Yet its impact on the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) has not been fully studied. We present a detailed study on fringing for CCDs already implemented on the Rubin Observatory LSST Camera’s focal plane. After making physical measurements and knowing the compositions, we developed a model for the e2v CCDs. We present a method to fit for the internal height variation of the epoxy layer within the sensors based on fringing measurements in a laboratory setting. This method is generic enough that it can be easily modified to work for other CCDs. Using the derived fringing model, we successfully reproduce comparable fringing amplitudes that match the observed levels in images taken by existing telescopes with different optical designs. This model is then used to forecast the expected level of fringing in a single LSST y -band sky background exposure with Rubin telescope optics in the presence of a realistic time-varying sky spectrum. The predicted fringing amplitude in LSST images ranges from 0.04% to 0.2% depending on the location of a CCD on the focal plane. We find that the predicted variation in surface brightness caused by fringing in LSST y -band sky background images is about 0.6 μ Jy arcsec − 2 , which is 40 times larger than the current measurement error. We conclude that it is necessary to include fringing correction in the Rubin’s LSST image processing pipeline.

Astronomy & Astrophysics↗