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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Novel Analysis and Visualization of Chemical Events for Public Health Surveillance

Pacific Northwest National Laboratory hosted an intern-based web application development contest in the summer of 2016 centered around developing novel chemical surveillance applications to aid in health situational awareness. Making up the three teams were three graduate students (n=9) from various US schools majoring in non-public health domains, such as computer science and user design. The interns successfully developed three applications that demonstrated a value-add to chemical surveillance—ChemAnalyzer (text analytics), RetroSpect (retrospective analysis of chemical events),and ToxicBusters (geo-based trend analytics). These applications will be the basis for the first chemical surveillance application to be incorporated into the DTRA Biosurveillance Ecosystem (BSVE).Pacific Northwest National Laboratory (PNNL), on behalf the Defense Threat Reduction Agency (DTRA; project number CB10190), hosts an annual intern- based web app development contest. Previous competitions have focused on mobile biosurveillance. The 2016 competition pivoted away from biosurveillance to focus on addressing challenges within the field of chemical surveillance and increasing public health chemical situational awareness. The result of the app will be integrated within the DTRA BSVE. PNNL hosted nine graduate interns for a 10-week period in the summer of 2016 as participants in a summer web application development contest. Students were drawn from such fields as software engineering and user experience and design and placed into three teams of three students. The challenge presented to the interns was to design and develop a fully-functional web application that would address a critical need within the chemical surveillance community. The interns developed their own ideas (vetted by PNNL and DTRA), discovered and integrated their own data sources, and produced their own visualizations and analytics, independent of any assistance outside of that provided in an advisory capacity. The competition end with a judging event with a panel of subject matter experts and cash awards were distributed to the teams. Each team produced a unique application. Although there was mild overlap between some of the ideas, the applications were developed independently and each reflected the unique contributions of the teams. ChemAnalyzer is a text-analytics platform designed to facilitate more data-driven decision, given a corpus of text data about a chemical event. Their platform provided the ability to automatically identify and highlight key words in documents related to chemical events. The keywords are drawn from an ontology installed with the system, as well as any user-identified keywords. The ChemAnalyzer team finished in third place. The RetroSpect team developed a visual analytic tool for performing retrospective analysis and monitoring of chemical events. Their app provided the ability to search and analyze past events, as well as visualization of state and county information for the recorded chemical events. The RetroSpect team finished in second place. The Toxic busters team - the winners of the competition - created a geo-based situational awareness tool for tracking chemical events. Their app featured an updateable map overlay, search functionality for finding specific or related events, incident and city/state/national-level statistics and trends, as well as news and social media integration based on keywords related to chemical surveillance. Each of the apps developed by the teams provides value to an analyst tasked with monitoring chemical events. The apps integrated unique data sources to provides a full picture of a chemical event, and its effects upon the surrounding population. This integrated analytics provides a valuable benefit over existing workflows, where analysts must monitor news, social, and other information sources manually for real-time information. The apps developed by these interns are designed to enable identification and analysis of the incident as quickly as possible, allowing for more timely assessments of the incident and its impacts. The web app development contest provided a unique opportunity for students to learn about the emerging needs in chemical surveillance as it relates to health situational awareness. Students were drawn from a variety of fields and were tasked with developing novel web apps addressing some of the most pressing challenges in the field of chemical surveillance. The ideas generated by the students will help form the basis for future chemical surveillance application development to be integrated with the DTRABSVE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Blanco DECam Bulge Survey (BDBS) II: project performance, data analysis, and early science results

ABSTRACT The Blanco DECam Bulge Survey (BDBS) imaged more than 200 sq deg of the Southern Galactic bulge using the ugrizY filters of the Dark Energy Camera, and produced point spread function photometry of approximately 250 million unique sources. In this paper, we present details regarding the construction and collation of survey catalogues, and also discuss the adopted calibration and dereddening procedures. Early science results are presented with a particular emphasis on the bulge metallicity distribution function and globular clusters. A key result is the strong correlation (σ ∼ 0.2 dex) between (u − i)o and [Fe/H] for bulge red clump giants. We utilized this relation to find that interior bulge fields may be well described by simple closed box enrichment models, but fields exterior to b ∼ −6° seem to require a secondary metal-poor component. Applying scaled versions of the closed box model to the outer bulge fields is shown to significantly reduce the strengths of any additional metal-poor components when compared to Gaussian mixture models. Additional results include: a confirmation that the u band splits the subgiant branch in M22 as a function of metallicity, the detection of possible extratidal stars along the orbits of M 22 and FSR 1758, and additional evidence that NGC 6569 may have a small but discrete He spread, as evidenced by red clump luminosity variations in the reddest bands. We do not confirm previous claims that FSR 1758 is part of a larger extended structure.

Johnson, Christian I.↗
DOE OSTI2021

Science Capsule - Capturing the Data Life Cycle

The data generated from scientific workflows often become unusable due to the lack or incompleteness of information required for processing and analyzing the data. Reproducibility of scientific data and workflows facilitates efficient processing and analyses. A key to enabling reproducibility is to capture the end-to-end workflow life cycle, and any contextual metadata and provenance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Next Generation Weather Radar (NEXRAD) Radar Line-of-Sight

The Next Generation Weather Radar (NEXRAD) system is a network of doppler radar operated jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force. This dataset represents a line-of-sight for each radar station. Radar line-of-sight can become saturated with wind energy due to interference. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Airspace/Defense↗
DOE OSTI2024

Ridgelines: Department of Interior Definition

This dataset represents ridgelines as defined by the Department of Interior (DOI): "Areas within 660 feet of the top of the ridgeline, where a ridgeline has at least 150 feet of vertical elevation gain with a minimum average slope of 10 percent between the ridgeline and the base." The dataset was created using the Geomorphons package from the University of Guelph, which can be found here: Geomorphons Package, and the 3DEP 1/3 arc-second digital elevation model. A TIF data file and a PNG map of the data are provided.

Array↗
DOE OSTI2015

Use of a metadata documentation and search tool for large data volumes: The NGEE arctic example

The Online Metadata Editor (OME) is a web-based tool to help document scientific data in a well-structured, popular scientific metadata format. In this paper, we will discuss the newest tool that Oak Ridge National Laboratory (ORNL) has developed to generate, edit, and manage metadata and how it is helping data-intensive science centers and projects, such as the U.S. Department of Energy’s Next Generation Ecosystem Experiments (NGEE) in the Arctic to prepare metadata and make their big data produce big science and lead to new discoveries.

Devarakonda, Ranjeet↗
DOE OSTI2019

Collaborative Computing Center (C3)

High Performance Computing (HPC) compliments theory and informs experimental processes in support of NE programs Big Data Analytics capabilities aid in the discovery of science-relevant relationships in data The C520 staff is made up of 19 Staff – Engineering, Basic Sciences, Computer Science, Statistics, Data Science, Technology Computer Computing Hardware Operations include: Falcon is fully utilized with an average 3X over subscription of waiting jobs over the past year Lemhi recently deployed in Feb 2019 Sawtooth procurement underway with Fall 2019 delivery 509 HPC accounts, lab, industry, university users 1.2 PB disk storage, 1.0 PB scratch storage, 0.8 PB disaster recovery storage Collaborative Computing Center nearing completion with approximately 200 staff -- Office space and 200 racks -- Data Center space for computer equipment

99 GENERAL AND MISCELLANEOUS↗