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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Buildings-to-Grid Technical Opportunities: Introduction and Vision

The introduction of smart sensing, metering, and control technology is transforming the nature of our power system from end to end. Enabled by the proliferation of information technology, decision makers—such as utilities and building owners—can see and understand the operation of their systems with unprecedented levels of granularity and detail. Technological advances such as smart thermostats and Phasor Measurement Units, which grant grid operators increased situational awareness, promise tremendous gains in the efficiency and grid-responsiveness of commercial and residential buildings, as well as the overall power system’s reliability and efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Cosmic Visions Dark Energy. Science

Cosmic surveys provide crucial information about high energy physics including strong evidence for dark energy, dark matter, and inflation. Ongoing and upcoming surveys will start to identify the underlying physics of these new phenomena, including tight constraints on the equation of state of dark energy, the viability of modified gravity, the existence of extra light species, the masses of the neutrinos, and the potential of the field that drove inflation. Even after the Stage IV experiments, DESI and LSST, complete their surveys, there will still be much information left in the sky. This additional information will enable us to understand the physics underlying the dark universe at an even deeper level and, in case Stage IV surveys find hints for physics beyond the current Standard Model of Cosmology, to revolutionize our current view of the universe. There are many ideas for how best to supplement and aid DESI and LSST in order to access some of this remaining information and how surveys beyond Stage IV can fully exploit this regime. These ideas flow to potential projects that could start construction in the 2020's.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Cosmic Visions Dark Energy: Technology

A strong instrumentation and detector R&D program has enabled the current generation of cosmic frontier surveys. A small investment in R&D will continue to pay dividends and enable new probes to investigate the accelerated expansion of the universe. Instrumentation and detector R&D provide critical training opportunities for future generations of experimentalists, skills that are important across the entire Department of Energy High Energy Physics program.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Hydropower Vision: A New Chapter for America’s 1 st Renewable Electricity Source (Full Report)

Hydropower has provided clean, affordable, reliable, and renewable electricity in the United States for more than a century. Building on hydropower’s historical significance, and to inform the continued technical evolution, energy market value, and environmental performance of the industry, the U.S. Department of Energy’s (DOE’s) Wind and Water Power Technologies Office has led a first-of-its-kind comprehensive analysis focused on a set of potential pathways for the environmentally sustainable expansion of hydropower (hydropower generation and pumped storage) in the United States.

13 HYDRO ENERGY↗
DOE OSTI2016

Cosmic Visions Dark Energy: Technology

A strong instrumentation and detector R&D program has enabled the current generation of cosmic frontier surveys. A small investment in R&D will continue to pay dividends and enable new probes to investigate the accelerated expansion of the universe. Instrumentation and detector R&D provide critical training opportunities for future generations of experimentalists, skills that are important across the entire DOE HEP program.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Cosmic Visions Dark Energy: Science

Cosmic surveys provide crucial information about high energy physics including strong evidence for dark energy, dark matter, and inflation. Ongoing and upcoming surveys will start to identify the underlying physics of these new phenomena, including tight constraints on the equation of state of dark energy, the viability of modified gravity, the existence of extra light species, the masses of the neutrinos, and the potential of the field that drove inflation. Even after the Stage IV experiments, DESI and LSST, complete their surveys, there will still be much information left in the sky. This additional information will enable us to understand the physics underlying the dark universe at an even deeper level and, in case Stage IV surveys find hints for physics beyond the current Standard Model of Cosmology, to revolutionize our current view of the universe. There are many ideas for how best to supplement and aid DESI and LSST in order to access some of this remaining information and how surveys beyond Stage IV can fully exploit this regime. These ideas flow to potential projects that could start construction in the 2020's.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Cosmic Visions Dark Energy: Small Projects Portfolio

Understanding cosmic acceleration is one of the key science drivers for astrophysics and high-energy physics in the coming decade (2014 P5 Report). With the Large Synoptic Survey Telescope (LSST) and the Dark Energy Spectroscopic Instrument (DESI) and other new facilities beginning operations soon, we are entering an exciting phase during which we expect an order of magnitude improvement in constraints on dark energy and the physics of the accelerating Universe. This is a key moment for a matching Small Projects portfolio that can (1) greatly enhance the science reach of these flagship projects, (2) have immediate scientific impact, and (3) lay the groundwork for the next stages of the Cosmic Frontier Dark Energy program. In this White Paper, we outline a balanced portfolio that can accomplish these goals through a combination of observational, experimental, and theory and simulation efforts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Grand Challenges for Biological and Environmental Research: Progress and Future Vision. A Report from the Biological and Environmental Research Advisory Committee

The Biological and Environmental Research (BER) program within the U.S. Department of Energy (DOE) Office of Science supports research focusing on the interconnections between energy production and the living environment. This fundamental research, conducted at universities, DOE national laboratories, and research institutions across the country, explores organisms and ecosystems that can influence the U.S. energy system and advances understanding of the relationships between energy and environment from local to global scales. Research within BER can be categorized into biological systems and Earth and environmental systems. Biological systems research seeks to characterize and predictively understand microbial and plant systems using genomic science, computational analyses, and experimental approaches. Foundational knowledge of the structure and function of these systems underpins the ability to leverage natural processes for energy production, including the sustainable development of biofuels and other bioproducts. Earth and environmental systems research seeks to characterize and predictively understand feedbacks between Earth and energy systems and includes studies on atmospheric physics and chemistry, ecosystem ecology and biogeochemistry, and development and validation of Earth system models extending from regional to global scales. To promote world-class research in these areas, BER supports user facilities that enable observation and measurement of atmospheric, biological, and biogeochemical processes using the latest technologies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

Within the DOE complex, 3013 canisters are used to store Pu-bearing waste. Wall penetration due to corrosion, specifically stress corrosion cracking, is considered to be the most likely cause of failure over the 50-year lifetime of the canisters. This project has 2 objectives: The first is the development of machine learning algorithms to identify the presence of corrosion from a very large set of images generated by laser confocal microscope scanning of 3013 canister used to store Pu oxides. This portion of the LDRD constitutes image analysis of a metal surface for the presence of corrosion. The image processing algorithms developed for this project will provide a suitable basis for analysis of other types of corrosion data, which can be produced in vast quantities using modern devices. The second component of the LDRD consists of the development of machine learning algorithms that obtain molecular mechanics force-fields from ab-initio Density Functional Theory (DFT) calculations for corrosive attack by chlorides on 304L or 316L stainless steel. The goal of this latter component is to determine means for mitigating corrosion on a fundamental level, including coatings, welding methods, metal composition, etc. Force-field modeling is necessary for this endeavor because the incipience and progression of corrosion is the governed by molecular structures, grain boundaries, dislocations and surface structures represented by large numbers of atoms. Although DFT calculations are extremely adept at describing molecular scale processes, they are computationally expensive making them ill-suited for calculations of more than several hundred atoms, especially for the repeated applications inherent in material design. Fortunately, force-field methods provide an avenue for viable molecular-scale calculations involving the numbers of atoms involved in corrosion processes. The accuracy of the force-field calculations depends strongly on the accuracy of the force-field model, which is extremely difficult and time-consuming to derive from either data or ab-initio calculations. The use of machine learning algorithms has the potential to make the calculation of force-fields much more efficient but has not been explored significantly for corrosion processes. If shown to be viable, the technique would have wide -ranging impact on design of corrosion resistant materials and on the mitigation of corrosion in existing process systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

A Publicly Available, Annotated Dataset for Naturalistic Driving Study and Computer Vision Algorithm Development

Oak Ridge National Laboratory developed and implemented a data collection effort to create a dataset for use in evaluating and testing algorithms for analyzing driver behavior under controlled settings for support of the Federal Highway Administration’s Exploratory Advanced Research Program. This collection is called the ORNL Naturalistic Driving Study Sample (ONDSS). The dataset is designed to emulate aspects of the Second Strategic Highway Research Project (SHRP2), which contained a massive naturalistic driving study (NDS) with over 3000 drivers between 2010 and 2013 using their personal vehicles, with over 4300 person-years of data collected [HANKEY].

42 ENGINEERING↗
DOE OSTI2021

Atmospheric Radiation Measurement (ARM) User Facility 2020 Decadal Vision

The Atmospheric Radiation Measurement (ARM) user facility was established in 1989 by the U.S Department of Energy (DOE) Office of Biological and Environmental Research (BER) to provide an observational basis for studying the Earth’s climate. ARM began collecting observations in 1992 and was designated a user facility in 2003. The facility includes a network of extensively instrumented long-term fixed-location observatories and mobile facilities. The ARM facility also includes an aerial component to augment these ground-based measurements. Because of the diversity of in situ and remotely obtained observations, ARM’s data management infrastructure is designed to collect, process, and deliver data to the research community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Lessons from the COVID Era and Visions for the Future

In December 2020, the U.S. Department of Energy Office of Science convened a virtual Roundtable of its 27 operating scientific user facilities to discuss facility challenges and lessons learned during the COVID-19 pandemic as well as facility responses, best practices, and innovations that could be adopted going forward. Roundtable participants included facility staff, users, and user executive committee chairs. This report summarizes their discussions, which encompassed topics such as user research and facility operations in virtual and physically distanced contexts; user training and engagement; computation, data, and network resources; and crosscutting issues.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗