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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Pressure Drop with Moving Contact Lines and Dynamic Contact Angles in a Hydrophobic Round Minichannel: Visualization via Synchrotron X-ray Imaging and Verification of Experimental Correlations

In hydrophobic mini- and microchannels, slug flow with moving contact lines is typically generated under various two-phase flow conditions. There is a significant pressure drop in this flow pattern with moving contact lines, which is closely related to the dynamic contact angles. Researchers have investigated dynamic contact angles experimentally for decades, but due to the limitations of visualization techniques, these experiments have typically been conducted in low Weber number regions (We < 10 –3 ). In this study, we clearly visualized the dynamic contact angles of a liquid slug in high Weber number regions (10 –3 < We <1) via synchrotron X-ray imaging with high temporal (~1000 fps) and spatial (~ 2 μm/pixel) resolutions. We precisely measured the pressure drop with moving contact lines in a hydrophobic minichannel (inner diameter = 1.018 mm). On the basis of our experimental data, we verified previous correlations for dynamic contact angles and explored the relationship between pressure drop with moving contact lines and dynamic contact angles.

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

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

The development of usable visualization solutions is essential for ensuring both their adoption and effectiveness. User-centered design principles, which involve users throughout the entire development process, have been shown to be effective in numerous information visualization endeavors. We describe how we applied these principles in scientific visualization over a two year collaboration to develop a hybrid in situ/post hoc solution tailored towards combustion researcher needs. Lastly, we examine the importance of user-centered design and lessons learned over the design process in an effort to aid others seeking to develop effective scientific visualization solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hyperspectral remote sensing-based plant community map for region around NGEE-Arctic intensive research watersheds at Seward Peninsula, Alaska, 2017-2019

Using airborne hyperspectral remote sensing data from NASA Airborne Visible-Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) platforms in a region near NGEE-Arctic intensive watersheds at Seward peninsula of Alaska, high resolution (5m) maps of plant community distribution were developed and included in this data collected. AVIRIS-NG data collected over 2017-2019 period were used to develop deep neural networks, trained using vegetation plot observations collected at NGEE-Arctic watersheds at Kougarok, Council and Teller. A hierarchical vegetation classification scheme consisting of six classes at Level I, and 16 classes at Level II contained in two .txt files were used to developed the plant community maps for the region. Two geospatial raster data files (.tif) at both thematic levels are shared in this data collection. Data files in this collection use Alaska Albers Equal Area projection. Readme files available in three formats (*.html, *.md, *.pdf) and one *.png visualization map.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2023

Quantum Computing – Real-Time Data Processing from a Dilution Refrigerator

This poster presents a real-time data visualization system for monitoring and displaying data from a Bluefors control unit connected to a dilution refrigerator. The goal is to provide an aesthetically pleasing and user-friendly web-based interface that continuously updates and displays critical data such as temperature and flow rates at various points within the refrigerator. Leveraging WebSocket technology, the system establishes multiple connections to the dilution refrigerators, enabling simultaneous monitoring of several units. The interactive webpage dynamically updates the data, providing researchers and operators with instant insights into the system's performance. The system's ability to stream and visualize data in real-time enhances the understanding of the dilution refrigerator's behavior, aiding in optimizing its operation and ensuring efficient scientific experiments. The application's web-based nature makes it easily accessible from any device with internet connectivity, promoting seamless collaboration and remote monitoring capabilities. Overall, this poster offers a comprehensive solution for real-time data visualization and analysis of dilution refrigerators, catering to the needs of researchers and scientists working in low-temperature physics and quantum computing fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Recovery and Enhanced Upgrading of Rare Earth Elements from Coal-Based Resources: Bioleaching and Precipitation

Rare earth elements (REEs) are of great importance to modern society and their reliable supply is a major concern of many industries that utilize them in metal alloys, semiconductors, electrical equipment, and defense equipment. REEs in the coal waste have been revealed to be an alternative resource for REEs production. In this study, the extraction, recovery, and upgrading of the REEs from coal waste has been realized with the bioleaching and precipitation processes. Reliable and sustainable acid and oxidant production from the oxidation of the pyrite with Acidithiobacillus ferrooxidans to generate acid for leaching were realized in this research. The acidified bioleaching solution was used to extract REEs from coal waste, with 13–14% yields for most REE elements (~72 h of leaching). However, recovery for longer duration tests was significant higher (varies from 40–60% for individual REEs). After extraction, precipitation and separation processes were designed with the aid of Visual Minteq calculations and modeling to concentrate the REEs. With the procedures designed in this research, a final REEs precipitate product containing 36.7% REEs was produced.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Peregrine Software Development: Report on the Code Conversion From Python to C++

This work package seeks to convert the Peregrine software tool from its original Python implementation to a production version based on the C++ language. Peregrine is a powerful research platform with a multitude of advanced data analytics and data visualization functionalities. Developed by scientists to explore multimodal and multidimensional data related to the production of components using powder bed additive manufacturing processes, the tool implements state-of-the-art algorithms to assist machine users in making build or part quality determinations. Given that Peregrine is data-intensive, the goal of this conversion is to enhance the tool’s flexibility and interactivity and reduce the number of code dependencies to facilitate its deployment as part of the ongoing technology transfer campaign. This brief document provides an overview of Peregrine’s functionalities and capabilities, along with a detailed description of the core functionalities that have been implemented to date in the new C++ version. This document serves as a development update at the end of the first year of the ongoing conversion and will be regularly updated as progress continues.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Transparent Object Tracking Benchmark

Visual tracking has achieved considerable progress in recent years. However, current research in the field mainly focuses on tracking of opaque objects, while little attention is paid to transparent object tracking. In this paper, we make the first attempt in exploring this problem by proposing a Transparent Object Tracking Benchmark (TOTB). Specifically, TOTB consists of 225 videos (86K frames) from 15 diverse transparent object categories. Each sequence is manually labeled with axis-aligned bounding boxes. To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking. In order to understand how existing trackers perform and to provide comparison for future research on TOTB, we extensively evaluate 25 state-of-the-art tracking algorithms. The evaluation results exhibit that more efforts are needed to improve transparent object tracking. Besides, we observe some nontrivial findings from the evaluation that are discrepant with some common beliefs in opaque object tracking. For example, we find that deeper features are not always good for improvements. Moreover, to encourage future research, we introduce a novel tracker, named TransATOM, which leverages transparency features for tracking and surpasses all 25 evaluated approaches by a large margin. By releasing TOTB, we expect to facilitate future research and application of transparent object tracking in both the academia and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AI/ML Expo Boosting Job Performance with AI: Innovative Approaches and Success Stories

Our technology leverages artificial intelligence (AI) to enhance the user experience in High Performance Computing (HPC) environments. By analyzing user behavior and providing personalized recommendations, our AI system helps HPC users optimize their workflows and improve productivity. Additionally, we offer an advanced image similarity search feature, which utilizes AI algorithms to identify and retrieve visually similar images, saving users valuable time and effort in their research and analysis.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Impact Mapping for Geospatial Reasoning and Decision Making

Objective The reported study evaluated a novel approach to aiding geospatial reasoning and decision making. Background Impact mapping aims to alleviate the cognitive demands of geospatial tasks in part by externalizing data in the form of an integrated decision surface. This is achieved by aggregating data across multiple sources of information and visualizing their combined utility rather than objective measurements or individual utility. Previous research has shown that geospatial decisions improve when aided in this manner, but it remains unknown if dynamic decision making, often plagued by fatigue and anchoring bias, would benefit similarly. Method The experiment implemented a systematic manipulation of the presence of a composite impact map and the number of attributes present in a two-stage disaster relief, resource allocation task to investigate when and how impact mapping is beneficial or deleterious to decision makers. Results The presence of the composite impact map increased the utility of selected sites, increased re-planning decisions, reduced information display views, and reduced workload. Generally, the effect of the composite impact map was greater when participants were asked to evaluate more attributes. Conclusion Composite impact maps appear to improve repeated geospatial reasoning and minimize anchoring bias because they alleviate the cognitive demands otherwise necessary to interpret and maintain information from multiple attributes. Application Data visualization techniques, such as impact mapping, can improve repeated geospatial decision making in environments that include high cognitive demand.

Illingworth, David A.↗
DOE OSTI2021

Urban Energy Systems: Research at Oak Ridge National Laboratory

In the coming decades, our planet will witness unprecedented urban population growth in both established and emerging communities. The development and maintenance of urban infrastructures are highly energy-intensive. Urban areas are dictated by complex intersections among physical, engineered, and human dimensions that have significant implications for traffic congestion, emissions, and energy usage. In this chapter, we highlight recent research and development efforts at Oak Ridge National Laboratory (ORNL), the largest multipurpose science laboratory within the U.S. Department of Energy’s (DOE) national laboratory system, that characterizes the interactions between the human dynamics and critical infrastructures in conjunction with the integration of four distinct components: data, critical infrastructure models, and scalable computation and visualization, all within the context of physical and social systems. Discussions focus on four key topical themes: population and land use, sustainable mobility, the energy-water nexus, and urban resiliency, that are mutually aligned with DOE’s mission and ORNL’s signature science and technology capabilities. Using scalable computing, data visualization, and unique datasets from a variety of sources, the institute fosters innovative interdisciplinary research that integrates ORNL expertise in critical infrastructures including energy, water, transportation, and cyber, and their interactions with the human population.

Bhaduri, Budhu↗
DOE OSTI2023

Toward a Smart Metaverse City: Immersive Realism and 3D Visualization of Digital Twin Cities

Metaverse and its related extended reality technologies can enable immersive, realistic, and participatory visualization of 3D data, and their use and the potential within a smart city can be effective for supporting urban research and urban operations management. This book chapter describes a vision for prototyping a “Smart Metaverse City” to combine the unique advantage of the Metaverse technology with the two-way connectivity of a digital twin city application. This synergy aims to create a virtual environment for immersive geovisualization to help researchers and the public understand the complex urban system through science-based and data-driven approaches. This book chapter selectively reviews past technological and paradigm advancements for collecting, analyzing, and visualizing 3D urban big data. Then we present a prototyping Geographic Information System (GIS) framework, together with some relevant data sources and open-source web technologies, to help researchers create a smart Metaverse city. We demonstrate our vision and discuss its application opportunities through a real-world example, a digital twin city developed at the Oak Ridge National Laboratory, to facilitate participatory, smart, and sustainable campus management.

Xu, Haowen↗
DOE OSTI2026

VirJenDB: a FAIR (meta)data and bioinformatics platform for all viruses

High-throughput sequencing has generated an unprecedented volume of data. However, researcher-submitted data in repositories requires extensive curation and quality control for reuse. These tasks are hindered by the multiplicity of repositories, the sheer volume of the data, and the complexity of virus (meta)data curation. To address these challenges, VirJenDB offers a user-friendly platform to facilitate versioned, community-driven curation, and ontology development. Virus sequences were ingested from 16 sources, including ~200 fields of metadata or standards, covering taxonomy, sample, and host information. Up to 85 metadata fields have undergone at least one round of curation, and are linked to 15.4 million virus sequences, with 88 % from those infecting eukaryotes and the remaining infecting prokaryotes. Subsets were created, including a novel collection of 0.91 million viral operational taxonomic unit (vOTU) sequences across all viruses, while keeping the original sequences from each vOTU to facilitate downstream analyses, e.g. sequence variation. The VirJenDB web portal (https://www.virjendb.org) provides HTTPS and Application Programming Interface (API) access to the sequence datasets and metadata, offering a search engine, filtering, download, visualizations, and documentation. VirJenDB aims to connect the phage and eukaryotic virus research communities by supporting webtool integration, meta-analyses, and metadata schema extensions.

Saghaei, Shahram↗
DOE OSTI2024

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Scalable Technologies Achieving Risk-Informed Condition-Based Predictive Maintenance Enhancing the Economic Performance of Operating Nuclear Power Plants

The primary objective of the research presented in this report is to develop scalable technologies that are deployable across plant assets and across the nuclear fleet to achieve risk-informed predictive maintenance (PdM) strategies at commercial nuclear power plants (NPPs). Over the years, the nuclear fleet has relied on labor-intensive and time-consuming preventive maintenance (PM) programs, driving up operation and maintenance (O&M) costs to achieve high capacity factors. A well-constructed risk-informed PdM approach for an identified plant asset has been developed in this research, taking advantage of advancements in data analytics, machine learning (ML), artificial intelligence (AI), physics-informed modeling, and visualization. These technologies would allow commercial NPPs to reliably transition from current labor-intensive PM programs to a technology driven PdM program, eliminating unnecessary O&M costs. The work presented in the report is being developed as part of a collaborative research effort between Idaho National Laboratory and Public Service Enterprise Group Nuclear, LLC. This report (1) reflects the results of work by LWRS Program researchers with PSEG, Nuclear LLC-owned Salem and Hope Creek Nuclear Power Plants; (2) presents utilization of circulating water system (CWS) heterogeneous data and fault modes from both the Salem and Hope Creek nuclear power plant sites to develop salient fault signatures associated with each fault mode; (3) describes the integration of component-level predictive models into a robust system-level model enabled by the federated-transfer learning; (4) describes the development of physics-informed model of circulating water pump and motor; (5) develops a scalable risk and economic model; and (6) outlines the development of a user-centric visualization application. The outcomes presented in this report lays the foundation and provides a much-needed technical basis to focus on explainability and trustworthiness of ML and AI-based technologies, as part of future research.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Measurement report: Vertically resolved atmospheric properties observed over the Southern Great Plains with the ArcticShark uncrewed aerial system

Abstract. This study presents the unique capability of the Department of Energy (DOE) ArcticShark – a mid-size fixed-wing uncrewed aerial system (UAS) – for measuring vertically resolved atmospheric properties over the Southern Great Plains (SGP) of the United States. Focusing on atmospheric states, such as ambient temperature, wind, and aerosol properties, we overview measurements from 32 research flights (∼ 97 flight hours) in 2023. The August operations, aided by a visual observer on a chase plane, allowed for extensive UAS coverage, surpassing typical UAS operation envelopes. Our data from March, June, and August 2023 reveal distinctive seasonal patterns within the atmospheric column through unique chemical composition measurements. In situ measurements combined with remote sensing retrievals and radiosonde measurements provided valuable insights into their consistency and complementarity. Furthermore, we demonstrate the capabilities of the ArcticShark through several case studies, including the analyses of correlations between UAS-derived atmospheric profiles and conventional radiosonde measurements, as well as the derivation of vertically resolved profiles of aerosol chemical, optical, and microphysical properties. These case studies highlight the versatility of the ArcticShark UAS as a powerful tool for comprehensive atmospheric research, effectively bridging data gaps and enhancing our understanding of vertical atmospheric structures in the region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geophysical Signatures of Crack Network Coalescence in Rocks at Multiple Length Scales

The main goal of the research project was to identify the geophysical signatures of fracture growth in natural rocks by utilizing novel geophysical techniques. The research objectives were to (a) investigate the potential for geophysical methods to determine when cracks initiate, the types and locations of propagated cracks, and the coalescence of networks of cracks in natural rocks at multiple scales, (b) determine how damage at the microscale evolved into damage at the macroscale and then link the microscopic and macroscopic observations, (c) quantify crack coalescence in rocks under realistic stress conditions using coupled mechanical-geophysical-optical visualization, and (d) identify the precursors in geophysical signals to crack coalescence. The following research thrusts were explored to achieve the research objectives: (1) uniaxial compression testing of rock specimens with and without a set of pre‐existing flaws and (2) triaxial compression testing of natural rock specimens. These thrusts allowed for exploring fracturing in rocks under realistic in situ environments and at multiple scales. This project provided educational opportunities for nine graduate and undergraduate students and resulted in 27 peer-reviewed publications. This first research thrust focused on investigating the micromechanics of fractures in rocks through uniaxial compression testing combined with advanced geophysical and imaging techniques, specifically acoustic emission (AE) monitoring, ultrasonic imaging, and 2-dimensional Digital Image Correlation (2D-DIC). By examining damage processes under time-independent and time-dependent loading conditions, insights into damage localization, crack initiation, and fracturing mechanisms were gained. It was observed that the AE signals and the strain-based measurements directly reflect the state of damage in the rock specimen and could be used to identify the cracking levels, such as the crack initiation (CI) and crack damage (CD), and the mode of deformation. A novel calibration apparatus was developed to enhance the accuracy of AE sensors, allowing for the estimation of key parameters such as magnitude, source dimension, stress drop, and radiated seismic energy associated with the fractures. The findings highlighted significant variations in the temporal evolution of AE source parameters during the primary, secondary, and tertiary stages of creep, identifying tensile cracking as the primary deformation mode. The second research thrust focused on enhancing the understanding of fracturing processes in natural rocks through triaxial compression testing, real-time AE monitoring, and ultrasonic monitoring. We investigated the impact of various factors such as fracture propagation regimes, injection parameters, rock types, and pre-existing conditions on the hydraulic fracture (HF) behavior using scaled true-triaxially loaded specimens of Barre granite and Lyons sandstone. Custom sensor housing facilitated concurrent active and passive monitoring to analyze hydro-mechanical responses and microseismicity associated with different HF scenarios. A coupled investigation of passive microseismicity and active signal attributes permitted a detailed comprehension of the various HF processes (aseismic deformation, fracture initiation and propagation, fluid permeation, and leak-off) and their dependence on the specific rock type. The findings of this research demonstrated the effectiveness of AE monitoring techniques in providing valuable insights into the impact of various factors on the behavior and dynamics of HF processes. The advancements in monitoring techniques, offering a more thorough and precise approach, represent a significant step towards optimizing HF practices and ensuring sustainable resource extraction.

58 GEOSCIENCES↗
DOE OSTI2024

Data Visualization and Analytics for Optimal Process Parameter Selection in Turning

The objective of this project is to research physics-guided machine learning methods to recommend optimal tools and machining process parameters for turning applications using the MSC test database. For a given turning application, the MSC metalworking specialist needs to make decisions on tools and the associated process parameters for the MSC customer. For a given material, there are many alternatives for tools and a wide range of process parameters to consider. MSC has built a database of tools and parameters for different applications from the historical turning tests completed at various customer sites. The research project aims to use machine learning methods to predict optimal tools and process parameters for the MSC metalworking specialists using the MSC test database. This enables continuous learning of optimal tool and process parameters for different applications as new information is collected from testing. Through MSC, this information can be shared with machining shops across the US leading to improved productivity and efficiency.

42 ENGINEERING↗