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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

The Black Pearl mine, Arizona - Wolframite veins and stockscheider pegmatite related to an albitic stock

Wolframite-bearing quartz veins flanked by greisen alteration occur at and near the Black Pearl mine, Yavapai County, Arizona. The veins are genetically related to a small albitite stock, and cut a series of Proterozoic metasedimentary and intrusive rocks. The largest vein, the only one mined, is located at the apex of the stock. Field relations imply that this stock is a late-stage differentiate of time 1.4-Ga anorogenic Lawler Peak batholith, which crops out about 3 km to the south. The albitites are of igneous origin and have suffered only minor deuteric alteration. A thin (1 to 2 m) pegmatite unit ('stockscheider') occurs at the contact of the Black Pearl Albitite stock with the country rocks. Directional indicators and other evidence suggest that the pegmatite was formed in the presence of a volatile-rich fluid phase close to the time of magma emplacement. The sudden change from coarse-grained microcline-rich pegmatite to fine-grained, albite-rich albitite suggests pressure quenching, possibly due to escape of fluids up the Black Pearl vein. Stockscheider-like textures typically occur near the apical contacts of productive plutons. The presence or absence of this texture is a useful guide in prospecting for lithophile metal deposits.

Schmitz, Christopher↗
DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Inhibition mechanisms of AcrF9, AcrF8, and AcrF6 against type I-F CRISPR–Cas complex revealed by cryo-EM

Prokaryotes and viruses have fought a long battle against each other. Prokaryotes use CRISPR–Cas-mediated adaptive immunity, while conversely, viruses evolve multiple anti-CRISPR (Acr) proteins to defeat these CRISPR–Cas systems. The type I-F CRISPR–Cas system in Pseudomonas aeruginosa requires the crRNA-guided surveillance complex (Csy complex) to recognize the invading DNA. Although some Acr proteins against the Csy complex have been reported, other relevant Acr proteins still need studies to understand their mechanisms. As such, here, we obtain three structures of previously unresolved Acr proteins (AcrF9, AcrF8, and AcrF6) bound to the Csy complex using electron cryo-microscopy (cryo-EM), with resolution at 2.57 Å, 3.42 Å, and 3.15 Å, respectively. The 2.57-Å structure reveals fine details for each molecular component within the Csy complex as well as the direct and water-mediated interactions between proteins and CRISPR RNA (crRNA). Our structures also show unambiguously how these Acr proteins bind differently to the Csy complex. AcrF9 binds to key DNA-binding sites on the Csy spiral backbone. AcrF6 binds at the junction between Cas7.6f and Cas8f, which is critical for DNA duplex splitting. AcrF8 binds to a distinct position on the Csy spiral backbone and forms interactions with crRNA, which has not been seen in other Acr proteins against the Csy complex. Our structure-guided mutagenesis and biochemistry experiments further support the anti-CRISPR mechanisms of these Acr proteins. Our findings support the convergent consequence of inhibiting degradation of invading DNA by these Acr proteins, albeit with different modes of interactions with the type I-F CRISPR–Cas system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

SPRUCE Root Tip and Ectomycorrhizal Fungi Colonization Measurements from Ingrowth Cores, 2017

This data set contains root tip and ectomycorrhizal fungi colonization measurements taken from ingrowth cores from the SPRUCE experiment (Hanson et al. 2017) that were deployed during the 2017 growing season (2017-06 to 2017-10-01). This study explored the relationship between warming treatments and fine-root growth. Increased fine-root growth may increase root exudates and accelerate turnover, representing an underlying mechanism for peat decomposition through priming, as exudates provide a labile carbon source to the microbial community. Roots of two tree species were studied: an evergreen conifer Picea mariana (black spruce) and a deciduous conifer Larix laricina (tamarack). Measurements include root tips counts and densities by tree species and the abundance of ectomycorrhizal colonization on root tips. This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

black spruce [Picea mariana]↗
NASA NTRS2001

AVIRIS Reflectance Retrievals: UCSB Users Manual

The following write-up is designed to help students and researchers take Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance data and retrieve surface reflectance. In the event that the software is not available, but a user has access to a reflectance product, this document is designed to provide a better understanding of how AVIRIS reflectance was retrieved. This guide assumes that the reader has both a basic understanding of the UNIX computing environment, and that of spectroscopy. Knowledge of the Interactive Data Language (IDL) and the Environment for Visualizing Images (ENVI) is helpful. This is a working document, and many of the fine details described in the following pages have been previously undocumented. After having read this document the reader should be able to process AVIRIS to reflectance, provided access to all of the code is possible. The AVIRIS radiance data itself is pre-processed at the Jet Propulsion Laboratory (JPL) in Pasadena, California. The first section of this paper describes how to read data from tape and byte-swap the data. Section 2 describes the procedure in preparing support files before running the 'h2o' suite of programs. Section 3 describes the four programs used in the process, h2olut9.f, h2ospl9.f, vlsfit9.f and rfl9.f.

Roberts, Dar A.↗
DOE OSTI2022

Effect of oxidation on the release of multiple metals from industrially polluted sediments and synchrotron-based evidence of Cu–S dynamic association

Disposal operations for industrially polluted sediments are usually accompanied by disturbance and resuspension, which can induce metal remobilization and secondary pollution. Evaluating the risk of metal release under various redox conditions is fundamental for predicting contaminant mobilization and guiding remediation measures. An abandoned oxidation pond, Yanjia Lake, China, was selected as a typical industrially polluted site. Re-suspension experiments were carried out by mixing polluted sediments with lake water under oxic or anoxic conditions, then investigating the effect of oxidation conditions on the release of multiple metals. Metal concentrations and aqueous chemistry in the overlying water were monitored. Synchrotron-based methods, including X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS), were used to characterize oxidation states and coordination conditions of metals in sediments. The release of metals, including Cr, Co, Ni, Cu, Zn, Se, Mo, Sn, Cd, and Pb, was enhanced under oxic vs. anoxic conditions. The XANES analysis revealed that elevated Cr and Zn concentrations under oxic conditions likely resulted from the oxidation of Cr(III) and oxidizing dissolution of ZnS, respectively. K-edge Cu XANES, S XANES, and Cu EXAFS analyses reconstructed the Cu–S association, indicating that S-related oxidation promoted Cu release and Cu–O partly replaced Cu–S in the sediment after a 7-day oxic treatment. The release of most metals was promoted under oxic conditions, resulting from the oxidation of sulfides and metals as indicated by aqueous and synchrotron-based evidence. The risk of secondary pollution is greatly enhanced under oxic conditions, which suggests that measures should be taken to minimize the redox disturbance during sediment remediation. This information can guide the management of sediments in Yanjia Lake and other contaminated sites with similar properties.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Astrometry with Hubble Space Telescope Fine Guidance Sensor number 3: Position-mode stability and precision

We report results from a test exploring the long- and short-term astrometric stability of Hubble Space Telescope Fine Guidance Sensor (FGS) #3. A test field was observed 40 times over 522 days to determine the precision and accuracy of FGS astrometry and to measure the character and magnitude of possible secular scale changes. We examine the astrometric data and the associated guide-star data to determine random errors. These data are also explored to find sources of systematic error. After correcting for some systematic effects we obtain a precision of 0.002 arcsec (2 mas) per observation (RSS of x and y). This is relative astrometry within a central 2.5 arcmin FGS field of view for any orientation. We find that the scale varies over time and confirm the sense of the trend with independent data. From the 40 observation sets we produce a catalog of an astrometry test field containing eight stars whose relative positions are known to an average 0.7 and 0.9 mas in x and y. One reference star has a relative parallax of 3.1 plus or minus 0.5 mas. Finally, we report that eleven observation sets acquired over 387 days produce parallaxes and relative positions with 1-mas precision.

Benedict, G. F.↗
DOE OSTI2020

The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information

Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. High resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding regional climate variability and change, and are critical for supporting societal responses to changing climate.

Gutowski, William↗
DOE OSTI2022

Weighted relaxation for multigrid reduction in time

Current trends in computer architectures now mean that faster computation speed must come primarily from increased concurrency, not faster clock speeds, which are stagnating. Thus, this situation creates bottlenecks for serial algorithms, including the well-known bottleneck for sequential time-integration, where each individual time-value (i.e., time-step) is computed sequentially. One approach to alleviate this and achieve parallelism in time is with multigrid. Here, in this work, we consider multigrid-reduction-in-time (MGRIT), a multilevel method applied to the time dimension that computes multiple time-steps in parallel. Like all multigrid methods, MGRIT relies on the complementary relationship between relaxation on a fine-grid and a correction from the coarse grid to solve the problem. All current MGRIT implementations are based on unweighted-Jacobi relaxation; here we introduce the concept of weighted relaxation to MGRIT. We derive new convergence bounds for weighted relaxation, and use this analysis to guide the selection of relaxation weights. Numerical results then demonstrate that by choosing appropriate non-unitary relaxation weights, one can achieve faster convergence rates and lower iteration counts for MGRIT when compared with unweighted relaxation. In most cases, weighted relaxation yields a 10%–20% saving in iterations, which is significant when using large high-performance computers. For A-stable integration schemes, results also illustrate that under-relaxation can restore convergence in some cases where unweighted relaxation is not convergent.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Designing complex concentrated alloys with quantum machine learning and language modeling

Designing novel complex concentrated alloys (CCAs) is an essential topic in materials science. However, due to the complicated high-dimensional component-property relationship, tuning material properties by researchers’ experience is challenging, even when guided by physical or empirical rules. Here, we adopt quantum computing (QC) technology and machine learning models to provide a proof-of-concept application of QC in physical metallurgy. We propose a quantum support vector machine (QSVM) model to predict single-phase CCAs. We show that fine-tuned quantum kernels with entanglement deliver promising performance, with a maximum accuracy of 89.4%. The QSVM model is then used to identify 1,741 lightweight CCAs jointly with a new text-mining-based method. Meanwhile, we devise a controllable approach to study the effect of noise on model performance and find that the noise level needs to be minimized for high-performance QSVM models. Finally, this study provides a practical and general approach to designing CCAs based on quantum technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

DPO↗
DOE OSTI2024

The crucial role of diagnostics in achieving ignition on the National Ignition Facility (NIF)

Well over 100 diagnostics can operate on the National Ignition Facility (NIF) as a result of several decades of development on NIF, and before that on Nova, OMEGA, and earlier LLNL lasers. A subset of these have guided the approach to achieving ignition on the NIF in 2022 [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129(7), 075001 (2022)]. Achieving ignition on NIF has required many types of experiments with this core set of diagnostics, some constraining known unknowns and some revealing surprises—arguably unknown unknowns. Early design work realized that the extreme precision required for ignition on NIF would require fine-tuning by experiment, that is, measuring and adjusting known unknowns. Many examples are given where the use of the core set of ignition diagnostics in experimental arrangements called platforms demonstrated control of the key theoretical parameters defined as shape, adiabat, velocity, and mix. The direction of the adjustments to input conditions is found either by trend analysis or, in many cases, by observing from the diagnostic data the direction to make an adjustment. In addition, diagnostics have revealed some unexpected or neglected known issues, which degrade performance, or unexpected issues, unknown unknowns. Some of these factors had been previously considered, but underestimated or difficult to calculate at the time. The overall methodology can be described as a variant of Popper's falsifiability philosophy [K. Popper, The Logic of Scientific Discovery (Hutchinson, 1974)]. This paper summarizes the role of ignition diagnostics in terms of falsification or validation of theory or experimental setup as well as uncovering unexpected issues. The journey to ignition started in the seventies with a 1-µm wavelength laser producing disastrous results. Diagnostics have guided us to the recent multi-decadal goal of demonstrating ignition and burn in the laboratory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

SPRUCE Aboveground Vegetation Coverage in Root Ingrowth Core Plots, Marcell Experimental Forest, Minnesota, August 2022

This dataset contains vegetation survey measurements from root ingrowth core plots (Määttä et al. 2025) inside SPRUCE Experiment plots at the Marcell Experimental Forest in northern Minnesota. Vegetation surveys were conducted in 0.25 meter2 plots containing root ingrowth cores on August 8th and 9th, 2022 (2022-08-08 to 2022-08-09). The warming and elevated carbon dioxide (CO2) treatments in the dataset include the full treatment gradient: +0 degrees Celsius (C) (+0 and +500 parts per million (ppm) elevated CO2), +2.25 degrees C (+0 and +500 ppm), +4.5 degrees C (+0 and +500 ppm elevated CO2), +6.75 degrees C (+0 and +500 ppm) and +9 degrees C (+0 and +500 ppm elevated CO2) for both hummocks and hollows. This dataset includes measurements of the height and absolute coverage (%) for each vascular plant and moss species, as well as organic litter and dead overstory vascular plants, and the distance from the grid center to the nearest tree and the species of the nearest tree. These data were used as species-specific aboveground plant metadata for assessing the warming and elevated CO2 response of fine roots across different peatland microtopographical features (hummocks and hollows) and plant functional types (shrub, spruce and larch). This dataset contains one data file in comma separate (.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

ESS-DIVE CSV File Formatting Guidelines Reporting ↗
DOE OSTI2023

Oxidation Driven Thin‐Film Solid‐State Metal Dealloying Forming Bicontinuous Nanostructures

Abstract Solid‐state metal dealloying (SSMD) is a promising method for fabricating nanoscale metallic composites and nanoporous metals across a range of materials. Thin‐film SSMD is particularly attractive due to its ability to create fine features via solid‐state interfacial reactions within a thin‐film geometry, which can be integrated into devices for various applications. This work examines a new dealloying couple, namely the Nb–Al alloy with the dealloying agent Sc, as previously predicted in the machine‐learning (ML) models. Prior ML predictions aimed to guide the design of nanoarchitectured materials through dealloying, relying on intuition‐driven discovery within a large parameter space. However, this work reveals that at the nanoscale, the involvement of oxygen in thin film processing may instead drive the dealloying process, resulting in the formation of bicontinuous nanostructures similar to those formed by metal‐agent dealloying. The phase evolution, as well as chemical and morphological changes, are closely analyzed using a combination of X‐ray absorption spectroscopy, diffraction, and scanning transmission electron microscopy to understand the mechanisms behind nanostructure formation. The findings suggest a potential pathway for utilizing oxygen to drive the formation of bicontinuous metal–metal oxide nanocomposites, paving the way for further development of functional nanoporous materials in diverse fields.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A snapshot of high-entropy alloy processing techniques and their effects on resulting mechanical properties

High-entropy alloys (HEAs) exhibit exceptional strength, corrosion resistance, and thermal stability, making them promising candidates for nuclear, aerospace, and other extreme applications. While most prior work has focused on compositional design, manufacturing techniques themselves can alter microstructure and mechanical properties as dramatically as alloy chemistry. This review compiles and compares the effects of processing routes—including arc melting, induction melting, mechanical alloying with spark plasma sintering, and additive manufacturing—on the structure and properties of HEAs. Quantitative comparisons highlight, for example, that SPS-processed alloys can achieve ∼20–45 % higher yield and tensile strength than arc-melted counterparts, while Bridgman solidification produces nearly single-crystal structures with elongation to failure exceeding 80 %. Additive manufacturing routes such as selective laser melting offer fine microstructures but also introduce anisotropy and porosity, leading to yield strengths spanning 100–600 MPa for the same composition. By synthesizing such results, this review provides actionable insight into how processing routes interact with HEA core effects (high entropy, lattice distortion, sluggish diffusion, and cocktail effect) to determine performance, thereby offering a practical guide for optimizing manufacturing strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).

36 MATERIALS SCIENCE↗
DOE OSTI2025

An engineering perspective on evaluating mechanisms governing ductility in pure molybdenum

The United States lacks a stable domestic supply of 99 mTc, a critical medical imaging isotope generated from 99 Mo. Accelerator-based production using 100 Mo targets (aMo) introduces mechanical concerns due to the ductile-to-brittle transition temperature inherent to refractory metals like Mo. This study evaluates the tensile behavior of aMo targets from 25 to 1000 °C, compared to powder-metallurgy-processed natural Mo (PMo) and cast-and-rolled Mo (RMo), both as-received and after 5 ppm O 2 exposure in flowing He. RMo showed superior ductility (7.3% at 25 °C, 48% at 1000 °C) and strength, attributed to its fine, elongated grains and high geometrically necessary dislocation density. PMo exhibited variable ductility (up to 33%), while aMo remained brittle, with a maximum elongation of 10.4% at 600 °C. EBSD analysis revealed weak texture in PMo and aMo, but high defect density in aMo limited dislocation mobility. This work links processing, microstructure, and deformation mechanisms to guide fabrication of ductile refractory targets.

Hyer, Holden C. [Oak Ridge National Laboratory (OR↗
NASA NTRS1985

An alternate fine guidance sensor for the Space Telescope

Design features and the performance envelope of an alternate fine guidance sensor for the Space Telescope are delineated. A Digicon sensor, which requires focusing of a star image on the sensor, was selected as the alternate for the Koesters prism interferometer originally chosen. Optical elements scan the focal plane and are connected to star selecter servomechanisms. A 5 sq arcsec FOV serves as input, and guide stars have been identified to assure a 0.95 probability that one will always be present. The Digicon will permit pointing by 14.5 visual magnitude stars with errors kept below 0.004 arcsec. Guide star acquisition will be effected in the bang-bang mode with a state feedback in the linear area of the instrument error function. Simulations have demonstrated a satisfactory lock-on to stars down to 19th magnitude.

Griffin, M. D.↗