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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Regressing Nuclear Reactor Power Level Using Low-Cost Sensor Network Data

Multisensor networks deployed at nuclear facilities can be leveraged to collect data used as inputs to machine learning models predicting nuclear safeguard relevant information. This work demonstrates an application of this idea by regressing nuclear reactor power levels, a key indicator for nuclear safeguard verification, at the McClellan Nuclear Research Center using data collected by five Merlyn multisensor platforms with LASSO and LSTM models. This work also demonstrates the use of Leave One Node Out to measure the importance of each multisensor for this regression problem providing insight into model explainability and allowing inferential hypotheses about the nuclear facility to be made. This work can be used as a starting point for future development of methods for regression on reactor power levels at nuclear facilities using multisensor network data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Brine Availability Test in Salt (BATS) FY21 Update

This report summarizes the 2021 fiscal year (FY21) status of ongoing borehole heater tests in salt funded by the disposal research and development (R&D) program of the Office of Spent Fuel & Waste Science and Technology (SFWST) of the US Department of Energy’s Office of Nuclear Energy’s (DOE-NE) Office of Spent Fuel and Waste Disposition (SFWD). This report satisfies SFWST milestone M2SF- 21SN010303052 by summarizing test activities and data collected during FY21. The Brine Availability Test in Salt (BATS) is fielded in a pair of similar arrays of horizontal boreholes in an experimental area at the Waste Isolation Pilot Plant (WIPP). One array is heated, the other unheated. Each array consists of 14 boreholes, including a central borehole with gas circulation to measure water production, a cement seal exposure test, thermocouples to measure temperature, electrodes to infer resistivity, a packer-isolated borehole to add tracers, fiber optics to measure temperature and strain, and piezoelectric transducers to measure acoustic emissions. The key new data collected during FY21 include a series of gas tracer tests (BATS phase 1b), a pair of liquid tracer tests (BATS phase 1c), and data collected under ambient conditions (including a period with limited access due to the ongoing pandemic) since BATS phase 1a in 2020. A comparison of heated and unheated gas tracer test results clearly shows a decrease in permeability of the salt upon heating (i.e., thermal expansion closes fractures, which reduces permeability).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments

Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s −1 , which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

36 MATERIALS SCIENCE↗
DOE OSTI2026

CRGTBSO3 TBS Ozone Data

The Tethered Balloon System (TBS) operated for two weeks during a summer IOP of the CoURAGE campaign. This dataset includes data collected from an En-Sci electrochemical cell (ECC) ozonesonde on the TBS. The ozonesonde was connected to an iMet-4RSB radiosonde, and the overall data collected included ozone, relative humidity, temperature, and altitude. The data from the iMet is the same as that found in the TBSMERGED data product. The TBS also had another instrument (iMet XQ2) that collected meteorological data, which may have more accurate relative humidity (RH) data. This ozonesonde data set is intended to complement the TBSMERGED data product, the CRGTBSO3 surface ozone measurements, and the CoURAGE SWARM ozone lidar (TOLNet) measurements from other locations.

Atmospheric relative humidity↗
DOE OSTI2022

Utah FORGE Downhole Geophone Seismic Data (August 2022)

This is a link to downhole geophone data collected by Schlumberger. These data were collected in the Utah FORGE deep seismic monitoring wells 58-32 and 56-32. The format is a standard SEGY and the units are bits. To convert to acceleration (m/s2) multiply by 2.333 x 10-7. Use one of the scripts linked below to use wget commands to pull the data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

A large interlaboratory electron diffraction study of monolayer graphene

Standardisation of data collection and analysis is essential to enable commercialisation of 2D materials in a wide range of technologies. Selected area electron diffraction (SAED) in the transmission electron microscope (TEM) is one of the key methods for distinguishing monolayer from bilayer and few-layer graphene by comparing the 1st and 2nd order diffraction spot intensities. Yet there are many factors that can affect the reliability of data collection and interpretation, causing the measurement of monolayer samples to deviate from the literature boundary condition of $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1 for monolayer graphene (1LG). Here we present the results of a large interlaboratory SAED comparison study, where 15 international laboratories measured and analysed nominally identical samples of chemical vapour deposited graphene. Large variations were observed in the measured ratios of diffraction spot intensities, with the largest variance associated with poor quality SAED data resulting from inadequate specimen handling and storage. To inform the reliable determination of monolayer thickness from SAED patterns we provide a description of best practice for specimen handling, TEM operation, data collection and analysis. This work was undertaken within VAMAS Technical Working Area 41: Graphene and related 2D materials—Project 9, the results of which have been directly incorporated into ISO/TS 21356–2 for the characterisation of graphene sheets. We find that when this methodology is followed, 1LG can be distinguished from bilayer or thicker material with high confidence where analysis of a single SAED pattern gives $I_{\{\bar{2}110\}}$$/$$I_{\{1\bar{1}00\}}$ < 1.2, even in the absence of precise specimen tilting.

2D materials↗
DOE OSTI2021

Short Term Plasticity for Artificial Neural Networks

Achieving efficient learning for AI systems was identified as a major challenge in the DOE's recently released, AI for Science , report. The human brain is capable of efficient and low-powered learning. It is likely that implementing brain-like principles will lead to more efficient AI systems. In this LDRD, I aim to contribute to this goal by creating a foundation for implementing and studying a brain phenomenon termed short term plasticity (STP) in spiking artificial neural networks within Sandia. First, data collected by the Allen Institute for Brain Science (AIBS) was analyzed to see if STP could be classified into types using the data collected. Although the data was inadequate at the time, AIBS has updated their database and created models that could be utilized in the future. Second, I began creating a software package to assess the ability of a Boltzmann machine utilizing STP to sample from national security data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Characterization of wind speed and directional shear at the AWAKEN field campaign site

The American wake experiment (AWAKEN) is taking place in northern Oklahoma, USA, close to the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) atmospheric observatory. The planning for the deployment of the instruments in this observational field campaign required an assessment of the wind characteristics of the site. This paper analyzes long-term data collected by instruments at the ARM SGP observatory to characterize the winds near the AWAKEN site. The analysis shows that this site experiences high wind shear and veer events with a large number of nocturnal low-level jets. A total of 7086 low-level jet wind profiles over 6 years are examined and found to be dominant from the south and southeast. Significant nocturnal wind veer is observed, which causes southerly wind near the surface to become westerly wind aloft. By identifying a strong relationship between atmospheric stability and wind shear, the wind shear at the site is predicted using the Monin–Obukhov similarity theory (MOST) and validated with the observational data collected by a scanning Doppler lidar. The results show that wind speed at a height of 91 m, a proxy hub height for wind turbines in this area, can be predicted from data collected at a height of 10 m with a bias of −0.35 and 0.65 m s−1 in unstable and stable atmospheric boundary layers, respectively. The bias of the predicted wind speed is mostly in the region of low wind speed, and wind speed above 5 m s−1 at a height of 91 m can be predicted with a bias of less than 0.2 m s−1, and the limitations of the MOST in predicting winds during the stably stratified boundary layer is well-observed.

17 WIND ENERGY↗
DOE OSTI2022

High-Resolution Floating Solar PV Data

This dataset contains over two years of 1-minute resolution data collected from four floating solar sites, as well as data from a land-based PV system co-located with one of the floating sites. The dataset includes highly granular module temperature measurements - five modules per floating site, with three sensors per module, totaling 15 module temperature sensors per floating site. In addition to the module temperature data, meteorological data collected at the floating sites is also included, along with traditional PV system-level parameters. The data is intended for analysis of solar energy production, efficiency, and performance degradation over time. For information about the data file usage see the "README" resource below. See "Metadata File" for information about individual files and other metadata information.

Array↗
DOE OSTI2026

WholeTraveler Anonymized Data Phase 1

Phase 1 of the WholeTraveler Study data collection consisted of an online-only survey. This survey captured data on three categories of observable variation in the population relevant to transportation decisions. First, the survey collected traditional demographic data such as age, gender, income, and education level. Second, it collected data across personality, psychological, and preference categories. This included: 1. The "Big Five" inventory personality traits: openness to new experience, conscientiousness, extroversion, agreeableness, and neuroticism; 2. Risk and time preferences; and 3. Environmental preferences. Third, the survey collected data on historical behavior patterns including: 1. Adoption of (as well as interest in) new technologies or innovations (e.g., smartphones, PEVs, solar panels, adaptive cruise control [ACC]); 2. Car ownership history and current car ownership status; 3. Recent mode use across different time scales (e.g., previous week, previous month, previous year); and 4. Timing of major life events such as starting a family as well as overall lifecycle trajectory patterns. Data from Phase 1 and Phase 2 are linked by a unique respondent identifier. Anonymized versions of the Phase 1 and Phase 2 data are both available on Livewire.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

EcoSpec: Highly Equipped Tower-Based Hyperspectral and Thermal Infrared Automatic Remote Sensing System for Investigating Plant Responses to Environmental Changes

Despite an advanced ability to forecast ecosystem functions and climate at regional and global scales, little is known about relationships between local variations in water and carbon fluxes and large-scale phenomena. To enable data collection of local-scale ecosystem functions to support such investigations, we developed the EcoSpec system, a highly equipped remote sensing system that houses a hyperspectral radiometer (350–2500 nm) and five optical and infrared sensors in a compact tower. Its custom software controls the sequence and timing of movement of the sensors and system components and collects measurements at 12 locations around the tower. The data collected using the system was processed to remove sun-angle effects, and spectral vegetation indices computed from the data (i.e., the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Photochemical Reflectance Index (PRI), and Moisture Stress Index (MSI)) were compared with the fraction of photochemically active radiation (fPAR) and canopy temperature. The results showed that the NDVI, NDWI, and PRI were strongly correlated with fPAR; the MSI was correlated with canopy temperature at the diurnal scale. These correlations suggest that this type of near-surface remote sensing system would complement existing observatories to validate satellite remote sensing observations and link local and large-scale phenomena to improve our ability to forecast ecosystem functions and climate. The system is also relevant for precision agriculture to study crop growth, detect disease and pests, and compare traits of cultivars.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School

By using the same occupant schedule for all spaces, a building level occupancy schedule in building energy modelling can reduce the cost and time of data collection, especially for large-scale simulations or when detailed occupancy data cannot be obtained. However, by describing the unique occupancy status in each space, a space level schedule can better reflect real-world scenarios. This research investigates the energy prediction impact of a space level occupancy schedule for primary school modelling in 16 ASHRAE climate zones. The results show that when switched from a building level to a space level occupancy schedule, the energy prediction difference is between -1.0% and 0.8%. Generally, the predicted building energy consumption using a space level occupancy schedule is higher than when using a building level occupancy schedule in hot and warm climate zones, but lower in other climate zones.

building energy model↗
DOE OSTI2020

NLR OpenPATH™ (National Laboratory of the Rockie's Open Platform for Agile Trip Heuristics [SWR-20-73]

National Laboratory of the Rockies' Open Platform for Agile Trip Heuristics (NLR OpenPATH™) enables people to track their travel modes—by car, bus, bike, walking, etc.—and measure their associated energy use. Formerly known as e-mission, the NLR OpenPATH tool features continuous data collection and analysis via a smart phone app backed by a server and automated data processing. Its open nature enables transparent data collection and analysis while allowing for continuous improvement coupled with accessibility and adaptability by others. NLR OpenPATH empowers communities to collect and understand their own travel data while achieving place-based, locally relevant mobility goals.

Shankari, K.↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Sequential Design of Experiments for Pilot Testing of Novel Solvent System

The CCSI2 program is supporting a six-month test campaign at the National Carbon Capture Center (NCCC) for evaluation of a novel water-lean solvent. This presentation describes CCSI2’s efforts in process modeling of the solvent system for both coal and natural gas-based flue gas sources and initial uncertainty quantification (UQ) work to estimate parametric uncertainty in key sub-models of interest (e.g., thermodynamics, mass transfer, reaction kinetics). Moreover, perspective is provided on how UQ and sequential design of experiments (SDoE) tools are used to assess the impact of model uncertainty on projected process performance, use this information to optimize data collection during the campaign, and refine process models through data collection. This framework is expected to reduce the overall model uncertainty, and thus risk associated with scale-up as the process moves towards commercialization.

Morgan, Joshua↗
DOE OSTI2022

Source Term Analysis of Xenon (STAX): An effort focused on differentiating man-made isotope production from nuclear explosions via stack monitoring

An overview of the hardware and software developed for the Source Term Analysis of Xenon (STAX) project is presented which includes the data collection from two stack monitoring systems installed at medical isotope production facilities, infrastructure to transfer data to a central repository, and methods for sharing data from the repository with users. STAX is an experiment to collect radioxenon emission data from industrial nuclear facilities with the goal of developing a better understanding of the global radioxenon background and the effect industrial radioxenon releases have on nuclear explosion monitoring. The final goal of this work is to utilize collected data along with atmospheric transport modeling to calculate the contribution of a peak or set of peaks detected by the International Monitoring System (IMS) to provide desired discriminating information to the International Data Centre (IDC) and National Data Centers (NDCs). Types of data received from the STAX equipment are shown and collected data was used for a case study to predict radioxenon concentrations at two IMS stations closest to the Institute for RadioElements (IRE) in Belgium. The initial evaluation of results indicate that the data is very valuable to the nuclear explosion monitoring community.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Darshan for HEP applications

Modern HEP workflows must manage increasingly large and complex data collections. HPC facilities may be employed to help meet these workflows’ growing data processing needs. However, a better understanding of the I/O patterns and underlying bottlenecks of these workflows is necessary to meet the performance expectations of HPC systems.Darshan is a lightweight I/O characterization tool that captures concise views of HPC application I/O behavior. It intercepts application I/O calls at runtime, records file access statistics for each process, and generates log files detailing application I/O access patterns.Typical HEP workflows include event generation, detector simulation, event reconstruction, and subsequent analysis stages. A study of the I/O behavior of the ATLAS simulation and filtering stage, and the CMS simulation workflow using Darshan is presented, including insights into the I/O operations and data access size.

Wang, Rui↗