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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Climatology and surface impacts of atmospheric rivers on West Antarctica

Abstract. Atmospheric rivers (ARs) transport large amounts of moisture from the mid- to high-latitudes and they are a primary driver of the most extreme snowfall events, along with surface melting, in Antarctica. In this study, we characterize the climatology and surface impacts of ARs on West Antarctica, focusing on the Amundsen Sea Embayment and Marie Byrd Land. First, we develop a climatology of ARs in this region, using an Antarctic-specific AR detection tool combined with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) atmospheric reanalyses. We find that while ARs are infrequent (occurring 3 % of the time), they cause intense precipitation in short periods of time and account for 11 % of the annual surface accumulation. They are driven by the coupling of a blocking high over the Antarctic Peninsula with a low-pressure system known as the Amundsen Sea Low. Next, we use observations from automatic weather stations on Thwaites Eastern Ice Shelf with the firn model SNOWPACK and interferometric reflectometry (IR) to examine a case study of three ARs that made landfall in rapid succession from 2 to 8 February 2020, known as an AR family event. While accumulation dominates the surface impacts of the event on Thwaites Eastern Ice Shelf (> 100 kg m−2 or millimeters water equivalent), we find small amounts of surface melt as well (< 5 kg m−2). The results presented here enable us to quantify the past impacts of ARs on West Antarctica's surface mass balance (SMB) and characterize their interannual variability and trends, enabling a better assessment of future AR-driven changes in the SMB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

17 WIND ENERGY↗
DOE OSTI2022

Validation of wind resource and energy production simulations for small wind turbines in the United States

Abstract. Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce energy generation expectations. While efficient and user-friendly, these models and tools are subject to errors that have been insufficiently quantified at small wind turbine heights. This study leverages observations from meteorological towers and sodars across the United States to validate wind speed estimates from the Wind Integration National Dataset (WIND) Toolkit, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), revealing average biases within ±0.5 m s−1 at small wind hub heights. Observations from small wind turbines across the United States provide references for validating energy production estimates from the System Advisor Model (SAM), Wind Report, MyWindTurbine.com, and Global Wind Atlas 3 (GWA3), which are seen to overestimate actual annual capacity factors by 2.5, 4.2, 11.5, and 7.3 percentage points, respectively. In addition to quantifying the error metrics, this paper identifies sources of model and tool discrepancies, noting that interannual fluctuation in the wind resource, wind speed class, and loss assumptions produces more variability in estimates than different horizontal and vertical interpolation techniques. The results of this study provide small wind installers and owners with information about these challenges to consider when making performance estimates and thus possible adjustments accordingly. Looking to the future, recognizing these error metrics and sources of discrepancies provides model and tool researchers and developers with opportunities for product improvement that could positively impact small wind customer confidence and the ability to finance small wind projects.

17 WIND ENERGY↗
DOE OSTI2020

Forward Physics Facility - Snowmass 2021 Letter of Interest

A rich physics program remains unexplored in the far-forward region at the LHC. The Forward Physics Facility (FPF) is a proposal to enlarge an existing cavern in the far-forward region of ATLAS to house a suite of experiments with groundbreaking new capabilities for neutrinos, long-lived particle searches, milli-charged particle searches, QCD, dark matter, dark sectors, and cosmic rays. The FPF will be located 500 m from the ATLAS interaction point. It is shielded from the ATLAS interaction point by 100 m of concrete and rock, creating an extremely low-background environment, ideal for many standard model studies and new physics searches. In this Letter of Interest, we describe the FPF’s location and general features, its physics potential in the HL-LHC era, and topics for further study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Snowmass 2021 Whitepaper: Proton Structure at the Precision Frontier

An overwhelming number of theoretical predictions for hadron colliders require parton distribution functions (PDFs), which are an important ingredient of theory infrastructure for the next generation of high-energy experiments. This whitepaper summarizes the status and future prospects for determination of high-precision PDFs applicable in a wide range of energies and experiments, in particular in precision tests of the Standard Model and in new physics searches at the high-luminosity Large Hadron Collider and Electron–Ion Collider. We discuss the envisioned advancements in experimental measurements, QCD theory, global analysis methodology, and computing that are necessary to bring unpolarized PDFs in the nucleon to the N2LO and N3LO accuracy in the QCD coupling strength. Special attention is given to the new tasks that emerge in the era of the precision PDF analysis, such as those focusing on the robust control of systematic factors both in experimental measurements and theoretical computations. Various synergies between experimental and theoretical studies of the hadron structure are explored, including opportunities for studying PDFs for nuclear and meson targets, PDFs with electroweak contributions or dependence on the transverse momentum, for incisive comparisons between phenomenological models for the PDFs and computations on discrete lattice, and for cross-fertilization with machine learning/AI approaches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Nuclear diagnostics for Inertial Confinement Fusion (ICF) plasmas

The field of nuclear diagnostics for Inertial Confinement Fusion (ICF) is broadly reviewed from its beginning in the seventies to present day. During this time, the sophistication of the ICF facilities and the suite of nuclear diagnostics have substantially evolved, generally a consequence of the efforts and experience gained on previous facilities. As the fusion yields have increased several orders of magnitude during these years, the quality of the nuclear-fusion-product measurements has improved significantly, facilitating an increased level of understanding about the physics governing the nuclear phase of an ICF implosion. The field of ICF has now entered an era where the fusion yields are high enough for nuclear measurements to provide spatial, temporal and spectral information, which have proven indispensable to understanding the performance of an ICF implosion. At the same time, the requirements on the nuclear diagnostics have also become more stringent. To put these measurements into context, this review starts by providing some historical remarks about the field of ICF and the role of nuclear diagnostics, followed by a brief overview of the basic physics that characterize the nuclear phase and performance of an ICF implosion. A technical discussion is subsequently presented of the neutron, gamma-ray, charged-particle and radiochemistry diagnostics that are, or have been, routinely used in the field of ICF. This discussion is followed by an elaboration of the current view of the next-generation nuclear diagnostics. Since the seventies, the overall progress made in the areas of nuclear diagnostics and scientific understanding of an ICF implosion has been enormous, and with the implementation of new high-fusion-yield facilities world-wide, the next-generation nuclear diagnostics will play an even more important role for decades to come.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Measuring Mobility Potential: A Novel Metric to Quantify Mobility Energy Productivity (MEP) of Transportation Systems

For nearly a century, the automobile has been the primary mode of personal transportation in American life. This remains true today as millions of people rely heavily on cars to connect suburbs with cities or to travel long distances - often out of routine or convenience. However, advances in technology are fueling an era of transportation transformation, with the potential to transform a system that has remained virtually unchanged for decades. The challenges of interconnecting our cities and creating a cross-continental transportation system for military purposes spawned the interstate highway system, generating the age of the automobile. In this century, congestion and mobility challenges of rising urban populations are spawning ever-evolving mobility and communications technologies to connect people to goods, services, and employment within a metropolitan and national context - all of which define a high quality of life. Aspiring smart cities are wrestling with questions such as: How does mobility impact a person's quality of life? Would people make different travel choices if they were presented with better information about their mobility options? Would businesses make different location decisions if they could assess the quality of mobility in that area? The ability to quantify the quality of mobility at a given location is the first step toward answering these questions. In response, an interdisciplinary team at the National Renewable Energy Laboratory (NREL) has developed the Mobility-Energy Productivity (MEP) metric. The MEP metric provides an avenue to not only measure the quality of mobility at a specific location in its current configuration, but also to test how various technological advances (e.g., connected and automated vehicles, plug-in electric vehicles, shared mobility) and infrastructure investments (e.g., building an additional highway lane, constructing a new shopping mall, implementing a transit-oriented development) impact the mobility of that location over time.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Cryocooled cold trap system for the SuperCDMS dilution refrigerator

Operating 6,800 feet underground at the SNOLAB facility in Sudbury, Ontario, Canada, the dilution refrigerator-cooled SuperCDMS SNOLAB (Super Cryogenic Dark Matter Search at the Sudbury Neutrino Observatory Laboratory) experiment has been designed for maximum cryogenic up-time and remote operations. A key element in achieving these goals is a pair cold traps in the helium circulation stream of the dilution refrigerator; the first operating near liquid nitrogen temperatures and the second operating near liquid helium temperatures. Previous experience with the CDMS experiment, located underground at the Soudan Under-ground Laboratory, has given significant operational experience with dilution refrigerator cold traps and has solidified the demand of a system of dual cold traps. Unlike the CDMS-era system, the new SuperCDMS system will feature a cryocooler powered liquid nitrogen re-liquefying system (as opposed to regular under-ground re-filling of cold trap dewars using portable nitrogen dewars) and a cryogen-free 4 K cold trap, which eliminates the need for a bath of liquid helium.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Hot Gravitons and Gravitational Waves From Kerr Black Holes in the Early Universe

Any abundance of black holes that was present in the early universe will evolve as matter, making up an increasingly large fraction of the total energy density as space expands. This motivates us to consider scenarios in which the early universe included an era that was dominated by low-mass ($M < 5\times 10^8$ g) black holes which evaporate prior to primordial nucleosynthesis. In significant regions of parameter space, these black holes will become gravitationally bound within binary systems, and undergo mergers before evaporating. Such mergers result in three potentially observable signatures. First, any black holes that have undergone one or more mergers will possess substantial angular momentum, causing their Hawking evaporation to produce significant quantities of high-energy gravitons. These products of Hawking evaporation are predicted to constitute a background of hot ($\sim$eV-keV) gravitons today, with an energy density corresponding to $\Delta N_{\rm eff} \sim 0.01-0.03$. Second, these mergers will produce a stochastic background of high-frequency gravitational waves. And third, the energy density of these gravitational waves can be as large as $\Delta N_{\rm eff} \sim 0.3$, depending on the length of time between the mergers and evaporation. These signals are each potentially within the reach of future measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Suppressing Coherent Gauge Drift in Quantum Simulations

Simulations of field theories on noisy quantum computers must contend with errors introduced by that noise. For gauge theories, a large class of errors violate gauge symmetry, and thus may result in unphysical processes occurring in the simulation. We present a method, applicable to non-Abelian gauge theories, for suppressing coherent gauge drift errors through the repeated application of pseudorandom gauge transformation. In cases where the dominant errors are gauge-violating, we expect this method to be a practical way to improve the accuracy of NISQ-era simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

ROOT I/O compression improvements for HEP analysis

We overview recent changes in the ROOT I/O system, increasing performance and enhancing it and improving its interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly to improve experiment's software performance. The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased during the LHC era. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly: there are significant trade-offs between the increased CPU cost for reading and writing files and the reduce storage space.

Shadura, Oksana↗
DOE OSTI2020

Urban Traveler - Changes and Impacts: Mobility Energy Productivity (MEP) Metric

For nearly a century, the automobile has been the primary mode of personal transportation in American life. This remains true today as millions of people rely heavily on cars to connect suburbs with cities or to travel long distances—often out of routine or convenience. However, advances in technology are fueling an era of transportation transformation, with the potential to transform a system that has remained virtually unchanged for decades. Aspiring smart cities are wrestling with questions such as: How does mobility impact a person’s quality of life? Would people make different travel choices if they were presented with better information about their mobility options? The ability to quantify the mobility potential of a given location is the first step toward answering these questions. In response, an interdisciplinary team at the National Renewable Energy Laboratory (NREL) has developed the Mobility-Energy Productivity (MEP) metric. The MEP metric provides an avenue to not only measure the mobility potential at a specific location in its current configuration, but also to test how various technological advances (e.g., connected and automated vehicles, plug-in electric vehicles, shared mobility) and infrastructure investments (e.g., building an additional highway lane, constructing a new shopping mall, implementing a transit-oriented development) impact the mobility of that location over time. This presentation details the FY19 progress on the MEP metric project funded through DOE VTO's SMART Mobility Consortium.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Arctic Community Engagement: Enhancing Energy Affordability, Resilience, Security

The changing Arctic geopolitical and natural environment presents significant new challenges and opportunities for innovation, economic development, and sustainable energy, as well as enhancing resilience and security in Alaska communities and the greater Arctic region. NREL's Arctic strategy focuses on addressing the impacts of climate change, developing a clean energy and weatherization workforce in the arctic, advancing energy innovation, developing climate appropriate and affordable housing, and ensuring energy security in an era of growing geopolitical threats.

Alaska↗
DOE OSTI2021

Machine Intelligence to Detect, Characterise, and Defend against Influence Operations in the Information Environment

Social media has enabled a new era of manipulation in the information and cognitive domains. Deceptive content—misleading, falsified, and fabricated—is routinely created and spread in the modern social media environment with the intent to create confusion and widen political and social divides, and exploit the societal conflict exacerbated by these divides in the real-world (aka physical domain). Such disinformation campaigns demonstrate a threat to the integrity of economic, political, cultural, public health, and national security institutions around the world. In this work we overview our artificial intelligence (AI) capabilities to detect, describe, and defend against information operations on Twitter as an example social platform to understand the influence of misleading and falsified content diffusion and better enable those charged with defending against such manipulation to enable responsive parties to counter it. We first present novel linguistically-informed deep learning (DL) models for misinformation and disinformation detection, and present an in-depth linguistic analysis of psycho-linguistic markers across broad deception categories. We then demonstrate how our models perform in the multilingual and multimodal setting and categorize falsified and misleading content based on the intent to deceive. We also provide a large-scale analysis to describe user behavior and spread patterns while engaging with deceptive content and report novel findings about the immediate diffusion of deceptive content by characterizing the vulnerable sub-populations and their demographics, and explicitly measuring speed and scale of deception spread to uncover who shares deceptive content, how quickly, how much, and how evenly. In addition, we measure audience reactions to misinformation and disinformation at scale, distinguishing the reactions of users identified as bots versus humans. Finally, we take advantage of deep translation and generation models to create unique solutions for real-time defense against digital deception and discuss how to apply causal inference to prescribe and intervene into strategic communications jointly across information, cognitive, and physical domains.

artificial intelligence, deep learning, neural lan↗
DOE OSTI2021

An Indicator-based Approach to Sustainable Management of Natural Resources (Chapter 12)

Assessing the sustainability of natural resource management choices for agricultural and forest lands requires quantification of potential changes to a set of environmental and socioeconomic indicators selected to characterize reference scenarios relative to projected future scenarios. Correctly framing the questions with local stakeholders is a critical first step in the sustainability assessment, and the questions that can be addressed are often limited by data availability. Selecting and prioritizing indicators with stakeholders to address their needs and concerns improves the likelihood of investment in monitoring and evaluation of those indicators over time. Computational techniques for analyzing interactions between the selected indicators are inherently affected by the scales and formats of the assembled indicator datasets. Data analytics have the potential to improve understanding of the potential synergies and tradeoffs involved with meeting multiple environmental and socioeconomic goals simultaneously, but timely and appropriate indicator datasets are not always available—even in this new era of “big data.” Continued improvements in data science and data analytics are needed to broaden understanding and acceptance of problems and to provide valuable information for natural resource management. Advances in these areas will enable society to design future landscapes that meet multiple objectives, including the provisioning of agricultural and forest resources along with a variety of ecosystem services (e.g., clean water and healthy soils).

Parish, Esther↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2022

Semi-automated Design of Artificial Intelligence Earth Science Models

Prediction and observation of water cycles involve not only patterns isolated in space and time, but rather modeling complex spatio-temporal relationships across multiple sources of data and domains. For instance, Evapotranspiration (ET) and Leaf Area Indexes (LAI) are two critical components in DOE’s Energy Exascale Earth System Model (E3SM). Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, current ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFT), which fail to capture the fine-scale variations due to change in vegetation type across the globe. Within this context and in light of the data-model integration challenges highlighted in the EESSD Strategic Plan, the new era of AI model development for geosciences calls for data-driven methods that provide domain scientists with estimations of parameters such as PFT and LAI in an efficient, interpretable, and easy-to-operate manner.

Ambrozio Dias, Philipe↗
DOE OSTI2021

Nuclear Deactivation and Downgrade of Enriched Uranium Facilities at the Y-12 National Security Complex

Buildings 9206 and 9212 at the Y-12 National Security Complex are both Hazard Category 2 nuclear facilities from the Manhattan Project era that supported various missions over the decades, including the processing of enriched uranium. The production mission for Building 9206 ended in the 1990s, while Building 9212 is still an active production facility. The disposition process for contaminated excess facilities is generally described in the U.S. Department of Energy (DOE) Guide DOE O 430.1A, Life Cycle Asset Management, which provides guidance on facility surveillance and maintenance, deactivation, decommissioning, and transition in supplemental guides DOE G 430.1-2 through 430.1-5. Historically, production facilities similar to Buildings 9206 and 9212 have not always been cleaned out and deactivated prior to being excessed, which significantly complicates the decommissioning phase. Many legacy facilities were shut down before the formal DOE guidance was established. This paper presents the practical application and implementation of the DOE guidance for the nuclear deactivation of Buildings 9206 and 9212, and summarizes the lessons learned for application across the DOE enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗