Harnessing data-driven techniques to discover novel materials for hydrogen storage and generation.
Abstract not provided.
Witman, Matthew↗
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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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Discovering and Transforming Geologic Data Using a Scalable Deep Learning Approach
Conference on Data Analysis (CoDA2023), Santa Fe, NM, March 7-9, 2023
Morkner, Paige↗
NASA's Global Change Master Directory: Discover and Access Earth Science Data Sets, Related Data Services, and Climate Diagnostics
NASA's Global Change Master Directory provides the scientific community with the ability to discover, access, and use Earth science data, data-related services, and climate diagnostics worldwide. The GCMD offers descriptions of Earth science data sets using the Directory Interchange Format (DIF) metadata standard; Earth science related data services are described using the Service Entry Resource Format (SERF); and climate visualizations are described using the Climate Diagnostic (CD) standard. The DIF, SERF and CD standards each capture data attributes used to determine whether a data set, service, or climate visualization is relevant to a user's needs. Metadata fields include: title, summary, science keywords, service keywords, data center, data set citation, personnel, instrument, platform, quality, related URL, temporal and spatial coverage, data resolution and distribution information. In addition, nine valuable sets of controlled vocabularies have been developed to assist users in normalizing the search for data descriptions. An update to the GCMD's search functionality is planned to further capitalize on the controlled vocabularies during database queries. By implementing a dynamic keyword "tree", users will have the ability to search for data sets by combining keywords in new ways. This will allow users to conduct more relevant and efficient database searches to support the free exchange and re-use of Earth science data. http://gcmd.nasa.gov/
Aleman, Alicia↗
Case study of visualizing global user download patterns using Google Earth and NASA World Wind
Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualization tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASAWorld Wind. We illustrate our methods by visualizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the hot spot areas of research. Most importantly, our methods demonstrate an easy way to geovisualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change).
Zong, Ziliang↗
Revival of the Dark Matter Hypothesis for the Galactic Center Gamma-Ray Excess
Statistical evidence has previously implied that the galactic center GeV excess (GCE) originates largely from point sources, and not from annihilating dark matter. Here, we examine the impact of unmodeled source populations on identifying the true origin of the GCE using non-Poissonian template fitting (NPTF) methods. In a proof-of-principle example with simulated data, we discover that unmodeled sources in the Fermi bubbles can lead to a dark matter signal being misattributed to point sources by the NPTF. We discover striking behavior consistent with a mismodeling effect in the real Fermi data, finding that large artificial injected dark matter signals are completely misattributed to point sources. Consequently, we conclude that dark matter may provide a dominant contribution to the GCE after all.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Data-Logger Interface And Test Controller
Data-logger interface and test controller developed to enable automation of tests in conjunction with data-acquisition functions performed by data loggers that have output-switching capabilities. Includes relay logic circuits that remain deenergized until out-of-tolerance condition on any data channel discovered. Designed to be connected to Fluke model 2286A (or equivalent) data-logger system, which features 3 control channels with 6 data inputs per channel. Includes elapsed-time counter that keeps track of power outages.
Burch, Donnie R.↗
Reactor Fuel Isotopics and Code Validation for Nuclear Applications
Experimentally measured isotopic concentrations of well characterized spent nuclear fuel (SNF) samples have been collected and analyzed by previous researchers. These sets of experimental data have been used extensively to validate the accuracy of depletion code predictions for given sets of burnups, initial enrichments, and varying power histories for different reactor types. The purpose of this report is to present the diversity of data in a concise manner and summarize the current accuracy of depletion modeling. All calculations performed for this report were done using the Oak Ridge Isotope GENeration (ORIGEN) code, an internationally used irradiation and decay code solver within the SCALE comprehensive modeling and simulation code. The diversity of data given in this report includes key actinides, stable fission products, and radioactive fission products. In general, when using the current ENDF/B-VII.0 nuclear data libraries in SCALE, the major actinides are predicted to within 5% of the measured values. Large improvements were seen for several of the curium isotopes when using improved cross section data found in evaluated nuclear data file ENDF/B-VII.0 as compared to ENDF/B-V-based results. The impact of the flux spectrum on the plutonium isotope concentrations as a function of burnup was also shown. The general accuracy noted for the actinide samples for reactor types with burnups greater than 5,000 MWd/MTU was not observed for the low-burnup Hanford B samples. More work is needed in understanding these large discrepancies. The stable neodymium and samarium isotopes were predicted to within a few percent of the measured values. Large improvements were seen in prediction for a few of the samarium isotopes when using the ENDF/B-VII.0 libraries compared to results obtained with ENDF/B-V libraries. Very accurate predictions were obtained for 133 Cs and 153 Eu. However, the predicted values for the stable ruthenium and rhodium isotopes varied as much as 10% of the measured values, and 109 Ag was consistently over-predicted by as much as 170%. In general, there is a larger uncertainty for modeling radioactive fission products when compared to either the actinides or the stable fission products in SNF. The relative C/E ratios ranged from a few percent for 137 Cs up to 60% and 100% for 106 Ru and 125 Sb, respectively. Limited or no radioactive fission products data exist in the current data sets for reactor types other than PWRs and BWRs. More work is needed in obtaining a greater diversity of radioactive fission product data. While performing this survey, issues leading to inconsistencies in nuclear fission yield data were discovered that specifically impacted the fission product noble gases. Emphasis was given to this legacy data, and corrective actions were taken as described in this report. After the fission yield data were corrected, the stable xenon and krypton fission products were predicted to within 5% of their measurements. However, preliminary results not explicitly given in this report indicate that the relative C/E ratio for the radioactive isotope 85 Kr varied as much as 10%. Due to the complex migration and the difficulty in measuring noble gases in the fuel, a more thorough investigation is needed to understand how accurately depletion codes can calculate these gas concentrations.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA GIBS and Worldview: Leveraging Visualizations to Improve Data Discovery
NASA's Global Imagery Browse Services (GIBS) leverages scientific and community best practices and standards to provide a scalable, compliant, and authoritative source for NASA Earth Observing System (EOS) Earth science data visualizations. Since 2013, its goal has been to "transform how end users interact and discover [EOS] data through visualizations." Imagery layers within GIBS allow end users to easily and quickly interact with full resolution, pre-generated visualizations of scientific parameters. This interactive discovery approach relies on visual observation and identification of phenomena that are not as simply identified otherwise.
Global Imagery Browse Services↗
THE SECOND FERMI LARGE AREA TELESCOPE CATALOG OF GAMMA-RAY PULSARS
This catalog summarizes 117 high-confidence ≥0.1 GeV gamma-ray pulsar detections using three years of data acquired by the Large Area Telescope (LAT) on the Fermi satellite. Half are neutron stars discovered using LAT data through periodicity searches in gamma-ray and radio data around LAT unassociated source positions. The 117 pulsars are evenly divided into three groups: millisecond pulsars, young radio-loud pulsars, and young radio-quiet pulsars. We characterize the pulse profiles and energy spectra and derive luminosities when distance information exists. Spectral analysis of the off-peak phase intervals indicates probable pulsar wind nebula emission for four pulsars, and off-peak magnetospheric emission for several young and millisecond pulsars. We compare the gamma-ray properties with those in the radio, optical, and X-ray bands. We provide flux limits for pulsars with no observed gamma-ray emission, highlighting a small number of gamma-faint, radio-loud pulsars. The large, varied gamma-ray pulsar sample constrains emission models. Fermi's selection biases complement those of radio surveys, enhancing comparisons with predicted population distributions.
79 ASTRONOMY AND ASTROPHYSICS↗
Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae
To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we, in this study, generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.
59 BASIC BIOLOGICAL SCIENCES↗
IMG-ABC. A knowledge base to fuel discovery of biosynthetic gene clusters and novel secondary metabolites
In the discovery of secondary metabolites, analysis of sequence data is a promising exploration path that remains largely underutilized due to the lack of computational platforms that enable such a systematic approach on a large scale. In this work, we present IMG-ABC (https://img.jgi.doe.gov/abc), an atlas of biosynthetic gene clusters within the Integrated Microbial Genomes (IMG) system, which is aimed at harnessing the power of “big” genomic data for discovering small molecules. IMG-ABC relies on IMG’s comprehensive integrated structural and functional genomic data for the analysis of biosynthetic gene clusters (BCs) and associated secondary metabolites (SMs). SMs and BCs serve as the two main classes of objects in IMG-ABC, each with a rich collection of attributes. A unique feature of IMG-ABC is the incorporation of both experimentally validated and computationally predicted BCs in genomes as well as metagenomes, thus identifying BCs in uncultured populations and rare taxa. We demonstrate the strength of IMG-ABC’s focused integrated analysis tools in enabling the exploration of microbial secondary metabolism on a global scale, through the discovery of phenazine-producing clusters for the first time in lphaproteobacteria . IMG-ABC strives to fill the long-existent void of resources for computational exploration of the secondary metabolism universe; its underlying scalable framework enables traversal of uncovered phylogenetic and chemical structure space, serving as a doorway to a new era in the discovery of novel molecules. IMG-ABC is the largest publicly available database of predicted and experimental biosynthetic gene clusters and the secondary metabolites they produce. The system also includes powerful search and analysis tools that are integrated with IMG’s extensive genomic/metagenomic data and analysis tool kits. As new research on biosynthetic gene clusters and secondary metabolites is published and more genomes are sequenced, IMG-ABC will continue to expand, with the goal of becoming an essential component of any bioinformatic exploration of the secondary metabolism world.
59 BASIC BIOLOGICAL SCIENCES↗
Connecting AmeriFlux to the Globe, Extending the Partnership with Global Flux Network FLUXNET
We propose a project that will extend and expand the activities and contributions of AmeriFlux, to the global stage by working with our international partners to continue operating and expand the functioning of global FLUXNET database. One goal is to produce the next-generation FLUXNET dataset. This will be an integrated dataset on carbon, water and energy fluxes, on meteorological conditions and on the structure and function of the ecosystems; we have to potential to construct a new database, double the size of the La Thuile dataset, with over 2000 site-years of trace gas flux measurements, and supporting meteorological and site meta-data from over 400 sites world-wide. The production of the next FLUXNET database is critical to the activities of this ROSE’s solicitation because these data will drive many of the carbon cycle synthesis activities and the model parameterization and validation activities. A second goal is to build the next-generation, data-system that will house, distribute and query data and discover new information by the broader biogeoscience user community. The third goal is to use these data in synthesis activities that improve our understanding on how climate, weather, ecological, physiological and soil factors govern the exchange of carbon, water and energy between vegetation and the atmosphere at multiple time and space scales. The fourth goal is to act as a coordinator of the project to recruit data, to organize a workshop/conference and to build a community of data sharing and interaction across vast cultural spectrum of participating scientists. The rationale for proposing this work is based on the fact that the vast databases that are produced by flux networks cannot perpetuate and evolve without continued support of the intellectual activities of scientists who produce data, expert in databases, and scientists who use data for modeling. The actuality of a flux network takes human intervention to recruit data from different countries and cultures, to build trust to share data in open-access format, to support data users, and to collaborate. Specifically, we will develop an updated data portal that is easy to navigate and conducive for distributing, visualization, evaluation and discovery of the multiple types of flux and environmental data and information. We will foster collaboration and interactions with our national and international partners and the recruitment of data by: 1) continuing the periodic publication of the newsletter, FLUXLETTER; 2) by hosting an international conference; 3) by coordinating and launching multi-investigator data synthesis activities; and 4) by developing tools that coordinate the development of research papers resulting from the dataset. Data products include raw and gap-filled data at their native time steps (30 minute averages), data products that are integrated on daily and annual time scales and value added products like ecosystem respiration and canopy photosynthesis. Uncertainty analysis and quality control metrics will be produced in coordination with AmeriFlux and ICOS. We will also work with colleagues to expand the scope of FLUXNET beyond carbon dioxide and water vapor, and start archiving the growing methane flux databases that are being collected, world-wide. The interpretation of the flux data relies on good meta-data. We will work with data providers to populate and expand the meta-data archives. We will also work with collaborators to incorporate data from remote sensing products such as MODIS and Landsat, from corresponding climate and weather stations. Another goal is to incorporate new data on canopy structure, with recent LIDAR acquisitions, to populate the data archive with automated soil respiration and understory flux measurements, where available and local climate records for corresponding sites. And, we will collaborate with the TRY database, by merging flux data with plant trait information.
54 ENVIRONMENTAL SCIENCES↗
Considerations of Alloy N Code Extension for Commercial Molten Salt Reactor Development and Deployment
For commercial development and deployment of the molten salt reactor, a structural alloy that provides both strength at high temperature and resistance to very corrosive molten salt environment is required. To meet this requirement, a survey is conducted on domestic and international candidate alloys. Alloy N turns out to be the sole frontrunner in readiness for qualification to enable the desired deployment within an estimated 10 years. A review of the qualification for commercial nuclear applications indicates that Alloy N has met a large portion of the requirements. Gaps in the qualification are also identified. A search for historical data is underway to retrieve information needed for filling the gaps and upgrading the qualification. Scope of the discovered historical data is briefly discussed and strategic planning for research and development pathway is suggested to ensure successful evolution in commercial deployment of the molten salt reactor system.
Ren, Weiju↗
Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here, we describe a software toolbox—called seqNMF—with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.
60 APPLIED LIFE SCIENCES↗
GeoThermalCloud: Machine Learning for Geothermal Resource Exploration
Geothermal is a renewable energy source that can provide reliable and flexible electricity generation for the world. In the past decade, the U.S. Geological Survey's resource assessments, Play Fairway Analyses (PFA), and GeoVision report by the U.S. Department of Energy's Geothermal Technologies Office provided insights on enormous untapped potential for geothermal energy to contribute to the U.S. domestic energy needs. The past studies identified that geothermal resources without surface expression (e.g., blind/hidden hydrothermal systems) comprise a huge potential. These blind systems can significantly increase power generation. But a primary challenge is locating and quantifying these hidden resources, which do not have any thermal manifestations on the surface. PFA has successfully identified some blind systems in the western USA (e.g., specific locations in the Great Basin region within Nevada). However, a comprehensive search for these blind systems can be time-consuming, expensive, and resource-intensive with a low probability of success. Accelerated discovery of these blind resources is needed with growing energy needs and higher chances of exploration success. Recent advances in machine learning (ML) have shown promise in shortening the timeline for this discovery. This paper presents a novel ML-based methodology for geothermal exploration towards PFA applications. Our methodology is provided through our open-source ML framework called GeoThermalCloud \url{https://github.com/SmartTensors/GeoThermalCloud.jl}. GeoThermalCloud uses a series of unsupervised, supervised, and physics-informed ML methods available in SmartTensors AI platform \url{https://github.com/SmartTensors}. Here, the presented analyses are performed using our unsupervised ML algorithm called NMF$k$, which is available in the SmartTensors AI platform. Our ML algorithm facilitates the discovery of new phenomena, hidden patterns, and mechanisms that helps us to make informed decisions. Moreover, the GeoThermalCloud enhances the collected PFA data and discovers signatures representative of geothermal resources. Through GeoThermalCloud, we were able to identify hidden patterns in the geothermal field data needed for the efficient discovery of blind systems. Crucial geothermal signatures often overlooked in traditional PFA are extracted using GeoThermalCloud and analyzed by the subject matter experts to provide ML-enhanced PFA, which is informative for efficient exploration. We applied our ML methodology on various open-source geothermal datasets within the U.S. (some of these are collected by past PFA work), and the results provide valuable insights on resource types within those explored regions. This ML-enhanced workflow makes GeoThermalCloud attractive for the geothermal community to improve existing datasets and extract valuable information often unnoticed during geothermal exploration.
machine learning (ML), geothermal energy↗
Update on Parametric Cost Models for Space Telescopes
Since the June 2010 Astronomy Conference, an independent review of our cost data base discovered some inaccuracies and inconsistencies which can modify our previously reported results. This paper will review changes to the data base, our confidence in those changes and their effect on various parametric cost models
Stahl. H. Philip↗
Discovering System Health Anomalies Using Data Mining Techniques
We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.
Sriastava, Ashok, N.↗
Multimetallic Layered Composites (MMLCs) for Rapid, Economical Advanced Reactor Deployment (Final Report)
This project focused on the development of multi-metallic layered composites (MMLCs) for advanced fission reactor technologies. There are many instances where one alloy or material simply cannot meet all the demands thrown at it by a reactor system, or cannot allow it to perform as strongly as one would like. Instead of focusing all our effort on developing one perfect alloy, we seek to leverage the design principle of “separation of functionality,” used in many other arenas in design, to boost performance beyond single alloys alone. One illustrative example shows the power of this approach for molten salt-cooled reactors: A three meter tall, three meter diameter reactor vessel made of Incoloy 800 was quoted at $\$$500k in 2018. A Hastelloy N vessel was quoted at $\$$5M. An MMLC vessel, in which a layer of Hastelloy N would be weld-overlaid onto Incoloy 800, was quoted at $\$$700k, and it would achieve the same performance. The potential economic gains of leveraging this approach are therefore substantial. At a minimum, each MMLC would contain one core structural layer and one coolant-facing corrosion-resistant layer. Sometimes, MMLCs required buffer layers, as the structural and corrosion-resistant layers were metallurgically incompatible. In other words, they didn’t always play nice, thus separating layers compatible with both functioned as intermediaries to keep the composite together. However, in doing so we inevitably produce new interfaces, where new issues can arise. Therefore, this project focused on what happens at these interfaces from a combination of high temperatures, irradiation, corrosion, and time. After all, a reactor makes money when it is operating, and outages of any kind erode its economic viability. First, we set out to experimentally prove that MMLCs for at least two advanced reactor systems can be made, today, in US domestic facilities. In this respect we were successful – one MMLC (a Ni-201/Incoloy 800H composite) was successfully made and drawn into two-inch coolant piping. Others were attempted, though new issues relating to cracking in vanadium layers for one and radiation damage performance of the corrosion-resistant layer in another prevented us from moving further in those specific arenas – these are engineering problems which deserve continued focus after this project. Additional experimental work focused on long-term corrosion testing of the outermost layers of the salt-cooled and liquid lead-cooled MMLC concepts, which would then be fed into predictions of how long the MMLCs could last. Next, computational (thermodynamics and atomistic) simulation studies studied how much we expect the interfaces to “blend,” due to the mixing action of neutron irradiation. This eats into both the margin for the structural layer of each MMLC, as dilution from the corrosion-resistant layer into the structural layer would decrease the total load-bearing capacity of an MMLC of finite size. On the other hand, dilution of the corrosion-resistant layer into the structural layer further reduced the margin of corrodible material, reducing the lifetime of the MMLC or necessitating extra thickness to be imparted to the MMLC to meet its functional requirements. Work here focused on irradiation-induced segregation to predict new phases which may embrittle the MMLCs, as well as quantifying irradiation-induced mixing at each interface. The results showed that mixing is expected, but it is both steady and therefore predictable, and not lifetime-limiting for most MMLC concepts – it simply has to be accounted for in calculations of reactor performance when utilizing an MMLC. Then, full-core simulations using the experimentally-derived corrosion data, the computationally discovered irradiation-induced mixing data (partially validated by experiment), and existing, benchmarked core designs for large and small sized reactor concepts (one salt-cooled, one lead-cooled) were conducted to quantify any expansion of reactor operating envelopes achieved by utilizing these MMLCs. This new framework, called REX (Reactor Envelope Expansion), incorporates a combination of core neutronics, thermal hydraulics, and the material performance data derived from this project to see how using an MMLC expands advanced fission reactor operating envelopes. It was discovered that in some cases, MMLC utilization does indeed increase the maximum operating temperatures and cycle lengths of reactor concepts, while in other cases it does not. Finally, our tech-to-market (T2M) strategy was not necessarily to create specific embodiments of MMLCs for immediate sale (because getting into the nuclear market is incredibly slow and laden with regulation, this is a long-term goal), but rather immediate stimulation of US industry using the design approach of MMLCs derived from this project. In this respect we were successful, as one of the PhD students funded on this project co-founded Allium Engineering, Inc., which created a stainless steel / low-alloy steel MMLC to function as chloride corrosion-resistant rebar for embedding into concrete structures. Allium Engineering continues to be successful, having recently opened their first factory as of this writing.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗