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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The effect of Zr on precipitation in oxide dispersion strengthened FeCrAl alloys

Oxide dispersion strengthened (ODS) FeCrAl alloys are promising candidate materials for advanced nuclear reactor applications requiring high-temperature strength, corrosion resistance, and irradiation tolerance. As these alloys have increased in compositional complexity through attempts to use highly reactive elements such as Zr to refine particle sizes and optimize nanoprecipitate dispersion characteristics, much debate has ensued as to the effects of these alloying element additions on alloy properties. In an attempt to reconcile differences in nanoprecipitate distributions reported in the literature over the past decade, a detailed investigation of a recently developed ODS FeCrAl alloy with nominal composition Fe–10Cr-6.1Al-0.3Zr+0.3Y 2 O 3 is presented here using a combination of atom probe tomography (APT), scanning/transmission electron microscopy (S/TEM), and computational thermodynamics modeling. It is illustrated that based on the amount of Zr available in the lattice, Zr competes with Al and Cr to form carbides and nitrides as opposed to oxygen-rich precipitates. This alloy system has a high number density (>10 23 m -3 ) of ~2–4 nm diameter (Y,Al,O)-rich nanoprecipitates, but it is shown that due to the compositional spread and unknown partitioning of Al between the matrix and precipitates, significant challenges still exist for quantifying the exact compositions of these precipitates using APT. However, the noted compositional spread is supported by identified complex oxides yttrium aluminum monoclinic (YAM) and yttrium aluminum garnet (YAG) using S/TEM. As a result of these findings, researchers developing ODS FeCrAl with reactive element additions must pay careful attention to C and N impurities when optimizing reactive element additions.

electron microscopy↗
DOE OSTI2022

X-ray absorption spectra of f-element complexes: insight from relativistic multiconfigurational wavefunction theory

X-ray absorption near edge structure (XANES) spectroscopy, coupled with ab initio calculations, has emerged as the state-of-the-art tool for elucidating the metal-ligand bonding in f-element complexes. Furthermore, this highlight presents recent efforts in calculating XANES spectra of lanthanide and actinide compounds with relativistic multiconfiguration wavefunction approaches that account for differences in donation bonding in the ground state (GS) versus a core-excited state (ES), multiplet effects, and spin-orbit-coupling. With the GS and ES wavefunctions available, including spin-orbit effects, an arsenal of chemical bonding tools that are popular among chemists can be applied to rationalize the observed intensities in terms of covalent bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Review of Experimental Data for Validating Computer Codes Used in Shielding Calculations for Spent Fuel Storage and Transportation Systems

This report presents a review of available radiochemical assay data and shielding benchmarks applicable to spent nuclear fuel (SNF) shielding calculations. The relevant information reviewed herein includes the Spent Fuel Composition (SFCOMPO) database, the Shielding Integral Benchmark Archive and Database (SINBAD), the International Handbook of Evaluated Criticality Safety Benchmark Experiments, and published measurements of external dose rates of casks loaded with SNF. The relevant experimental data identified in this report may be used to support verification and validation of computer codes used in SNF cask/transport shielding applications, as well as development of calculation uncertainties. It should be noted that a relatively small subset of the identified experimental data (e.g., criticality alarm experiments) is available in a standard format established by the international community participating in experimental isotopic and shielding data evaluations. An effort of the SFCOMPO Technical Review Group (TRG) is underway to publish first isotopic evaluations of individual assay data using a standard data evaluation format. The SINBAD TRG has recently initiated benchmark evaluations and modernization of the database. Therefore, more relevant information is expected in the future that will enable users to select quality experimental data in depletion code and shielding code validations for SNF applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

Abstract. Identifying, detecting, and localizing extreme weather events is a crucial first step in understanding how they may vary under different climate change scenarios. Pattern recognition tasks such as classification, object detection, and segmentation (i.e., pixel-level classification) have remained challenging problems in the weather and climate sciences. While there exist many empirical heuristics for detecting extreme events, the disparities between the output of these different methods even for a single event are large and often difficult to reconcile. Given the success of deep learning (DL) in tackling similar problems in computer vision, we advocate a DL-based approach. DL, however, works best in the context of supervised learning – when labeled datasets are readily available. Reliable labeled training data for extreme weather and climate events is scarce. We create “ClimateNet” – an open, community-sourced human-expert-labeled curated dataset that captures tropical cyclones (TCs) and atmospheric rivers (ARs) in high-resolution climate model output from a simulation of a recent historical period. We use the curated ClimateNet dataset to train a state-of-the-art DL model for pixel-level identification – i.e., segmentation – of TCs and ARs. We then apply the trained DL model to historical and climate change scenarios simulated by the Community Atmospheric Model (CAM5.1) and show that the DL model accurately segments the data into TCs, ARs, or “the background” at a pixel level. Further, we show how the segmentation results can be used to conduct spatially and temporally precise analytics by quantifying distributions of extreme precipitation conditioned on event types (TC or AR) at regional scales. The key contribution of this work is that it paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data using a curated expert-labeled dataset – ClimateNet. ClimateNet and the DL-based segmentation method provide several unique capabilities: (i) they can be used to calculate a variety of TC and AR statistics at a fine-grained level; (ii) they can be applied to different climate scenarios and different datasets without tuning as they do not rely on threshold conditions; and (iii) the proposed DL method is suitable for rapidly analyzing large amounts of climate model output. While our study has been conducted for two important extreme weather patterns (TCs and ARs) in simulation datasets, we believe that this methodology can be applied to a much broader class of patterns and applied to observational and reanalysis data products via transfer learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

To Dim or Not to Dim: Why Is That Still a Difficult Question?

LED lighting is becoming widely adopted and is displacing most traditional lighting technologies. However, the traditional methods used to control light sources have not been similarly displaced. In an effort to address some of the long-standing weaknesses of one of the most popular control methods, the National Electrical Manufacturers Association (NEMA) recently released the ANSI C137.1-2019 standard for 0-10V control of LED drivers and fluorescent ballasts. The new standard builds upon previous specifications by providing requirements for some aspects of dimming-curve shape and endpoints. In order to characterize the performance variation found in market-available LED drivers, and to explore the potential impact of ANSI C137.1, Pacific Northwest National Laboratory’s Connected Lighting Test Bed was used to evaluate the response to 0-10V controls signals of 21 different LED outdoor cobrahead luminaires (typically used for street-lighting applications) that claimed dimmability via a 0-10V interface. The results provide insights about how ANSI C137.1 will affect the performance variation currently found among such products.

0-10V, LED dimming, interoperability↗
DOE OSTI2024

Introduction and Status of Fermilab's ACORN Project

Modernizing the Fermilab accelerator control system is essential to future operations of the laboratory's accelerator complex. The existing control system has evolved over four decades and uses hardware that is no longer available and software that uses obsolete frameworks. The Accelerator Controls Operations Research Network (ACORN) Project will modernize the control system and replace end-of-life power supplies to enable future accelerator complex operations with megawatt particle beams. An overview of the ACORN Project and a summary of recent research and development activities will be presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

MARLOWE: An Untargeted Proteomics, Statistical Approach to Taxonomic Classification for Forensics

General proteomics research for fundamental science typically addresses laboratory- or patient-derived samples of known origin and composition. However, in a few research areas, such as environmental proteomics, clinical identification of infectious organisms, archeology, art/cultural history, and forensics, attributing the origin of a protein-containing sample to the organisms that produced it is a central focus. A small number of groups have approached this problem and developed software tools for taxonomic characterization and/or identification using bottom-up proteomics. Most such tools identify peptides via database search, and many rely on organism-specific peptides as markers. Our group recently introduced MARLOWE, a software tool for taxonomic characterization of unknown samples based on de novo peptide identification and signal-erosion-resistant strong peptides, which are shared peptides distributed in a taxonomy-dependent manner. In the current work, we further characterize the utility of MARLOWE using publicly available proteomics data from forensically-relevant samples. MARLOWE characterizes samples based on their protein profile, and returns ranked organism lists of potential contributors and taxonomic scores based on shared strong peptides between organisms. Overall, the correct characterization rate ranges between 44 and 100%, depending on the sample type and data acquisition parameters (with lower numbers associated with lower-quality data sets). MARLOWE demonstrates successful characterization of true contributors and close relatives, and provides sufficient specificity to distinguish certain microbial species. MARLOWE demonstrates its ability to provide insight into potential taxonomic sources for a wide range of sample types without prior assumptions about sample contents. As a result, this approach can find utility in forensic science and also broadly in bioanalytical applications that utilize proteomics approaches for taxonomic characterization.

Bacteria↗
DOE OSTI2022

Soil viruses: Understudied agents of soil ecology

Over the past couple decades there has been intense exploration of the soil microbiome using modern sequencing and bioinformatics approaches. These studies have revealed a vast diversity of microorganisms across a variety of habitats (Thompson et al., 2017) and how differences in the environment, for example with climate change, influence the soil microbial community composition (Jansson and Hofmockel, 2020). The focus of these studies has primarily been on microbial cellular organisms, including bacteria, archaea and fungi. By contrast, in depth study of soil viruses has been largely neglected, until recently. There are several reasons that soil viruses are less studied, including difficulties with extraction of viruses from soil particles, difficulties with classification of soil viruses based on microscopic imaging, and reliance on the small proportion of microbial hosts that can be cultivated. Now that comprehensive soil metagenomes are available the current trend has been to survey the metagenomes for viral sequences. This approach has revealed that soil viruses are incredibly abundant, highly diverse, and largely uncharacterized (Paez-Espino et al., 2016). This so-called ‘viral dark matter’ presents an incredible research opportunity for the future. Here I will highlight some of these potential research avenues based on what is known to date.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Direct detection system for full-field nanoscale X-ray diffraction-contrast imaging

Recent developments in X-ray science provide methods to probe deeply embedded mesoscale grain structures and spatially resolve them using dark field X-ray microscopy (DFXM). Extending this technique to investigate weak diffraction signals such as magnetic systems, quantum materials and thin films prove challenging due to available detection methods and incident X-ray flux at the sample. We present a direct detection method developed in conjunction with KAImaging which focuses on DFXM studies in the hard X-ray range of 10s of keV and above capable of approaching nanoscale resolution. Additionally, we compare this direct detection scheme with routinely used scintillator-based optical detection and achieve an order of magnitude improvement in exposure times allowing for imaging of weakly diffracting ordered systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Task 12 PV Sustainability - Status of PV Module Recycling in Selected IEA PVPS Task 12 Countries

Photovoltaic (PV) deployment has accelerated in recent years compared to projections in the early 2010s. This means that PV end of life (EOL) waste streams will also increase at a higher pace than anticipated. To meet and optimise PV EOL management, appropriate regulatory and technological approaches must be implemented in the near term, ensuring that available options are adapted to the conditions of each country or region. This report aims to review the current regulatory and industrial landscape for selected countries belonging to the International Energy Agency's PV Power Systems technology collaboration programme, to assess status of PV EOL management, allow for comparison and cross-fertilization, and establish a foundation for future tracking of progress. Although volumes of EOL PV modules are still small, EOL PV is treated and recycled in a proper manner in the countries and regions that have EOL regulations in place. However, the current low volumes, limited available recycling technologies, logistics challenges, and undeveloped markets for recovered materials result in a high-cost, low-revenue scenario for PV module recycling globally. Nevertheless, the implementation of PV EOL regulations in more countries and R&D investment in PV recycling is expected to accelerate further improvements to meet future demand and to achieve high-value, low-cost recycling. We hope this report contributes to understanding the global status of PV recycling and to accelerating its development as a promising option for the proper EOL management of PV modules in the coming decades.

14 SOLAR ENERGY↗
DOE OSTI2025

Quantum fisher information reveals UV-IR mixing in the strange metal

The density-density response in optimally doped Bi 2 Sr 2 CaCu 2 O 8+$x$ has recently been shown to exhibit conformal symmetry. Using, the experimentally inferred conformal dynamic susceptibility, we compute the resultant quantum Fisher information (QFI), a witness to multi-partite entanglement. For a Fermi liquid, we find that the QFI grows quadratically as the temperature increases, consistent then with the phase space available for scattering in the standard theory of metals. By contrast, the QFI in a strange metal increases as a power law at as the temperature decreases, but ultimately extrapolates to a constant at T=0. The constant is of the form, ω$^{2Δ}_g$, where Δ is the conformal dimension and ω g is the UV cutoff which is on the order of the pseudogap. As this constant depends on both UV and IR properties, it illustrates that multipartite entanglement in a strange metal exhibits UV-IR mixing, a benchmark feature of doped Mott insulators as exemplified by dynamical spectral weight transfer. We conclude with a discussion of the implication of our results for low-energy reductions of the Hubbard model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A global database of soil microbial phospholipid fatty acids and enzyme activities

Abstract Soil microbes drive ecosystem function and play a critical role in how ecosystems respond to global change. Research surrounding soil microbial communities has rapidly increased in recent decades, and substantial data relating to phospholipid fatty acids (PLFAs) and potential enzyme activity have been collected and analysed. However, studies have mostly been restricted to local and regional scales, and their accuracy and usefulness are limited by the extent of accessible data. Here we aim to improve data availability by collating a global database of soil PLFA and potential enzyme activity measurements from 12,258 georeferenced samples located across all continents, 5.1% of which have not previously been published. The database contains data relating to 113 PLFAs and 26 enzyme activities, and includes metadata such as sampling date, sample depth, and soil pH, total carbon, and total nitrogen. This database will help researchers in conducting both global- and local-scale studies to better understand soil microbial biomass and function.

Science & Technology - Other Topics↗
DOE OSTI2021

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB

Abstract Motivation Membrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task. Results We present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D. Availability and implementation Annotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sea Level Rise in the CESM Large Ensemble: The Role of Individual Climate Forcings and Consequences for the Coming Decades

The emergence of a spatial pattern in the externally forced response (FR) of dynamic sea level (DSL) during the altimeter era has recently been demonstrated using climate models but our understanding of its initial emergence, drivers, and implications for the future is poor. Here the anthropogenic forcings of the DSL pattern are explored using the Community Earth System Model Large Ensemble (CESM-LE) and Single-Forcing Large Ensemble, a newly available set of simulations where values of individual forcing agents remain fixed at 1920 levels, allowing for an estimation of their effects. Statistically significant contributions to the DSL FR are identified for greenhouse gases (GHGs) and industrial aerosols (AERs), with particularly strong contributions resulting from AERs in the mid-twentieth century and GHGs in the late twentieth and twenty-first century. Secondary, but important, contributions are identified for biomass burning aerosols in the equatorial Atlantic Ocean in the mid-twentieth century, and for stratospheric ozone in the Southern Ocean during the late twentieth century. Key to understanding regional DSL patterns are ocean heat content and salinity anomalies, which are driven by surface heat and freshwater fluxes, ocean dynamics, and the spatial structure of seawater thermal expansivity. Finally, potential implications for the interpretation of DSL during the satellite era and the longer records from tide gauges are suggested as a topic for future research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Electrothermal Micromirror Adaptive Illumination

As part of the Cyclotron Road program, Helux Lighting aimed to develop a highly adaptive illumination source based on microelectromechanical system (MEMS) technology and next-generation solid state lighting. Solid state lighting has caused a paradigm shift in the industry. Developing systems that put light only where it is needed can reduce energy consumption and have an impact on the well-being of the population by eliminating glare and wasted light. Full control could also reduce the disruption of sensitive ecosystems caused by light pollution. The most advanced fixtures available today are based on mechanical components that are bulky and expensive. The track light is the only inexpensive lighting system that permits redirection and, in some cases, spot size by motorized optical components. The most advanced light fixtures today are composed of a strategically arranged array of LEDs. More recent developments in the research community include dynamic liquid crystal lenses but fail to realize simultaneous dynamic steering and spot size. In this project, Helux worked on the capability development of their controllable micromirror combined with state-of-the-art solid state lighting to electronically adjusting light position, brightness, and illumination area.

42 ENGINEERING↗
DOE OSTI2021

Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The project is motivated by the challenges, risks, and costs associated with geothermal exploration and production. Many processes and parameters impacting geothermal conditions are poorly understood. Diverse datasets are available to help characterize subsurface geothermal conditions (public and proprietary; satellite, airborne surveys, vegetation/water sampling, geological, geophysical, etc.). Yet, it is not clear how to properly leverage these datasets for geothermal exploration due to an incomplete understanding of how physical processes impacting subsurface geothermal conditions are represented in these observations. Recent advancements in machine learning (ML) provide great promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. Our goals and work under Phases 1 and 2 (as proposed) of this project address all these needs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Titanium and Iron in the Cassiopeia A Supernova Remnant

Mixing above the proto-neutron star is believed to play an important role in the supernova engine, and this mixing results in a supernova explosion with asymmetries. Elements produced in the innermost ejecta, e.g., 56 Ni and 44 Ti, provide a clean probe of this engine. The production of 44Ti is particularly sensitive to the exact production pathway and, by understanding the available pathways, we can use 44 Ti to probe the supernova engine. Using thermodynamic trajectories from a three-dimensional supernova explosion model, we review the production of these elements and the structures expected to form under the "convective-engine" paradigm behind supernovae. Overall, we compare our results to recent X-ray and γ-ray observations of the Cassiopeia A supernova remnant.

79 ASTRONOMY AND ASTROPHYSICS↗