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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Roadmap on cosmic EUV and x-ray spectroscopy

Cosmic EUV/x-ray spectroscopists, including both solar and astrophysical analysts, have a wide range of high-resolution and high-sensitivity tools in use and a number of new facilities in development for launch. As this bandpass requires placing the spectrometer beyond the Earth’s atmosphere, each mission represents a major investment by a national space agency such as NASA, ESA, or JAXA, and more typically a collaboration between two or three. In general justifying new mission requires an improvement in capabilities of at least an order of magnitude, but the sensitivity of these existing missions are already taxing existing atomic data quantity and accuracy. This roadmap reviews the existing missions, showing how in a number of areas atomic data limits the science that can be performed. The missions that will be launched in the coming Decade will without doubt require both more and improved measurements of wavelengths and rates, along with theoretical calculations of collisional and radiative cross sections for a wide range of processes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Theory and Simulation of Nanoscale Phenomena

The Center for Integrated Nanotechnologies (CINT) is a Department of Energy/Office of Science Nanoscale Science Research Center (NSRC), operating as a national user facility devoted to establishing the scientific principles that govern nanoscale integration. Nanoscale integration is defined as assembling diverse nanoscale materials across length scales to design and achieve new properties and functionality. The CINT Theory and Simulation of Nanoscale Phenomena thrust is the component of CINT dedicated to developing and applying theory to enable nanoscale integration. Our focus is on understanding and simulating the unique behavior of integrated materials and systems with nanoscale structure. This mission is achieved through collaborations with CINT Users, between thrust scientists, and with CINT scientists from other thrusts. Our research is focused on three science directions that together form the basis for integration at the nanoscale, namely (i) Hierarchical structure and dynamics in soft matter, (ii) Excitation and Transport in Nanostructured Systems, and (iii) Emergent phenomena at surfaces and interfaces. A broad spectrum of techniques is developed and applied including continuum fluid theory, atomistic and coarse-grained molecular dynamics simulations, static and dynamic electronic structure calculations, multiscale modeling, low-energy effective Hamiltonian methods, and perturbative and exact quantum many-body approaches. These tools are applied to physical systems of interest to CINT Users, the other CINT thrusts, and the general scientific community with the goals of understanding and controlling the interactions between nanoscale building blocks to assemble specific integrated structures, controlling energy transfer and other interactions over multiple length scales, and designing and exploiting the interactions within assembled structures to achieve new materials functionality.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS2020

Roadmap on cosmic EUV and x-ray spectroscopy

Cosmic EUV/x-ray spectroscopists, including both solar and astrophysical analysts, have a wide range of high-resolution and high-sensitivity tools in use and a number of new facilities in development for launch. As this bandpass requires placing the spectrometer beyond the Earth’s atmosphere, each mission represents a major investment by a national space agency such as NASA, ESA, or JAXA, and more typically a collaboration between two or three. In general justifying new mission requires an improvement in capabilities of at least an order of magnitude, but the sensitivity of these existing missions are already taxing existing atomic data quantity and accuracy. This roadmap reviews the existing missions, showing how in a number of areas atomic data limits the science that can be performed. The missions that will be launched in the coming Decade will without doubt require both more and improved measurements of wavelengths and rates, along with theoretical calculations of collisional and radiative cross sections for a wide range of processes.

Randall Smith↗
DOE OSTI2022

Panorama 360 (Final Report)

This final technical report from the lead institution, USC grant #DE-SC0012636, serves as the final technical report for collaborative institution UNC-CH grant #DE-SC0012390. The goal was to develop a repository and associated capabilities for data collection, ingestion, and analysis for a broad class of DOE applications that span experimental and simulation science workflows. In particular, this work focuses on workflows that include experimental data generation at DOE facilities. The main activities of Panorama 360 include the development of: (1) A distributed repository that stores different types of workflow execution data (e.g., point and time series performance traces at fine- and coarse-grained levels); (2) A set of open-source data capture, curation, and publishing tools fully integrated with a state-of-the-art workflow management system that automates data ingestion to the repository and enables users to discover, query, and process data from the repository; (3) A set of analysis algorithms and machine learning based tools to perform analysis and characterization of the gathered data, which can be used to detect anomalous performance or system faults; and (4) Best practices and recommendations for workflow evaluation, analysis, execution, and architectures.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

Data handling and visualization for NASA's science programs

Advanced information systems capabilities are essential to conducting NASA's scientific research mission. Access to these capabilities is no longer a luxury for a select few within the science community, but rather an absolute necessity for carrying out scientific investigations. The dependence on high performance computing and networking, as well as ready and expedient access to science data, metadata, and analysis tools is the fundamental underpinning for the entire research endeavor. At the same time, advances in the whole range of information technologies continues on an almost explosive growth path, reaching beyond the research community to affect the population as a whole. Capitalizing on and exploiting these advances are critical to the continued success of space science investigations. NASA must remain abreast of developments in the field and strike an appropriate balance between being a smart buyer and a direct investor in the technology which serves its unique requirements. Another key theme deals with the need for the space and computer science communities to collaborate as partners to more fully realize the potential of information technology in the space science research environment.

Bredekamp, Joseph H.↗
NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗
NASA NTRS2002

Making Sense of Rocket Science - Building NASA's Knowledge Management Program

The National Aeronautics and Space Administration (NASA) has launched a range of KM activities-from deploying intelligent "know-bots" across millions of electronic sources to ensuring tacit knowledge is transferred across generations. The strategy and implementation focuses on managing NASA's wealth of explicit knowledge, enabling remote collaboration for international teams, and enhancing capture of the key knowledge of the workforce. An in-depth view of the work being done at the Jet Propulsion Laboratory (JPL) shows the integration of academic studies and practical applications to architect, develop, and deploy KM systems in the areas of document management, electronic archives, information lifecycles, authoring environments, enterprise information portals, search engines, experts directories, collaborative tools, and in-process decision capture. These systems, together, comprise JPL's architecture to capture, organize, store, and distribute key learnings for the U.S. exploration of space.

knowledge management organizational learning NASA ↗
NASA NTRS2019

Image Labeler: Label Earth Science Images for Machine Learning

The application of machine learning for image-based classification of earth science phenomena, such as hurricanes, is relatively new. While extremely useful, the techniques used for image-based phenomena classification require storing and managing an abundant supply of labeled images in order to produce meaningful results. Existing methods for dataset management and labeling include maintaining categorized folders on a local machine, a process that can be cumbersome and not scalable. Image Labeler is a fast and scalable web-based tool that facilitates the rapid development of image-based earth science phenomena datasets, in order to aid deep learning application and automated image classification/detection. Image Labeler is built with modern web technologies to maximize the scalability and availability of the platform. It has a user-friendly interface that allows tagging multiple images relatively quickly. Essentially, Image Labeler improves upon existing techniques by providing researchers with a shareable source of tagged earth science images for all their machine learning needs. Here, we demonstrate Image Labeler’s current image extraction and labeling capabilities including supported data sources, spatiotemporal subsetting capabilities, individual project management and team collaboration for large scale projects.

Acharya, Ashish↗
NASA NTRSDate not supplied

Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2018

Snakes on a Spaceship—An Overview of Python in Heliophysics

Computational analysis has become ubiquitous within the heliophysics community. However, community standards for peer review of codes and analysis have lagged behind these developments. This absence has contributed to the reproducibility crisis, where inadequate analysis descriptions and loss of scientific data have made scientific studies difficult or impossible to replicate. The heliophysics community has responded to this challenge by expressing a desire for a more open, collaborative set of analysis tools. Here, this article summarizes the current state of these efforts and presents an overview of many of the existing Python heliophysics tools. It also outlines the challenges facing community members who are working toward the goal of an open, collaborative, Python heliophysics toolkit and presents guidelines that can ease the transition from individualistic data analysis practices to an accountable, communalistic environment.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Feasibility of Using Additive Manufacturing to Produce Automotive Tooling

Oak Ridge National Laboratory’s (ORNL) Manufacturing Demonstration Facility (MDF) worked with Diversified Tooling Group (DTG) in phase I of this technical collaboration to determine the feasibility of using additive manufacturing to produce automotive tooling using polymer Big Area Additive Manufacturing (BAAM) systems and will potentially work with DTG in phase II on metal BAAM systems. The following phase I goals in polymer BAAM were met: a polymer sheet metal check fixture and a polymer foundry casting pattern were completed and shipped to DTG.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bioinformatic teaching resources - for educators, by educators - using KBase, a free, user-friendly, open source platform

Over the past year, biology educators and staff at the Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free and easily accessible data science platform that integrates many bioinformatics resources into a graphical user interface built upon reproducible analysis notebooks. KBase held conversations with college and high school instructors to understand how KBase could potentially support their educational goals. These conversations morphed into a working group of biological and data science instructors that adapted the KBase platform to their curriculum needs, specifically around concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. The KBase Educators Working Group developed modular, adaptable, and customizable instructional units. Each instructional module contains teaching resources, publicly available data, analysis tools, and markdown capability to tailor instructions and learning goals for each class. The online user interface enables students to conduct hands-on data science research and analyses without requiring programming skills or their own computational resources (these are provided by KBase). Alongside these resources, KBase continues to work with instructors, supporting the development of additional curriculum modules. For anyone new to the platform, KBase, and the growing KBase Educators Organization, provides a community network, accompanied by community-sourced guidelines, instructional templates, and peer support to use KBase within a classroom whether virtual or in-person.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

BES-HEP Connections: Common Problems in Condensed Matter and High Energy Physics, Round Table Discussion

On February 2, 2015 the Offices of High Energy Physics (HEP) and Basic Energy Sciences (BES) convened a Round Table discussion among a group of physicists on ‘Common Problems in Condensed Matter and High Energy Physics’. This was motivated by the realization that both fields deal with quantum many body problems, share many of the same challenges, use quantum field theoretical approaches and have productively interacted in the past. The meeting brought together physicists with intersecting interests to explore recent developments and identify possible areas of collaboration.... Several topics were identified as offering great opportunity for discovery and advancement in both condensed matter physics and particle physics research. These included topological phases of matter, the use of entanglement as a tool to study nontrivial quantum systems in condensed matter and gravity, the gauge-gravity duality, non-Fermi liquids, the interplay of transport and anomalies, and strongly interacting disordered systems. Many of the condensed matter problems are realizable in laboratory experiments, where new methods beyond the usual quasi-particle approximation are needed to explain the observed exotic and anomalous results. Tools and techniques such as lattice gauge theories, numerical simulations of many-body systems, and tensor networks are seen as valuable to both communities and will likely benefit from collaborative development.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2004

Improving the Transition of Earth Satellite Observations from Research to Operations

There are significant gaps between the observations, models, and decision support tools that make use of new data. These challenges include: 1) Decreasing the time to incorporate new satellite data into operational forecast assimilation systems, 2) Blending in-situ and satellite observing systems to produce the most accurate and comprehensive data products and assessments, 3) Accelerating the transition from research to applications through national test beds, field campaigns, and pilot demonstrations, and 4) Developing the partnerships and organizational structures to effectively transition new technology into operations. At the Short-term Prediction Research and Transition (SPORT) Center in Huntsville, Alabama, a NASA-NOAA-University collaboration has been developed to accelerate the infusion of NASA Earth science observations, data assimilation and modeling research into NWS forecast operations and decision-making. The SPoRT Center research focus is to improve forecasts through new observation capability and the regional prediction objectives of the US Weather Research Program dealing with 0-1 day forecast issues such as convective initiation and 24-hr quantitative precipitation forecasting. The near real-time availability of high-resolution experimental products of the atmosphere, land, and ocean from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Advanced Infrared Spectroradiometer (AIRS), and lightning mapping systems provide an opportunity for science and algorithm risk reduction, and for application assessment prior to planned observations from the next generation of operational low Earth orbiting and geostationary Earth orbiting satellites. This paper describes the process for the transition of experimental products into forecast operations, current products undergoing assessment by forecasters, and plans for the future. The SPoRT Web page is at (http://www.ghcc.msfc.nasa.gov/sport).

Goodman, Steven J.↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Deletion Mutants, Archived Transposon Library, and Tagged Protein Constructs of the Model Sulfate-Reducing Bacterium Desulfovibrio vulgaris Hildenborough

ABSTRACT The dissimilatory sulfate-reducing deltaproteobacterium Desulfovibrio vulgaris Hildenborough (ATCC 29579) was chosen by the research collaboration ENIGMA to explore tools and protocols for bringing this anaerobe to model status. Here, we describe a collection of genetic constructs generated by ENIGMA that are available to the research community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

A Phase-Field Model for the U-Pu-Zr System

Modeling nuclear materials is a multi-scale, multi-physics endeavor. Researchers are currently investigating how bulk material properties are affected by the evolution of microscopic material structures, i.e. microstructures. Development of tools that can mechanistically predict the effects of processes like diffusion and grain growth on properties like thermal conductivity will transform nuclear material design, manufacturing, and use. Intermediate-scale, i.e. mesoscale, modeling is among the most promising tools available for this task. Expedited development of mesoscale models, however, will require structured collaboration between modelers and experimentalists. Coupling these fields promises to improve both model accuracy and experiment design. With these ideas in mind, a mesoscale model of the U-Pu-Zr system is being developed. Its development will accompany a series of experiments involving irradiation of metallic fuel samples.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗