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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cold Weather Impacts on Electric School Bus Performance in Aurora, Colorado

This brief highlight details the key takeaways from a project that utilized NLR's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline related to electric school bus (ESB) operation. ESBs using battery energy as their primary heating source have a higher energy consumption rate in cold weather, which fleet managers can account for when planning ESB purchases and making dispatching and charging decisions. Researchers found that electric school buses operate 2-5 times more efficiently than conventional buses, on average. Cold weather can double electric school bus energy demands, but strategies such as thermal pre-conditioning significantly reduce this effect. Understanding these impacts can help fleets plan charging, dispatching, and purchase decisions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Brady's Geothermal Field Nodal Seismometer Data

This submission includes links to raw data, field notes, metadata, and p-wave arrival auto-picks from processed data (not provided) from the nodal seismometer array deployed at the PoroTomo Natural Laboratory in Brady's Hot Springs, Nevada during the March 2016 testing. The data is available as continuous or windowed (to vibroseis sweep) files and is stored in an AWS S3 bucket. Note: No raw data was recovered from stations 73 and 82. These data are available for download without login credentials through the free and publicly accessible Open Energy Data Initiative (OEDI) data viewer which allows users to browse and download individual or groups of files.

15 GEOTHERMAL ENERGY↗
NASA NTRS2003

The Ultra-Soft X-ray Background: A Probe of the Hot Interstellar Medium and the Local Bubble - ADP-99

The Ultra-Soft X-ray Telescope (UXT) was a sounding rocket mission flown three times in 1984 - 1986. At the beginning of the project, the data existed solely in form of raw telemetry data stored on 9 track tapes. The primary goal of this proposal has been to extract event files from the raw telemetry stream and to create instrument response models and calibrated spectra from it. We have completed this project, and the data will soon be available to all via the HEASARC archive of high-energy data at Goddard Space Flight Center. We are in the process of combining the results with the ALEXIS and DXS observations of the Local Bubble in modelling the 72 eV iron line (recently observed by the X-ray Quantum Calorimeter) and the carbon emission lines that are uniquely visible in this dataset. Our results agree with the XQC observation which predicts a maximum emission in the 72 eV iron lines that is below the limit observable with UXT. However, this leaves an open question as to what lines were responsible for the observed Be-band emission. The answer to this question will likely require more observations of soft X-rays with the Chandra LETGS and new atomic data models of potentially emitting ions.

Smith, Randall↗
DOE OSTI2013

Enhancements in Continuous-Energy Monte Carlo Capabilities in SCALE

Monte Carlo tools in SCALE are commonly used in criticality safety calculations as well as sensitivity and uncertainty analysis, depletion, and criticality alarm system analyses. Recent improvements in the continuous-energy data generated by the AMPX code system and significant advancements in the continuous-energy treatment in the KENO Monte Carlo eigenvalue codes facilitate the use of SCALE Monte Carlo codes to model geometrically complex systems with enhanced solution fidelity. The addition of continuous-energy treatment to the SCALE Monaco code, which can be used with automatic variance reduction in the hybrid MAVRIC sequence, provides significant enhancements, especially for criticality alarm system modeling. This paper describes some of the advancements in continuous-energy Monte Carlo codes within the SCALE code system.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2022

Washington, D.C. & Maryland Energy: Estimating Solar Potential Using NASA POWER Data to Inform Renewable Energy Policy for Washington, D.C.

In line with the Sustainable D.C. 2.0 plan to combat climate change, Washington D.C. aims to decrease its greenhouse gas emissions by 100% by 2050. As solar energy is a clean, renewable energy form, its integration into the region’s power grids lowers energy costs and incentivizes sustainable development. We partnered with the Washington D.C. Department of Energy & Environment (DOEE) to determine how urban areas surrounding D.C. can better be incorporated into decisions regarding renewable energy policy. The team used NASA’s Prediction of Worldwide Energy Resources (POWER) solar data and a Light Detection and Ranging (LiDAR) derived digital surface model, to estimate and visualize rooftop solar potential for Maryland’s Prince George’s and Montgomery counties. POWER provided solar irradiance data adjusted for tilt angle while the digital surface model contributed aspect and slope data. This methodology factored out areas that were unsuitable for solar panel installation while displaying areas that possess a high potential for energy return. The team found the total rooftop solar potential for the study area to be almost 32 million kW, which is equivalent to roughly 660 kW per building. The methodology used to generate the solar potential maps can be applied to other regions of the country seeking to efficiently utilize solar energy. The end users at the DOEE can use our resulting solar potential map and data table to effectively target buildings that have the highest potential to generate solar energy.

Edward Cronin↗
DOE OSTI2017

A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling

As urban areas continue to grow and evolve in a world of increasingenvironmental awareness, the need for detailed information regarding residentialenergy consumption patterns has become increasingly important. Though currentmodeling efforts mark significant progress in the effort to better understand thespatial distribution of energy consumption, the majority of techniques are highlydependent on region-specific data sources and often require building- ordwelling-level details that are not publicly available for many regions in the UnitedStates. Furthermore, many existing methods do not account for errors in input datasources and may not accurately reflect inherent uncertainties in model outputs. Wepropose an alternative and more general hybrid approach to high-resolution residentialelectricity consumption modeling by merging a dasymetric model with acomplementary machine learning algorithm. The method’s flexible data requirementand statistical framework ensure that the model both is applicable to a widerange of regions and considers errors in input data sources.

cities↗
DOE OSTI2019

Report of the Workshop on Advanced Fusion with Machine Learning, April 30-May 2, 2019

The pursuit of fusion energy has led to extensive activities in both experimental and theoretical science. The central goal of all these activities has been to develop the knowledge necessary for the design of successful fusion power plants. Even today, following decades of research in many key areas including plasma physics and material science, much remains to be learned in order to achieve optimization of the tokamak or other paths to fusion energy. Data science methods from the fields of machine learning and artificial intelligence (ML/AI) offer opportunities for enabling or accelerating progress toward the realization of fusion energy by maximizing the amount and usefulness of information extracted from experimental and simulation output data. From April 30 to May 2 of 2019, a workshop on “Advancing Fusion with Machine Learning Research Needs” was held in Gaithersburg, MD, with joint support from the Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR) Programs. The workshop brought together ~ 60 experts in fields spanning fusion science, data science, statistical inference and mathematics, machine learning, and artificial intelligence, along with DOE program managers and technical experts, with the goal of identifying Priority Research Opportunities (PROs) for the application of ML/AI methods to accelerating progress on solving fusion problems. During the workshop, seven key PROs were identified. The description of each PRO includes relevant fusion problems, potentially useful ML/AI approaches, known gaps currently preventing the use of such approaches, and research guidelines to maximize the effective application of ML/AI methods to the PRO. The report containing the full description of the workshop outcomes is available at https://science.osti.gov/-/media/fes/pdf/workshopreports/FES_ASCR_Machine_Learning_Report.pdf

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Thermosyphon Cooler Hybrid System for Water Savings in an Energy-Efficient HPC Data Center: Results from 24 Months and the Impact on Water Usage Effectiveness

In August 2016, the National Renewable Energy Laboratory (NREL) installed a thermosyphon hybrid cooling system to reduce water usage in its already extremely energy-efficient High-Performance Computing (HPC) Data Center. In its first year of use, the system saved 4,400 m3 (1.16 million gal) of water, and 7,950 m3 (2.10 million gal) during a 2-year period, cutting the use of water in the data center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia. center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Updated THM astrophysical factor of the {sup 19}F(p,α){sup 16}O reaction and influence of new direct data at astrophysical energies

Fluorine nucleosynthesis represents one of the most intriguing open questions in nuclear astrophysics. It has triggered new measurements which may modify the presently accepted paradigm of fluorine production and establish fluorine as an accurate probe of the inner layers of asymptotic giant branch (AGB) stars. Both direct and indirect measurements have attempted to improve the recommended extrapolation to astrophysical energies, showing no resonances. In this work, we will demonstrate that the interplay between direct and indirect techniques represents the most suitable approach to attain the required accuracy for the astrophysical factor at low energies, E{sub c.m.}≲300 keV, which is of interest for fluorine nucleosynthesis in AGB stars. We will use the recently measured direct {sup 19}F(p,α){sup 16}O astrophysical factor in the 600 keV≲E{sub c.m.}≲800 keV energy interval to renormalize the existing Trojan Horse Method (THM) data spanning the astrophysical energies, accounting for all identified sources of uncertainty. This has a twofold impact on nuclear astrophysics. It shows the robustness of the THM approach even in the case of direct data of questionable quality, as normalization is extended over a broad range, minimizing systematic effects. Moreover, it allows us to obtain more accurate resonance data at astrophysical energies, thanks to the improved {sup 19}F(p,α){sup 16}O direct data. Finally, the present work strongly calls for more accurate direct data at low energies, so that we can obtain a better fitting of the direct reaction mechanism contributing to the {sup 19}F(p,α){sup 16}O astrophysical factor. Indeed, this work points out that the major source of uncertainty affecting the low-energy S(E) factor is the estimate of the non-resonant contribution, as the dominant role of the 113 keV resonance is now well established.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Optical study on the possible Slater insulator SrIr 0.8 Sn 0.2 O 3

The orthorhombic perovskite iridate SrIr 0.8 Sn 0.2 O 3 is reported to be a new candidate of Slater insulator at low temperatures. Here, we present optical spectroscopy measurements on SrIr 0.8 Sn 0.2 O 3 at different temperatures (T) across the paramagnetic-antiferromagnetic phase transition. The low energy reflectivity decreases with reducing T. Six phonon peaks in the spectrum at low T are visible at room temperature, indicating the absence of crystal structure phase transition from 300 K to 10 K. The real part of conductivity spectra σ 1 (ω) are obtained by employing the standard Kramers-Kronig transformation. In the paramagnetic state at room temperature, σ 1 (ω) reaches a limiting value as ω → 0, implying an electrical conducting state. Upon decreasing T, the low frequency σ 1 (ω) decreases and approaches zero with a concomitant opening of a direct gap. As a result, detailed analysis of the low energy data shows that the gap opening is continuous and σ 1 (ω) at the low energy gap edge follows an unusual ω 1/2 dependence at low temperatures which is remarkably similar to other Slater insulators, such as NaOsO 3 .

3-dimensional systems↗
NASA NTRS2015

Enhancing the NASA Prediction of Worldwide Energy Resource Web Data Delivery System with Geographic Information System (GIS) Capabilities

Renewable energy technologies are changing the face of the world's energy market. Currently, these technologies are being incorporated within existing structures to increase energy efficiency. Crucial to the success of the emerging renewable market is the availability of accurate, global solar radiation, and meteorology data. This poster traces the history of the development of an effort to distribute data parameters from NASA's research for use in the energy sector applications spanning from renewable energy to energy efficiency. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings.

Chandler, William S.↗
DOE OSTI2013

Nuclear data processing for energy release and deposition calculations in the MC21 Monte Carlo code

With the recent emphasis in performing multiphysics calculations using Monte Carlo transport codes such as MC21, the need for accurate estimates of the energy deposition-and the subsequent heating - has increased. However, the availability and quality of data necessary to enable accurate neutron and photon energy deposition calculations can be an issue. A comprehensive method for handling the nuclear data required for energy deposition calculations in MC21 has been developed using the NDEX nuclear data processing system and leveraging the capabilities of NJOY. The method provides a collection of data to the MC21 Monte Carlo code supporting the computation of a wide variety of energy release and deposition tallies while also allowing calculations with different levels of fidelity to be performed. Detailed discussions on the usage of the various components of the energy release data are provided to demonstrate novel methods in borrowing photon production data, correcting for negative energy release quantities, and adjusting Q values when necessary to preserve energy balance. Since energy deposition within a reactor is a result of both neutron and photon interactions with materials, a discussion on the photon energy deposition data processing is also provided. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

CO2-Locate: A Dynamic Database and Tool for Accessing National Oil and Gas Well Data to Inform Carbon Storage Projects

The CO2-Locate Database is a growing compilation of publicly available wellbore resources that have been merged based on common attributes across data sources with an attribute schema developed to be consistent across disparate resources, reduce data gaps, and eliminate record redundancy. The first version of CO2-Locate has been published to Energy Data eXchange (EDX) and includes the integrated public wells dataset as well as additional geospatial summary layers of key wellbore characteristics to protect proprietary resources. Additionally, the CO2-Locate database has been deployed into a web application, enabling easy access, data filtering capabilities, and visualization of U.S. wellbore infrastructure by stakeholders to inform injection site selection and risk assessments.

Dyer, Alec S. [NETL Site Support Contractor, Natio↗
DOE OSTI2017

Black carbon emissions from biomass and coal in rural China

Residential solid fuel combustion makes a major contribution to black carbon (BC) emissions in China. A new estimation of BC emissions from rural solid biomass and coal consumption has been derived from field survey data. The following new contributions are made: (1) emission factors are collected and reviewed; (2) household energy data are collected from field survey data and from the literature; (3) a new extrapolation method is developed to extend the field survey data to other locations; (4) the ownership and usage of two stove types are estimated and considered in the emission calculations; and (5) uncertainties associated with the estimation results are quantified. It is shown that rural households with higher income will consume less biomass but more coal. Agricultural acreage and temperature also significantly influence the amount of solid fuel consumed in rural areas. It is estimated that 640±245 Gg BC/y were emitted to the atmosphere due to residential solid fuel consumption in rural China in 2014. Emissions of BC from straw, wood, and coal contributed 42±13%, 36±15%, and 22±10% of the total, respectively. As a result, we show that effective BC mitigation (a reduction of 47%) could be obtained through widespread introduction of improved stoves in rural households

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2017

Application of automated measurement and verification to utility energy efficiency program data

Trustworthy savings calculations are critical to convincing regulators of both the cost-effectiveness of energy efficiency program investments and their ability to defer supply-side capital investments. Today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of energy efficiency programs. They also require time-consuming data acquisition. A spectrum of savings calculation approaches is used, with some relying more heavily on measured data and others relying more heavily on estimated, modeled, or stipulated data. The increasing availability of “smart” meters and devices that report near-real time data, combined with new analytical approaches to quantify savings, offers the potential to conduct M&V more quickly and at lower cost, with comparable or improved accuracy. Commercial energy management and information systems (EMIS) technologies are beginning to offer these ‘M&V 2.0’ capabilities, and program administrators want to understand how they might assist programs in quickly and accurately measuring energy savings. This paper presents the results of recent testing of the ability to use automation to streamline the M&V process. In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. For the data sets studied we evaluate the fraction of buildings that are well suited to automated baseline characterization, the uncertainty in gross savings that is due to M&V 2.0 tools’ model error, and indications of labor time savings, and how the automated savings results compare to prior, traditionally determined savings results. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14. In addition the findings suggest that M&V 2.0 methods may also offer time-savings relative to traditional approaches. Lastly, we discuss the implications of these findings relative to the potential evolution of M&V, and pilots currently being launched to test how M&V automation can be integrated into ratepayer-funded programs and professional implementation and evaluation practice.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1999

Hard X-Ray Detection of the High Redshift Quasar 4C 71.07

BATSE/OSSE observations of the high redshift quasar 4C 71.07 indicate that this is the brightest and furthest AGN so far detected 20 keV. BATSE Earth occultation data have been used to search for emission from 4C 71.07 from nearly 3 years of observation. The mean source flux over the- whole period in the BATSE energy range 20-100 keV is (13.2 +/- 1.06) x 10(exp -11) erg/square cm/s corresponding to a luminosity of 2 x 10(exp 48 erg/s. The BATSE light curve over the 3 years of observations shows several flare-like events, one of which (in January 1996) is associated with an optical flare (R=16.1) but with a delay of 55 days. The OSSE/BATSE spectral analysis indicates that the source is characterized by a flat power spectrum (Gamma is approximately 1.1- 1.3) when in a low state: this spectral form is consistent within errors with the ASCA and ROSAT spectra. This means that the power law observed from 0.1 to 10 keV extends up to at least 1 MeV but steepens soon after to meet EGRET high energy data. BATSE data taken around the January 1996 flare suggests that the spectrum could be steeper when the source is in a bright state. The upsilon-F-upsilon representation of the source is typical of a low frequency peaked/ gamma- ray dominated blazar, with the synchrotron peak in the mm-FIR band and the Compton peak in the MeV band. The BATSE and OSSE spectral data seem to favour a model in which the high energy - flux is due to the sum of the synchrotron self-Compton and the external Compton contributions: this is also supported by the- variability behaviour of the source.

Malizia, A.↗