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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada

Mineral abundance maps of 18 minerals were made of the Cuprite Mining District using 1990 AVIRIS data and the Multiple Spectral Feature Mapping Algorithm (MSFMA) as discussed in Clark et al. This technique uses least-squares fitting between a scaled laboratory reference spectrum and ground calibrated AVIRIS data for each pixel. Multiple spectral features can be fitted for each mineral and an unlimited number of minerals can be mapped simultaneously. Quality of fit and depth from continuum numbers for each mineral are calculated for each pixel and the results displayed as a multicolor image.

Swayze, Gregg↗
NASA NTRS1995

The NAS Computational Aerosciences Archive

In order to further the state-of-the-art in computational aerosciences (CAS) technology, researchers must be able to gather and understand existing work in the field. One aspect of this information gathering is studying published work available in scientific journals and conference proceedings. However, current scientific publications are very limited in the type and amount of information that they can disseminate. Information is typically restricted to text, a few images, and a bibliography list. Additional information that might be useful to the researcher, such as additional visual results, referenced papers, and datasets, are not available. New forms of electronic publication, such as the World Wide Web (WWW), limit publication size only by available disk space and data transmission bandwidth, both of which are improving rapidly. The Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center is in the process of creating an archive of CAS information on the WWW. This archive will be based on the large amount of information produced by researchers associated with the NAS facility. The archive will contain technical summaries and reports of research performed on NAS supercomputers, visual results (images, animations, visualization system scripts), datasets, and any other supporting meta-information. This information will be available via the WWW through the NAS homepage, located at http://www.nas.nasa.gov/, fully indexed for searching. The main components of the archive are technical summaries and reports, visual results, and datasets. Technical summaries are gathered every year by researchers who have been allotted resources on NAS supercomputers. These summaries, together with supporting visual results and references, are browsable by interested researchers. Referenced papers made available by researchers can be accessed through hypertext links. Technical reports are in-depth accounts of tools and applications research projects performed by NAS staff members and collaborators. Visual results, which may be available in the form of images, animations, and/or visualization scripts, are generated by researchers with respect to a certain research project, depicting dataset features that were determined important by the investigating researcher. For example, script files for visualization systems (e.g. FAST, PLOT3D, AVS) are provided to create visualizations on the user's local workstation to elucidate the key points of the numerical study. Users can then interact with the data starting where the investigator left off. Datasets are intended to give researchers an opportunity to understand previous work, 'mine' solutions for new information (for example, have you ever read a paper thinking "I wonder what the helicity density looks like?"), compare new techniques with older results, collaborate with remote colleagues, and perform validation. Supporting meta-information associated with the research projects is also important to provide additional context for research projects. This may include information such as the software used in the simulation (e.g. grid generators, flow solvers, visualization). In addition to serving the CAS research community, the information archive will also be helpful to students, visualization system developers and researchers, and management. Students (of any age) can use the data to study fluid dynamics, compare results from different flow solvers, learn about meshing techniques, etc., leading to better informed individuals. For these users it is particularly important that visualization be integrated into dataset archives. Visualization researchers can use dataset archives to test algorithms and techniques, leading to better visualization systems, Management can use the data to figure what is really going on behind the viewgraphs. All users will benefit from fast, easy, and convenient access to CFD datasets. The CAS information archive hopes to serve as a useful resource to those interested in computational sciences. At present, only information that may be distributed internationally is made available via the archive. Studies are underway to determine security requirements and solutions to make additional information available. By providing access to the archive via the WWW, the process of information gathering can be more productive and fruitful due to ease of access and ability to manage many different types of information. As the archive grows, additional resources from outside NAS will be added, providing a dynamic source of research results.

Miceli, Kristina D.↗
DOE OSTI2025

Energy-resolved neutron imaging and diffraction including grain orientation mapping using event camera technology

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Application of multispectral scanner data to the study of an abandoned surface coal mine

The utility of aircraft multispectral scanner data for describing the land cover features of an abandoned contour-mined coal mine is considered. The data were obtained with an 11 band multispectral scanner at an altitude of 1.2 kilometers. Supervised, maximum-likelihood statistical classifications of the data were made to establish land-cover classes and also to describe in more detail the barren surface features as they may pertain to the reclamation or restoration of the area. The scanner data for the surface-water areas were studied to establish the variability and range of the spectral signatures. Both day and night thermal images of the area are presented. The results of the study show that a high degree of statistical separation can be obtained from the multispectral scanner data for the various land-cover features.

Spisz, E. W.↗
DOE OSTI2018

Health intelligence: how artificial intelligence transforms population and personalized health

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering innovations in medical devices have prompted demands for more comprehensive and coherent strategies to address the most fundamental questions in health care and medicine. Theory, methods, and models from artificial intelligence (AI) are changing the health care landscape in clinical and community settings and have already shown promising results in multiple applications in healthcare including, integrated health information systems, patient education, geocoding health data, social media analytics, epidemic and syndromic surveillance, predictive modeling and decision support, mobile health, and medical imaging (e.g. radiology and retinal image analyses). Health intelligence uses tools and methods from artificial intelligence and data science to provide better insights, reduce waste and wait time, and increase speed, service efficiencies, level of accuracy, and productivity in health care and medicine.

Shaban-Nejad, Arash↗
DOE OSTI2016

Imaging via complete cantilever dynamic detection: General dynamic mode imaging and spectroscopy in scanning probe microscopy

We develop and implement a multifrequency spectroscopy and spectroscopic imaging mode, referred to as general dynamic mode (GDM), that captures the complete spatially- and stimulus dependent information on nonlinear cantilever dynamics in scanning probe microscopy (SPM). GDM acquires the cantilever response including harmonics and mode mixing products across the entire broadband cantilever spectrum as a function of excitation frequency. GDM spectra substitute the classical measurements in SPM, e.g. amplitude and phase in lock-in detection. Here, GDM is used to investigate the response of a purely capacitively driven cantilever. We use information theory techniques to mine the data and verify the findings with governing equations and classical lock-in based approaches. We explore the dependence of the cantilever dynamics on the tip–sample distance, AC and DC driving bias. This approach can be applied to investigate the dynamic behavior of other systems within and beyond dynamic SPM. In conclusion, GDM is expected to be useful for separating the contribution of different physical phenomena in the cantilever response and understanding the role of cantilever dynamics in dynamic AFM techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗
NASA NTRS2004

A Simple, Scalable, Script-based Science Processor

The production of Earth Science data from orbiting spacecraft is an activity that takes place 24 hours a day, 7 days a week. At the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC), this results in as many as 16,000 program executions each day, far too many to be run by human operators. In fact, when the Moderate Resolution Imaging Spectroradiometer (MODIS) was launched aboard the Terra spacecraft in 1999, the automated commercial system for running science processing was able to manage no more than 4,000 executions per day. Consequently, the GES DAAC developed a lightweight system based on the popular Per1 scripting language, named the Simple, Scalable, Script-based Science Processor (S4P). S4P automates science processing, allowing operators to focus on the rare problems occurring from anomalies in data or algorithms. S4P has been reused in several systems ranging from routine processing of MODIS data to data mining and is publicly available from NASA.

Lynnes, Christopher↗
DOE OSTI2022

A High-Throughput Computing Infrastructure to Generate Custom, Open Community Geothermal Datasets

The most significant challenge facing geothermal research, development, and deployment is a lack of comprehensive datasets describing the geological and economical properties of North America. Automated knowledge base construction, the process of designing algorithms to analyze text and images to programmatically build new datasets, is one possible solution to this problem. The xDD library of full-text scientific articles (https://xdd.wisc.edu) is one of the largest collections of open and controlled-access scientific documents available for knowledge base construction in the world, but it has been underutilized by experts in geothermal research. The xDD development team attributed the lack of engagement by software developers and geothermal researchers to two perceived shortcomings of the system. First, the workflow for obtaining data from xDD for local development and testing of data mining applications was unnecessarily abstruse and required significant manual intervention by xDD systems administrators. Second, although xDD already held articles from a broad cross-section of scientific literature with an emphasis on the geosciences, it did not have an explicit set of geothermal research documents that could serve as the nucleus of a geothermal data mining application. To address these issues, the Automated Data Extraction PlaTform (ADEPT) was proposed to extend the data distribution capabilities of the xDD system. The ADEPT extension added the following four key features to xDD: 1) integration of National Geothermal Data System (NGDS) documents into the xDD library to provide an explicitly geothermally-themed collection; 2) improved RESTful (i.e., https-protocol driven) web services for external partners to access xDD data for machine learning application development; 3) a web platform for end-users and xDD administrators to coordinate the development of data mining applications from the initial step of browsing available documents to the final stage of deploying a production-quality machine learning application on high-throughput computing infrastructure; and 4) the development of demonstration data mining applications to illustrate the new workflow to potential collaborators. A total of 21,674 geothermal documents from NGDS were fully ingested into the xDD library and the associated metadata is publicly available through the xDD web services; furthermore, the ADEPT web platform is now publicly accessible and fully live at https://xdd.wisc.edu/adept/.

15 GEOTHERMAL ENERGY↗
NASA NTRS1974

Relevance of ERTS-1 to the State of Ohio

The author has identified the following significant results. During the first year of project effort the ability of ERTS-1 imagery to be used for mapping and inventorying strip-mined areas in south eastern Ohio, the potential of using ERTS-1 imagery in water quality and coastal zone management in the Lake Erie region, and the extent that ERTS-1 imagery could contribute to localized (metropolitan/urban), multicounty, and overall state land use needs were experimentally demonstrated and reported as significant project results. Significant research accomplishments were achieved in the technological development of manual and computerized methods to extract multi-feature information as well as singular feature information from ERTS-1 data as is exemplified by the forestry transparency overlay. Fabrication of an image transfer device to superimpose ERTS-1 data onto existing maps and other data sources was also a significant analytical accomplishment.

Sweet, D. C.↗
DOE OSTI2026

Monte Carlo simulation methodology for characterizing the energy response function and resolving power of a digital Flat Panel Detector (FPD) assembly

The use of high-resolution, digital flat panel detectors (FPDs) makes quantitative assessments of radiographic and 3D tomographic representations feasible. However, the energy response function and spatial resolving power of the detector must be known. Here, we present a methodology for characterizing an FPD assembly using Monte Carlo simulations. The detector assembly modeled here was fielded in the 3-Ring Flash X-ray Imaging System designed and built at Lawrence Livermore National Laboratory (LLNL), which employs fifteen Carestream ® HPX-DR3543 FPDs enclosed in protective housings. Detailed Monte Carlo (MCNP6) simulations of an as-fielded detector assembly were performed to estimate its energy response function in terms of analog-to-digital unit (ADU) “counts” per pixel per incident x-ray as a function of energy up to ∼ 500 keV and its theoretical (idealized) resolving power. The practical resolving power of the FPD assembly in a realistic scenario was then estimated by simulating radiographic images of a variant of the United Kingdom (U.K.) “Kaleidoscope” resolution target.

Physics↗
DOE OSTI2013

Mr. Scan: extreme scale density-based clustering using a tree-based network of GPGPU nodes, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.

Computer Science↗
DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗
DOE OSTI2016

Predicting plant attractiveness to pollinators with passive crowdsourcing

Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R 2 of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Building a Bilingual Google Earth Engine Dashboard to Increase Accessibility to Long-term Time Series Remote Sensing Data for Monitoring Saline System Changes in Chile’s Atacama Desert

Saline systems, consisting of salt flats, ponds, and marshes, provide vital water resources to wildlife and communities in northern Chile’s Atacama Desert, one of the driest regions in the world. Mining is extensive in the Atacama, which contains 30% of the world’s lithium reserves and is abundant in potassium and boron. The groundwater that feeds into salt marshes and ponds is extracted in large volumes for mining operations, limiting the availability of water for ecosystems. However, identifying long-term and large-scale environmental impacts from local lithium mining on the saline systems is limited by region inaccessibility and terrain variability. Open access satellite imagery and cloud computing technology has made studying Atacama saline systems feasible and allowed for collaboration across different agencies and countries. The NASA DEVELOP Program partnered with Chile’s la Universidad de La Serena and Servicio Nacional de Geología y Minería (SERNAGEOMIN) to create the Saline Analysis Tool (SalT) in Google Earth Engine (GEE). SalT is used to analyze the extent and distribution of remote saline systems in the Atacama from 1986 to the present day. The tool filters Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) data from GEE’s data catalog and creates a single composite image per year for analysis. Additional output analyses include land cover classification, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) raster images that can be displayed on the map interface or exported. The tool can also generate time-lapse videos and charts displaying NDVI, NDWI, and land cover over time. A key feature of the tool is the use of a bilingual graphical user interface to make analysis accessible and customizable to different users’ needs—SalT provides options to select an analysis area, analysis time period, and outputs to display or export. The tool also incorporates new Earth observations as they are added to GEE’s catalog. The ability to easily visualize and analyze long-term remote sensing imagery will enable SERNAGEOMIN and la Universidad de la Serena to continually monitor changes in these saline systems and inform future land management policy.

NASA DEVELOP↗
NASA NTRS2022

Keweenaw Bay Water Resources: Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior'S Keweenaw Bay

The Keweenaw Bay Indian Community (KBIC) has shoreline along the south of Lake Superior that is contaminated with copper stamp sands from legacy mining. The stamp sands have been capped with sandy-loam soils and restored native species, but erosion and flooding threaten to re-deposit these stamp sands onto wetlands and into the bay. Erosion and flooding also threaten loss of beaches and shoreline, infrastructure, wetland restoration projects, and coastal highways, which has driven shoreline armoring. However, while shoreline armoring can be effective in protecting the intended areas, it can also exacerbate erosion in nearby unarmored areas, so its net impact on the shoreline is yet to be quantified. The DEVELOP team partnered with KBIC and the Environmental Protection Agency (EPA) to utilize imagery from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in order to analyze turbidity proxies. The results from these analyses showed that seasonal variation in study area was greatest during the season dominated by rain, but spatial variability across our study period did not show a clear trend. These results will be used to better inform future shoreline management efforts and support resilience in the face of more coastal erosion.

remote sensing↗
DOE OSTI2025

Simulations of muon imaging with the LANL GMT detector for spent nuclear fuel cask content verification

Atmospheric muons are typically high energy, highly penetrating charged particles. They interact with matter primarily through multiple Coulomb scatterings. Muon scattering intensities can be used to characterize the density and atomic number of the matter that they pass through. Previously, the Los Alamos National Laboratory (LANL) muon tomography team performed muon imaging of the partially filled MC-10 spent nuclear fuel (SNF) cask at Idaho National Laboratory (INL). This experiment demonstrated the feasibility of muon imaging for the verification of spent fuel container contents. That original effort used the mini muon tracker array, consisting of two arrays of drift tubes on either side of the SNF cask. The reconstructed image quality was limited by statistics, largely due to low muon flux at high zenith angles. A LANL led team will perform new measurements with a larger array, the Giant Muon Tracker (GMT), to improve data collection rates and statistics. In this work, simulations were performed with the GMT near the partially filled INL MC-10 cask. For more general fuel diversion detection, a full MC-10 cask and casks with a singular missing fuel bundle were also simulated. To understand minimum measurement times needed for missing bundle identification, 100 000 to millions of tracked muons (corresponding to 1.4 days to several weeks measurement time) were analyzed. Simulated images were then analyzed visually and numerically to explore techniques designed to minimize the collection time needed to identify the diversion of fuel in each scenario.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗