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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data Driven Commercial Building Energy Code Compliance and Technology Inventory for New York City

Building Performance Standards (BPS) are gaining national traction. A BPS will require new processes in the design, construction, and operation of buildings that take the occupants into account and enable predictive analysis to ensure compliance with current and future GHG emissions caps. In New York City, most buildings over 25,000 square feet will be regulated by a BPS starting in 2024, regardless of whether it is new construction permitted under current energy codes or an existing building. This research is one of the first to begin the evaluation of a long-term series of building policies in the context of an open data ecosystem, in cooperation with city agencies. Existing building policies enacted in NYC have ranged from building energy benchmarking and labeling to energy audits to the regulation of GHG emission in buildings. Through the development of a dataset related to building technologies and energy consumption, this project can help to evaluate if meaningful conclusions can be drawn for the data that has been largely self-reported in compliance with city regulations. This project will also provide lessons learned from a deep dive into these types of datasets to provide best practices for municipalities or states seeking to embark on policies like those enacted in NYC. In addition, a Building Automation System (BAS) Stretch Standard of Care (SSOC) for owners, designers, and building operators will enable the measurement and predictive analysis of energy consumption and GHG emissions at the plant, system, or component level, in anticipation of regulated GHG limits on buildings based on energy use. The SSOC is expected to be suitable for use on a national level. The primary feature of an SSOC is a standardized format for a set of BAS points that can be used to control and to gather data from individual plants, systems, or components that are related to building energy consumption. This project examined how measurements compare to prescriptive or simulation-based energy code targets, finding little correlation between predictive 8760-hour energy modeling and actual energy consumption for a small sample (n=27) of buildings constructed after 2015. Other analysis found that, while large multifamily housing (MFH) buildings showed a general trend similar to predicted reductions in energy use from the implementation of model commercial energy codes, this trend was not evident in the office, K-12 school, and hotel use groups in NYC. No upward or downward trends in energy consumption were found when buildings were grouped by size. Energy audit data were analyzed and it appears that there is bias by audit company on measures recommended to clients. Further research should be performed to cross-analyze this with other attributes, such as building size, vintage, and number of stories. Analysis found that for 281 buildings that were permitted and completed after 2015 and had submitted benchmarking data in 2022, between 81% and 96% (by use group) were found to be in compliance with the 2024 to 2029 NYC BPS emission caps, and between 55% and 89% were in compliance with the 2030-2034 caps. This work is beneficial to the public in helping policymakers and building stakeholders better understand the wide-ranging implications of a BPS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2013

NASA Prediction of Worldwide Energy Resource High Resolution Meteorology Data For Sustainable Building Design

A primary objective of NASA's Prediction of Worldwide Energy Resource (POWER) project is to adapt and infuse NASA's solar and meteorological data into the energy, agricultural, and architectural industries. Improvements are continuously incorporated when higher resolution and longer-term data inputs become available. Climatological data previously provided via POWER web applications were three-hourly and 1x1 degree latitude/longitude. The NASA Modern Era Retrospective-analysis for Research and Applications (MERRA) data set provides higher resolution data products (hourly and 1/2x1/2 degree) covering the entire globe. Currently POWER solar and meteorological data are available for more than 30 years on hourly (meteorological only), daily, monthly and annual time scales. These data may be useful to several renewable energy sectors: solar and wind power generation, agricultural crop modeling, and sustainable buildings. A recent focus has been working with ASHRAE to assess complementing weather station data with MERRA data. ASHRAE building design parameters being investigated include heating/cooling degree days and climate zones.

Chandler, William S.↗
DOE OSTI2018

The Industry Energy Tool (IET): Documentation

Studies of industrial energy use typically mention the sector's heterogeneity and complexity as modelling barriers. One of the more significant impediments to analyzing and building models of U.S. industrial energy use is the lack of current, disaggregated data. The Industry Energy Tool (IET) and its foundational data addresses some of these barriers. The IET uses county-level energy data with stock turnover, energy efficiency, fuel switching, and emissions functionalities to calculate energy use and emissions from agriculture, construction, mining, and manufacturing industries from 2014 to 2050. The IET is an open-source, transparent, and flexible tool for exploring industrial sector energy and emissions scenarios. The IET takes a simplified approach to developing scenario projections of energy and emissions by allowing users to modify pre-populated inputs for achieved energy efficiency, rate of equipment stock turnover, and future penetration renewable electricity. In addition to its transparency and flexibility, the strengths of the IET lie in its geographically- and operationally-disaggregated data set. The IET's foundational data is built on energy data first calculated at the facility- and technology-level for many of the largest energy-using industries. These data are then aggregated to the county and end-use level with other publicly-available data for use in scenarios estimated by the IET.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Guidelines for Datacenter Energy Information System

The purpose of this document is to provide structured guidance to data center owners, operators, and designers, to empower them with information on how to specify and procure data center energy information systems (EIS) for managing the energy utilization of their data centers. Data centers are typically energy-intensive facilities that can consume up to 100 times more energy per unit area than a standard office building (FEMP 2013). This guidance facilitates “data-driven decision making,” which will be enabled by following the approach outlined in the guide. This will bring speed, clarity, and objectivity to any energy or asset management decisions because of the ability to monitor and track an energy management project’s performance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1980

Health and safety: Preliminary comparative assessment of the Satellite Power System (SPS) and other energy alternatives

Data readily available from the literature were used to make an initial comparison of the health and safety risks of a fission power system with fuel reprocessing; a combined-cycle coal power system with a low-Btu gasifier and open-cycle gas turbine; a central-station, terrestrial, solar photovoltaic power system; the satellite power system; and a first-generation fusion system. The assessment approach consists of the identification of health and safety issues in each phase of the energy cycle from raw material extraction through electrical generation, waste disposal, and system deactivation; quantitative or qualitative evaluation of impact severity; and the rating of each issue with regard to known or potential impact level and level of uncertainty.

Habegger, L. J.↗
DOE OSTI2021

Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks

ABSTRACT We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band DES images with linear, logarithmic, and gradient scales, matched with debiased visual classifications from the Galaxy Zoo 1 (GZ1) catalogue, are used to train our CNN models. With a training set including bright galaxies (16 ≤ i < 18) at low redshift (z < 0.25), we furthermore investigate the limit of the accuracy of our predictions applied to galaxies at fainter magnitude and at higher redshifts. Our final catalogue covers magnitudes 16 ≤ i < 21, and redshifts z < 1.0, and provides predicted probabilities to two galaxy types – ellipticals and spirals (disc galaxies). Our CNN classifications reveal an accuracy of over 99 per cent for bright galaxies when comparing with the GZ1 classifications (i < 18). For fainter galaxies, the visual classification carried out by three of the co-authors shows that the CNN classifier correctly categorizes discy galaxies with rounder and blurred features, which humans often incorrectly visually classify as ellipticals. As a part of the validation, we carry out one of the largest examinations of non-parametric methods, including ∼100 ,000 galaxies with the same coverage of magnitude and redshift as the training set from our catalogue. We find that the Gini coefficient is the best single parameter discriminator between ellipticals and spirals for this data set.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Spatial heterogeneity analysis of the effects of urbanization on airport CO2 emissions in China - A geographically weighted approach using nighttime light data

Regional disparities in carbon dioxide (CO2) emissions from airports at the city level are of increasing importance for low-carbon development of the civil aviation sector. However, CO2 emissions from airport operations have rarely been estimated and discussed. We investigate the main driving forces of airport CO2 emissions by using Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models, separately, to investigate whether urbanization drives airport CO2 emissions and to investigate spatial heterogeneity at the city level. Nighttime light (NTL) data from satellite observations are adopted as a proxy for urbanization. We obtained energy consumption data by end-use purpose for 70 airports in China and calculated the CO2 emissions from on-ground airport operations. The median CO2 emissions of the 70 sample airports are estimated to be 15.9 million tonnes for 2015. Results from the GWR model indicate that airport CO2 emissions are affected by five main factors: urbanization, foreign direct investment, the share of tertiary industry in gross domestic output, passenger turnover of civil aviation and passenger turnover of railways. The elasticity of urbanization shows an increasing trend from the east of China to the west. The spatial heterogeneity of the CO2 emissions of the five airport clusters that are located in five urban agglomerations is discussed. In order to achieve effective reductions of CO2 emissions from airports, policy-makers should consider the spatial heterogeneity of the major driving factors of carbon emissions in different regions to avoid carbon lock-in.

Airport CO2 emissions↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2024

BETTER Together

The Standard Energy Efficiency Data (SEED) and Building Efficiency Targeting Tool for Energy Retrofits (BETTER) platforms are both developed by the Department of Energy and work better together. SEED is a database to manage building characteristics and performance data from a variety of sources. BETTER provides simple energy efficiency measure analyses based on high level data about the building or portfolio of buildings. A demonstration of each platform and their integration will be provided. The inputs for BETTER are building type, floor area, location, utility data, and whether PV shall be included in the analysis. The BETTER analysis can be manually set up through the web application or data can be uploaded with a BuildingSync XML file either directly or through the API. SEED can be the source of this data and the data can be sent to BETTER through the SEED application after the BETTER API token has been entered. The benefit of utilizing SEED is that it has connections to many other sources of data such as ENERGY STAR Portfolio Manager, Audit Template, and Salesforce. Therefore, it is likely that a user of SEED will already have the required inputs for BETTER in SEED already and can create BETTER analyses across their whole portfolio in a couple mouse clicks. This is a major time savings and enables decision makers an easy path to identify buildings that should undergo more detailed audits or retrofit pathways.

ASHRAE↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ speed predictions, the vehicle-level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal driving (i.e., human driver) and conventional BTM strategy.

Engineering↗
DOE OSTI2014

Commercial Building Tenant Energy Usage Aggregation and Privacy

A growing number of building owners are benchmarking their building energy use. This requires the building owner to acquire monthly whole-building energy usage information, which can be challenging for buildings in which individual tenants have their own utility meters and accounts with the utility. Some utilities and utility regulators have turned to aggregation of customer energy use data (CEUD) as a way to give building owners whole-building energy usage data while protecting customer privacy. Meter profile aggregation adds a layer of protection that decreases the risk of revealing CEUD as the number of meters aggregated increases. The report statistically characterizes the similarity between individual energy usage patterns and whole-building totals at various levels of meter aggregation.

Data aggregation,. Aggregation threhold selection↗
DOE OSTI2015

Admiralty Inlet Pilot Tidal Project Final Technical Report

This document represents the final report for the Admiralty Inlet Pilot Tidal Project, located in Puget Sound, Washington, United States. The Project purpose was to license, permit, and install a grid-connected deep-water tidal turbine array (two turbines) to be used as a platform to gather operational and environmental data on tidal energy generation. The data could then be used to better inform the viability of commercial tidal energy generation from technical, economic, social, and environmental standpoints. This data would serve as a critical step towards the responsible advancement of commercial scale tidal energy in the United States and around the world. In late 2014, Project activities were discontinued due to escalating costs, and the DOE award was terminated in early 2015. Permitting, licensing, and engineering design activities were completed under this award. Final design, deployment, operation, and monitoring were not completed. This report discusses the results and accomplishments achieved under the subject award.

16 TIDAL AND WAVE POWER↗
DOE OSTI2011

Experimental study of {sup 6}He+{sup 9}Be elastic scattering at low energies

New data for the {sup 6}He+{sup 9}Be reaction at E{sub lab}=16.2 and 21.3 MeV have been taken and analyzed. The effect of the collective couplings to the excited states of the target has been studied by means of coupled-channels calculations, using a double-folding potential for the bare interaction between the colliding nuclei, supplemented with a phenomenological imaginary part of Woods-Saxon type. In addition, three- and four-body continuum-discretized coupled-channels calculations have been performed to investigate the effect of the projectile breakup on the elastic scattering. Both effects, the coupling to target and projectile excited states, are found to affect significantly the elastic scattering. The trivial local polarization potential extracted from the continuum-discretized coupled-channels calculations indicates that continuum couplings produce a repulsive real part and a long-range imaginary part in the projectile-target interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck . III. Combined cosmological constraints

Here we present cosmological constraints from the analysis of two-point correlation functions between galaxy positions and galaxy lensing measured in Dark Energy Survey (DES) Year 3 data and measurements of cosmic microwave background (CMB) lensing from the South Pole Telescope (SPT) and Planck. When jointly analyzing the DES-only two-point functions and the DES cross-correlations with SPT+Planck CMB lensing, we find Ω m = 0.344 ± 0.030 and S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.773 ± 0.016, assuming ΛCDM. When additionally combining with measurements of the CMB lensing autospectrum, we find Ω m = 0.306$^{+0.018}_{-0.021}$ and S 8 = 0.792 ± 0.012. The high signal-to-noise of the CMB lensing cross-correlations enables several powerful consistency tests of these results, including comparisons with constraints derived from cross-correlations only, and comparisons designed to test the robustness of the galaxy lensing and clustering measurements from DES. Applying these tests to our measurements, we find no evidence of significant biases in the baseline cosmological constraints from the DES-only analyses or from the joint analyses with CMB lensing cross-correlations. However, the CMB lensing cross-correlations suggest possible problems with the correlation function measurements using alternative lens galaxy samples, in particular the redMaGiC galaxies and high-redshift MagLim galaxies, consistent with the findings of previous studies. We use the CMB lensing cross-correlations to identify directions for further investigating these problems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

High-Fidelity Solar Irradiance Data: Simple Access to State-of-the-Art Information Accelerates Southeast Asia's Clean Energy Economic Transformation

High quality, robust, and reliable renewable energy resource data is foundational to climate-smart decision making, evidence-based policy planning, and clean energy investment mobilization. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to this critical resource data by providing free, high-fidelity solar resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights several ways the Southeast Asia solar resource data has been used for power system planning and project development in the region.

Advanced Energy Partnership for Asia↗
DOE OSTI2012

National Geothermal Data System: Interactive Assessment of Geothermal Energy Potential in the U.S.

Geothermal-relevant geosciences data from all 50 states (www.stategeothermaldata.org), federal agencies, national labs, and academic centers are being digitized and linked in a distributed online network via the U.S. Department of Energy-funded National Geothermal Data System (NGDS) to foster geothermal energy exploration and development through use of interactive online ‘mashups,’data integration, and applications. Emphasis is first to make as much information as possible accessible online, with a long range goal to make data interoperable through standardized services and interchange formats. An initial set of thirty geoscience data content models is in use or under development to define a standardized interchange format: aqueous chemistry, borehole temperature data, direct use feature, drill stem test, earthquake hypocenter, fault feature, geologic contact feature, geologic unit feature, thermal/hot spring description, metadata, quaternary fault, volcanic vent description, well header feature, borehole lithology log, crustal stress, gravity, heat flow/temperature gradient, permeability, and feature descriptions data like developed geothermal systems, geologic unit geothermal properties, permeability, production data, rock alteration description, rock chemistry, and thermal conductivity. Map services are also being developed for isopach maps, aquifer temperature maps, and several states are working on geothermal resource overview maps. Content models are developed preferentially from existing community use in order to encourage widespread adoption and promulgate minimum metadata quality standards. Geoscience data and maps from other NGDS participating institutions, or “nodes” (USGS, Southern Methodist University, Boise State University Geothermal Data Coalition) are being supplemented with extensive land management and land use resources from the Western Regional Partnership (15 federal agencies and 5 Western states) to provide access to a comprehensive, holistic set of data critical to geothermal energy development. As of September 2011, we have over 34,000 records registered in the system catalog, and 234,942 data resources online, along with scores of Web services to deliver integrated data to the desktop for free downloading or online use. The data exchange mechanism is built on the U.S. Geoscience Information Network (USGIN, http://usgin.org and http://lab.usgin.org) protocols and standards developed as a partnership of the Association of American State Geologists (AASG) and U.S. Geological Survey.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

Technical Note: Influence of the phantom material on the absorbed-dose energy dependence of the EBT3 radiochromic film for photons in the energy range 3 keV–18 MeV

Purpose: Water is the reference medium for radiation therapy dosimetry, but for film dosimetry it is more practical to use a solid phantom. As the composition of solid phantoms differs from that of water, the energy dependence of film exposed within solid phantoms may also differ. The energy dependence of a radiochromic film for a given beam quality Q (energy for monoenergetic beams) has two components: the intrinsic energy dependence and the absorbed-dose energy dependence f(Q), the latter of which can be calculated through a Monte Carlo simulation of radiation transport. The authors used Monte Carlo simulations to study the influence of the phantom material on the f(Q) of the EBT3 radiochromic film (Ashland Specialty Ingredients, Wayne, NJ) for photon beams with energies between 3 keV and 18 MeV. Methods: All simulations were carried out with the general-purpose Monte Carlo code PENELOPE 2011. The geometrical model consisted of a cylindrical phantom, with the film positioned at different depths depending on the initial photon energy. The authors simulated monoenergetic parallel photon beams and x-ray beams from a superficial therapy system. To validate their choice of simulation parameters, they also calculated f(Q) for older film models, EBT and EBT2, comparing with published results. In addition to water, they calculated f(Q) of the EBT3 film for solid phantom materials commonly used for film dosimetry: RW1 and RW3 (PTW-Freiburg, Freiburg, Germany), Solid Water (Gammex-RMI, Madison, WI), and PMMA. Finally, they combined their calculated f(Q) with published overall energy response data to obtain the intrinsic energy dependence of the EBT3 film in water. Results: The calculated f(Q) for EBT and EBT2 films was statistically compatible with previously published data. Between 10 keV and 18 MeV, the variation found in f(Q) of the EBT3 film for water was within 2.3%, with a standard statistical uncertainty less than 1%. If the quantity dose-to-water in the phantom is considered, which is the common practice in radiation dosimetry, the maximum difference of energy dependence for the solid phantoms with respect to water is about 6%, at an energy of 50 keV. Conclusions: The EBT3 film shows a reasonably constant absorbed-dose energy dependence when irradiated in water. If the dose-to-water in the phantom is considered, the maximum difference of EBT3 film energy dependence with the solid phantoms studied with respect to water is about 6% (at an energy of 50 keV). The reported overall energy dependence of the EBT3 film in water at energies below 100 keV is mainly due to the intrinsic energy dependence.

07 ISOTOPE AND RADIATION SOURCES↗