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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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This case study describes how Commonwealth Edison (ComEd) and the city of Chicago worked together to develop a way for commercial building owners to get access to whole-building energy data more efficiently. This would enable building owners to comply with local energy benchmarking ordinances.
The case study describes how Eversource and the cities of Boston and Cambridge, Massachusetts, worked together to develop a way for commercial building owners to get access to whole-building energy data more efficiently. This would enable building owners to comply with local energy benchmarking ordinances.
Human-building interactions significantly influence building energy consumption and affect peak energy demand. For example, heating and cooling contribute 46% of daily peak residential energy demand. Grid-interactive efficient buildings (GEBs) can potentially increase energy-demand flexibility and accelerate the adoption of renewables. However, traditional demand response (DR) programs focused on shedding peak loads disregard the human-building interactions leading to occupant thermal frustration. Specifically, they do not model occupants’ ability to override thermostat controls, nor how indoor environmental conditions and sociocultural factors affect the timing and magnitude of overrides. Studies have found 20% of occupants override thermostat setpoints during DR events longer than 6 hours yet lack detail about the motivation that might guide more successful efforts. Balancing energy-demand flexibility with occupant thermal comfort requires understanding dynamic occupant behavior, the underlying psychophysiological drivers, in the context of homes. This paper presents methods for scalable longitudinal studies of residential occupant behavior dynamics to inform the development of psychophysiological occupant-centric building models. Smart sensors were installed to measure local environmental conditions in 20 homes in two regions of the United States. Just-in-time ecological momentary assessments (EMAs) provided qualitative data on occupant thermal comfort and local environmental conditions not captured in Internet-of-Things (IoT) based studies or existing datasets. Participant interviews provided insight into environmental attitudes, mental models, and the role of economics in comfort and behavior, which in turn may affect thermostat interactions. Based on these data, interventions in the next phase of the study will collect data and monitor occupant behavior during simulated DR events.
The SW-IFL onroad fossil fuel carbon dioxide (FFCO2) emissions data product represents CO2 emissions from the combustion of fossil fuels by motor vehicles (e.g., passenger cars, trucks, buses, motorcycles) traveling on designated roadways. The emissions are represented geographically on each road segment within the state of Arizona spanning the 2017 to 2022 time period. This data product was developed as part of the Southwest Urban Corridor Integrated Field Laboratory (SW-IFL) project, which aims to provide new knowledge and tools that address urban environmental issues by integrating high-resolution observations, modeling, and civic engagement. The emissions data are provided in CSV (input data, ONR_FFCO2_AZ_county.csv) and GeoPackage form (output polyline objects - about 786,000 road segments, XXXX_AZ_v1.1.gpkg) designated by road class (interstates, arterials, collectors, local). The metadata file (Metadata_SW-IFL_Onroad_annualFFCO2_v1.1.docx) provides details about attributes and data formats. The GeoPackage emissions data are provided separately for local roads and nonlocal roads (interstates, arterials, collectors). The method file (Methods_SW-IFL_Onroad_annualFFCO2_v1.1.docx) describes the data processing flow and data sources. Update on 2024-04-17: Updates were made to both the input emission data file (.csv) and output segment-level emission file (.gpkg). There was an update in county-level emission input data (ONR_FFCO2_AZ_county.csv) and the entire road segments were reprocessed to reflect this update.Update on 2024-04-29: Update was made to one output segment-level emission file (Nonlocal_AZ_v1.0.gpkg). There was an error in the AADT values and the data were reprocessed to reflect this update.Update on 2024-10-22: Temporal coverage was extended to include 2022. VMT values were recalculated using new AADT data and the entire road segments were reprocessed to reflect these updates.
The major technological innovations in continuous wave (CW) submillimeter sources which are specifically suitable for application as local oscillators in heterodyne systems are reviewed. A description of the various sources is given which underscores the general principles and operating features for each type of device. Particular emphasis is placed on CW optically pumped lasers, which have had a dramatic impact as widely available sources of narrow linewidth coherent radiation. The state-of-the-art is summarized for these lasers and performance data are presented for several local oscillator packages. Optically pumped lasers are then compared and contrasted with other competing sources such as backward wave oscillators, IMPATT diodes, and Josephson junctions. By comparing their advantages and limitations for use as local oscillators, the potential applications of these different sources are projected. The prospects for increased tunability, reliability, and scalability are briefly considered, and several novel techniques for generating partially tunable radiation using Schottky diode mixers or CW Raman lasers are highlighted.
Research reported during the past few decades has revealed the importance for human sound localization of the so-called 'monaural spectral cues.' These cues are the result of the direction-dependent filtering of incoming sound waves accomplished by the pinnae. One point of view about how these cues are extracted places great emphasis on the spectrum of the received sound at each ear individually. This leads to the suggestion that an effective way of studying the influence of these cues is to measure the ability of listeners to localize sounds when one of their ears is plugged. Numerous studies have appeared using this monaural localization paradigm. Three experiments are described here which are intended to clarify the results of the previous monaural localization studies and provide new data on how monaural spectral cues might be processed. Virtual sound sources are used in the experiments in order to manipulate and control the stimuli independently at the two ears. Two of the experiments deal with the consequences of the incomplete monauralization that may have contaminated previous work. The results suggest that even very low sound levels in the occluded ear provide access to interaural localization cues. The presence of these cues complicates the interpretation of the results of nominally monaural localization studies. The third experiment concerns the role of prior knowledge of the source spectrum, which is required if monaural cues are to be useful. The results of this last experiment demonstrate that extraction of monaural spectral cues can be severely disrupted by trial-to-trial fluctuations in the source spectrum. The general conclusion of the experiments is that, while monaural spectral cues are important, the monaural localization paradigm may not be the most appropriate way to study their role.
The XENON1T collaboration Aprile et al. [2020] has found an excess of electron recoil events in their Science Run 1 data below ~7 keV with a spectral shape consistent with that expected from a solar-axion-induced signal. The claimed statistical significance of the solar-axion model over the null hypothesis is 3.5σ. In this work we provide suggestive evidence for mismodeling in the electron recoil data that may decrease the local significance of the axion model. To reach this conclusion, we search for a signal with the spectral template of the solar axion model, but shifted to higher (unphysical) energies above ~7 keV. We find that the distribution of significances found from this side-band analysis does not follow the expected chi-square distribution. For example, we find a high-significance feature in the data, with a global $p$-value $p≈0.026$, when the solar axion model is shifted upwards in energy by ~15 keV and allowed to have a negative normalization. We argue that such statistical tests, if performed a priori, provide a data-driven way to test and potentially account for systematic uncertainties on the background model in low-threshold dark matter experiments.
Generative Adversarial Network (GAN) – based models have been successfully applied in generating different geological models in the literature. However, it is still challenging to use GAN to generate geological realizations with extremely sparse conditioning data (e.g. several well data), which may be regarded as local noise by GAN during the training process. In this work, we propose a novel conditional Generative Adversarial Neural Operator (cGANO) to tackle this challenge. In cGANO, the mapping between conditioning data and output is established through the U-shaped neural operators (UNO), which better preserves local information. Another advantage of using UNO comes from its grid-independent property, which makes the generation of downscaling stochastic geologic realizations possible. We tested the model performance on the IBDP geostatistical dataset with 100 realizations.
A local electronics manufacturer, the Sterling Manufacturing Company, was presented with the opportunity to supply 30,000 automotive cellular antennas to a European subsidiary of a large U.S. auto manufacturer. Although the company built an antenna that they believed would meet the auto manufacturer's specifications, they were unable to conduct the necessary validation tests in-house. They decided to work with NASA Lewis Research Center's Space Electronics Division, which, as part of its technology development program, evaluates the performance of antennas in its Microwave Systems Lab to assess their capabilities for space communications applications. Data measured in Lewis' Microwave Systems Lab proved that Sterling's antenna performed better than specified by the auto manufacturer.
While green infrastructure (GI) can provide multiple environmental benefits, its role in post-disaster economic and social recovery remains relatively underexplored. This article investigates whether different characteristics of GI, such as size, shape, connectivity, and amenities, affect the resilience of local businesses following Tropical Storm Imelda in Beaumont, Texas. The study utilizes SafeGraph mobility data to analyze foot traffic patterns to local businesses before, during, and after the disaster. FRAGSTATS indices measure GI characteristics (e.g., area, shape index, fractal dimension, proximity) while park features such as sports facilities, playgrounds, water features, and accessibility are cataloged through manual observation. Ordinary Least Squares regression models assess the relationship between park characteristics and post-recovery business performance, controlling for demographic variables including income, race, and poverty levels. Results indicate that certain GI attributes significantly enhance business recovery. Points of interest within walking distance (0.5 miles) of parks demonstrated better post-recovery status compared to those beyond this range. Specifically, parks with larger areas (p < 0.01) and more complex shapes measured by fractal dimension index (p < 0.01) had the strongest positive impact on surrounding businesses' recovery. Interestingly, playgrounds showed a negative correlation with recovery (p < 0.05), likely due to flood damage rendering them unusable during the immediate recovery period. Social vulnerability factors, including higher poverty rates and minority populations, negatively affected recovery outcomes despite park proximity.
Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.
Wide-scale deployment of renewable energy technologies has the potential to significantly reduce greenhouse gas emissions and mitigate the effects of climate change. Many communities have ambitious clean energy goals with targets for locally generated renewable energy. To inform state and local clean energy planning, analysts from the Joint Institute for Strategic Energy Analysis (JISEA) and National Renewable Energy Laboratory (NREL) used data from NREL's State and Local Planning for Energy (SLOPE) platform to compare annual technical generation potential of renewable energy technologies to modeled electricity consumption in every county of the contiguous United States. Annual costs were calculated to produce a 20% share of electricity consumed annually from each technology to examine localized cost effectiveness of a diversified mix of generation sources. For example, combining distributed and utility-scale wind and solar generation can offset the need for storage and nonintermittent fossil resources to achieve high deployment of renewables. This county-level analysis provides insight into where localized renewable energy generation could cost-effectively match annual electricity consumption.
Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.
This report summarizes our recent data derivation efforts on STPSat-6 ZPS plasma measurements, presenting final outcomes. We begin by outlining the methodology developed to determine local magnetic field directions from measured directional intensities of ~20 keV ions, leveraging symmetry in particles’ pitch-angle distributions, and the determined field directions over a storm period are shown with errors analyzed through comparison with NOAA GOES measurements. Next, we further assess the validity of the method and quantify its overall high performance while acknowledging certain caveats. Finally, given local magnetic field directions and ZPS data, we explain how omni-directional fluxes of particles can be derived from ZPS measurements with limited directional coverage, and estimate the related errors under several theoretical scenarios. The methods developed in this study can be applied for processing and augmenting ZPS data for the next, and the insights gained here can also inform instrument design in the future.
The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance.
The development of the coastal-zone-related issues is generating an increasing need for information which is greater in quantity of natural resource data, greater in quality of detail of data, and more frequent in collection of data owing to the need to monitor certain aspects of programs. The array, detail, and frequency of information acquisition required to develop natural resources and to implement and maintain the resulting programs demand improved techniques of data gathering, processing, and interpretation which is conducive to the use of remote-sensing techniques. As Alaska, both in the state and federal domain, gears up to meet the energy-related issues facing the nation there will be a growing role for efforts which adapt state-of-the-art tools to solving existing problems.
In the twelve hours following a worldwide storm, there was a series of at least four magnetospheric substorms, the last and largest of which exhibited an expansion phase onset at approximately 1200 UT. Data from six spacecraft in three general local time groupings (0300, 0700, and 1300 LT) were examined and vector magnetic field data and energetic electron and ion data from approximately 15 keV to 2 MeV were employed.
A system of high-speed digital data networks was developed and installed to support real-time flight simulation at the NASA Langley Research Center. This system, unlike its predecessor, employs intelligence at each network node and uses distributed 10-V signal conversion equipment rather than centralized 100-V equipment. A network switch, which replaces an elaborate system of patch panels, allows the researcher to construct a customized network from the 25 available simulation sites by invoking a computer control statement. The intent of this paper is to provide a coherent functional description of the system. This development required many significant innovations to enhance performance and functionality such as the real-time clock, the network switch, and improvements to the CAMAC network to increase both distances to sites and data rates. The system has been successfully tested at a usable data rate of 24 M. The fiber optic lines allow distances of approximately 1.5 miles from switch to site. Unlike other local networks, CAMAC does not buffer data in blocks. Therefore, time delays in the network are kept below 10 microsec total. This system underwent months of testing and was put into full service in July 1987.