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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improved Gamma-Ray Point Source Quantification in Three Dimensions by Modeling Attenuation in the Scene

We discuss using a series of detector measurements taken at different locations to localize a source of radiation as a well-studied problem. The source of radiation is sometimes constrained to a single point-like source, in which case the location of the point source can be found using techniques such as maximum likelihood. Recent advancements have shown the ability to locate point sources in 2D and even 3D, but few have studied the effect of intervening material on the problem. In this work we examine gamma-ray data taken from a freely moving system and develop voxelized 3-D models of the scene using data from the onboard LiDAR. Ray casting is used to compute the distance each gamma ray travels through the scene material, which is then used to calculate attenuation assuming a single attenuation coefficient for solids within the geometry. Parameter estimation using maximum likelihood is performed to simultaneously find the attenuation coefficient, source activity, and source position that best match the data. Using a simulation, we validate the ability of this method to reconstruct the true location and activity of a source, along with the true attenuation coefficient of the structure it is inside, and then we apply the method to measured data with sources and find good agreement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Machine Learning-Based Extreme Data Reduction for Prompt Supernova Pointing at DUNE

One of the goals of the Deep Underground Neutrino Experiment (DUNE) is to use the massive underground liquid argon time projection chamber (LArTPC) detectors at its far site for multimessenger astronomy (MMA), in the detection of neutrinos from core-collapse supernovae (SNe). Its current baseline trigger strategy detects activity in the detector that is consistent with supernova (SN) neutrinos and saves the raw data for further offline analysis but provides no prompt pointing information crucial for optical follow-ups by other observatories. This approach is based on the assumption that prompt pointing determination using raw data is computationally prohibitive. In this article, we demonstrate a proof-of-concept based on applying extreme data reduction on the buffered SN data in the DUNE data acquisition (DAQ) system’s front-end computers using a machine learning (ML) workflow. This reduces the data by ~5 orders of magnitude, allowing a full track reconstruction to be carried out quickly on a single server. The total time to perform the ML-based data reduction and the full track reconstruction is less than the time to transfer the SN data back to Fermilab or a high-performance computing (HPC) center. This shows that prompt processing of raw SN data is possible and, in fact, trivial once the data have been reduced to reject radiological backgrounds, paving the way to a high-quality SN pointing trigger that is based on fully reconstructed data instead of trigger primitives (TPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

42 ENGINEERING↗
DOE OSTI2020

Predicting the Cetane Number, Yield Sooting Index, Kinematic Viscosity, and Cloud Point for Catalytically Upgraded Pyrolysis Oil Using Artificial Neural Networks

Abstract The conversion of biomass using fast pyrolysis has the potential to be significantly less expensive at scale compared to alternative methods such as fermentation and gasification. Selective upgrading of the products of fast pyrolysis through chemical catalysis produces compounds with lower oxygen content and lower acidity; however, identifying the specific catalytic pathways for producing viable fuels and fuel additives often requires a trial-and-error approach. Specifically, key properties of the compounds must be experimentally tested to evaluate the viability of the resultant compounds. The present work proposes predictive models constructed with artificial neural networks (ANNs) for cetane number (CN), yield sooting index (YSI), kinematic viscosity (KV), and cloud point (CP), with blind test set median absolute errors of 5.14 cetane units, 3.36 yield sooting index units, 0.07 millimeters squared per second, and 4.89 degrees Celsius, respectively. Furthermore, the cetane number, yield sooting index, kinematic viscosity, and cloud point were predicted for over three hundred expected products from the catalytic upgrading of pyrolysis oil. It was discovered that 130 of these compounds have predicted cetane numbers greater than 40, with four of these compounds possessing predicted yield sooting index values significantly less than that of diesel fuel and predicted viscosities and cloud points comparable to that of diesel fuel.

09 BIOMASS FUELS↗
DOE OSTI2021

Structure Perception in 3D Point Clouds

Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projections of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rotation, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under rotation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns perceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗
DOE OSTI2021

A Theoretical Model for Computing Freezing Point Depression of Lithium-Ion Battery Electrolytes

Reliable prediction of freezing point depression in liquid electrolytes will accelerate the development of improved Li-ion batteries which can operate in low temperature environments. In this work we establish a computational methodology to calculate activity coefficients and liquidus lines for battery-relevant liquid electrolytes. Electronic structure methods are used in conjuction with classical molecular dynamics simulations and theoretical expressions for Born solvation energy, ion-atmosphere effects from Debye-Hückel theory and solvent entropic effects. The framework uses no a priori knowledge beyond neat solvent properties and the concentration of salt. LiPF 6 in propylene carbonate (PC), LiPF 6 in dimethyl carbonate (DMC) and LiClO 4 in DMC are investigated up to 1 molal with accuracy better than 3 °C when compared to experimental freezing point measurements. Finally, we find that the difference in freezing point depression between the propylene carbonate-based electrolyte and the dimethyl carbonate electrolytes originates from the difference in the solvent dielectric constant.

25 ENERGY STORAGE↗
DOE OSTI2023

Automatic Point Cloud Building Envelope Segmentation (AutoCuBES)

The Auto-CuBES algorithm is based on unsupervised machine learning that automatically labels 3D point cloud data and reduces the time spent in manual segmentation. The algorithm can process high-resolution point clouds and generate a wire-frame building envelope model with a small set of calibration parameters. The algorithm inputs a 3D point cloud generated by commonly available surveying equipment and outputs a wire-frame model of the building envelope. Unsupervised machine learning methods were used to identify facades, windows, and doors while minimizing the number of calibration parameters.

Puente, BryanMaldonado↗
DOE OSTI2026

Equivalent Point Sources for Simulating Nuclear/Radiological Search Operations

Modeling and simulation can be used to estimate the detection of different types and strengths of radioactive sources. Simulations of shielded gamma-ray sources in complex environments, such as searches in urban areas using vehicle-mounted detectors, can demonstrate what source strengths and shielding combinations should be able to be detected. For faster calculations, gamma-emitting sources that could have different shielding layers can be simulated ahead of time, resulting in equivalent point source emission distributions that can be used in larger simulation models. Larger-scale models will then run faster using equivalent point sources because the shielding layers around the sources have already been accounted for. Accompanying this paper are 260 equivalent point source emission files for 72 sources with different shielding layers, formatted for three different Monte Carlo codes. Users wanting to request versions of the SCALE and/or MCNP software can find details here: https://rsicc.ornl.gov/

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Tipping points emerge from weak mutualism in metacommunities

The coexistence of obligate mutualists is often precariously close to tipping points where small environmental changes can drive catastrophic shifts in species composition. For example, microbial ecosystems can collapse by the decline of a strain that provides an essential resource on which other strains cross-feed. Here, we show that tipping points, ecosystem collapse, bistability and hysteresis arise even with very weak (non-obligate) mutualism provided the population is spatially structured. Based on numeric solutions of a metacommunity model and mean-field analyses, we demonstrate that weak mutualism lowers the minimal dispersal rate necessary to avoid stochastic extinction, while species need to overcome a mean threshold density to survive in this low dispersal rate regime. Our results allow us to make numerous predictions for mutualistic metacommunities regarding tipping points, hysteresis effects, and recovery from external perturbations, and let us draw general conclusions for ecosystems even with random, not necessarily mutualistic, interactions and systems with density-dependent dispersal rather than direct mutualistic interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗
DOE OSTI2023

Charged point defect benchmark of Hybrid and GGA-PBE

Data used by the publication "High-throughput calculations of charged point defect properties with semi-local density functional theory - performance benchmarks for materials screening applications." This work presented an in-depth benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We considered three different a-posteriori correction sets for semi-local calculations, implemented in a fully automated workflow, and consider the qualitative and quantitative differences for four different categories of defect information: thermodynamic transition levels, formation energies, fermi levels, and dopability limits. We highlighted the type of qualitative information about point defect properties that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy that can be achieved by these approaches.

Broberg, Danny↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-WPT Winous Point North Marsh

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-WPT Winous Point North Marsh. This is the FLUXNET version of the carbon flux data for the site US-WPT Winous Point North Marsh produced by applying the standard ONEFlux (1F) software. Site Description - The marsh site has been owned by the Winous Point Shooting Club since 1856 and has been managed by wildlife biologists since 1946. The hydrology of the marsh is relatively isolated by the surrounding dikes and drainages and only receives drainage from nearby croplands through three connecting ditches. Since 2001, the marsh has been managed to maintain year-round inundation with the lowest water levels in September. Within the 0–250 m fetch of the tower, the marsh comprises 42.9% of floating-leaved vegetation, 52.7% of emergent vegetation, and 4.4% of dike and upland during the growing season. Dominant emergent plants include narrow-leaved cattail (Typha angustifolia), rose mallow (Hibiscus moscheutos), and bur reed (Sparganium americanum). Common floating-leaved species are water lily (Nymphaea odorata) and American lotus (Nelumbo lutea) with foliage usually covering the water surface from late May to early October.

Chen, Jiquan↗
DOE OSTI2026

AmeriFlux FLUXNET-1F CA-TPA Ontario Turkey Point Observatory Agricultural Site

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-TPA Ontario Turkey Point Observatory Agricultural Site. This is the FLUXNET version of the carbon flux data for the site CA-TPA Ontario Turkey Point Observatory Agricultural Site produced by applying the standard ONEFlux (1F) software. Site Description - This agricultural flux tower site is located about 15 km southwest of Simcoe in southern Ontario, Canada. It was planted with corn (Zea mays) in 2020 and 2021, sweet potato (Ipomoea batatas) in 2022 and tobacco (Nicotiana tabacum) in 2023. The site is part of Turkey Point Environmental Observatory (TPEO). The establishment of the agricultural site has allowed TPEO to become representative of the major biomes in the Great Lakes region, encompassing coniferous and deciduous forests, as well as agricultural crops. The soil at this agricultural site is well-drained fine sandy loam. The area has a humid continental climate and has one of the longest-growing seasons in Canada with at least 150–160 frost-free days in a year.

Arain, M. Altaf [McMaster University]↗
DOE OSTI2026

AmeriFlux CA-GL3 Long Point

This is the AmeriFlux version of the carbon flux data for the site CA-GL3 Long Point. Site Description - Long Point Lighthouse is located at the end of Long Point on Lake Erie. The eddy covariance instrumentation is located on the historic lighthouse, completed in 1916, and instrumented with eddy covariance data in 2012 by a network of scientists from both US and Canada (eventually to be called the Great Lakes Evaporation Network (GLEN)). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community.

Spence, Chris [Environment and Climate Change Cana↗
DOE OSTI2022

Polymer Retention “Tailing” Phenomenon Associated with the Milne Point Polymer Flood

Summary Associated with the Milne Point polymer flood (on Alaska’s North Slope), this paper explores the unusual shape of hydrolyzed polyacrylamides (HPAM) breakout/propagation during dynamic polymer retention measurements in Milne Point core material. In contrast to conventional expectations, polymer retention does not delay the initial polymer arrival at the end of a Milne Point core. However, after effluent polymer concentrations rapidly rise to at least 50% of the injected value, the concentration gradually “tails” up over many pore volumes (PVs) before it finally achieves the injected value. To understand the origin and significance of this behavior, a wide range of core experiments were performed, including substantial variations in polymer concentration and molecular weight, core length, preservation state, sand grain size, and mineral composition. Illite was identified as primarily responsible for the tailing phenomenon. This phenomenon has important consequences that must be considered when projecting the performance of the field project. This work suggests that mineralogy analysis (especially for illite and kaolinite) may reveal whether tailing should be accounted for during simulations of polymer propagation/retention in a given field application.

Engineering↗
DOE OSTI2020

Validation of the Mach Stem Triple Point

Among the more complex characteristics of an explosion’s shock front is the creation of the Mach stem and its triple point. The scientific footage of the US Atmospheric Nuclear Weapons Testing Program has a film that may show the locus of points formed by the triple point, allowing us to visually investigate the trajectory of these shockwaves. During a nuclear blast, once the X-ray diffusion process ends, an incident shockwave forms in all directions. When the incident shockwave contacts the ground, a reflected shockwave is created. The overall magnitude and impact of these two shockwaves are proportional to the increasing total pressure and temperature, allowing the disturbances to move faster than the local speed of sound.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Gimballed Tracking Mount Pointing Angle Qualification

Tonopah Test Range (TTR), in support of its testing mission and modernization effort acquired a fleet of new gimballed tracking mounts (GTMs) manufactured by BAE Systems. The new GTMs can be operated remotely during flight tests and provide near real-time target tracking data. Furthermore, test vehicle Time-Space-Position-Information (TSPI) is evaluated using post-test synchronized imagery and pointing angle measurements acquired from each tracking mount. To comply with the Nuclear Enterprise Assurance Program (NEAP), all measurements devices must be certified. In keeping with the NEAP program, qualification of the new GTMs have been assessed to confirm that their pointing angle measurements produce acceptable TSPI results. This study only evaluated the four GTMs as a stand-alone solution and found that the GTMs meet their performance requirement of 0.006 degrees RMS error (or less) for post-processed pointing angles and produced TSPI solution with error volumes on the order of one meter or less. The new GTMs will be utilized in combination with existing optical tracking mounts, which will only improve the accuracy of the resulting TSPI data product. Details regarding the approach, analysis, summary results, and conclusions are presented.

42 ENGINEERING↗
DOE OSTI2025

2024 Results for Avian Monitoring at the Technical Area 36 Minie Site, Technical Area 39 Point 6, Technical Area 16 Burn Ground, and DARHT at Los Alamos National Laboratory

Los Alamos National Laboratory (LANL) biological subject matter experts in the Environmental Protection and Compliance Division initiated a multi-year program in 2013 to monitor avifauna (birds) at two open detonation sites and one open burn site on LANL property. Additional monitoring began in 2017 at a third firing site, the Dual-Axis Radiographic Hydrodynamic Test (DARHT) Facility. In this annual report, we compare monitoring results from these efforts among years to identify and evaluate firing and open burn site impacts on the local bird community. The objectives of this study are • to determine whether LANL operations impact bird abundance, species richness, or diversity; • to examine occupancy and nest success of secondary-cavity nesting birds that use nest boxes; and • to examine chemical concentrations (such as radionuclides, inorganic elements, and/or organic compounds) in nonviable eggs and deceased nestlings that are collected opportunistically with the upper-level bounds of background concentrations, when available. During May through July 2024, LANL biologists completed multiple avian point count surveys at each of the following treatment sites: • Technical Area (TA) 36 Minie Site, • TA-39 Point 6, • TA-16 Burn Ground, and • DARHT. We recorded a total of 1,088 birds that represented 65 species at the four treatment sites and compared these results with data from their associated control sites. In 2024, abundance and species richness at treatment and control sites continued to trend similarly from year to year, with minor random deviations expected from bird communities. Species richness at firing sites differed little from the previous year’s values. Two new bird species were observed at the firing sites—cedar waxwing (Bombycilla cedrorum) and pinyon jay (Gymnorhinus cyanocephalus). Shannon diversity values at TA-36 Minie Site, TA-39, and DARHT were statistically higher than one or more of their associated controls. Annual species diversity at treatment sites was high in 2024 across all firing sites relative to similar habitat control sites. We also monitored avian nest boxes to compare occupancy and nest success data from nest boxes at treatment sites with the overall avian nest box monitoring network and against a subset of relevant control sites. Nest box success has decreased at both treatment and control sites since monitoring began, suggesting that overlapping climatic factors are responsible for patterns of declining nest success. In 2024, nonviable avian eggs and one nestling were opportunistically collected at Bandelier National Monument, TA-16 Burn Ground, TA-36 Minie, TA-39 Point 6, and DARHT. All egg samples and the one nestling sample were evaluated for per- and polyfluoroalkyl substances, which were detected from all locations, including the control site at Bandelier National Monument. Overall results from 2024 continue to suggest that operations at the four treatment sites are not negatively impacting bird populations. This long-term project will continue to monitor for any changes over time.

54 ENVIRONMENTAL SCIENCES↗