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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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A grid spectroscopy measurement performed on a 200nm PbTiO3 thin film to acquire local piezoresponse hysteresis loops. This dataset is associated with the 2021 Smoky Mountains Data Challenge.
Delayed neutrons are of fundamental importance in the field of nuclear reactor dynamics and control. However, the precursor yield fraction for a given nuclear reactor are dependent on the properties of the reactor. Thus, in-pile experiments, such as oscillation experiments are conducted in order to measure those values. In this work, an alternative analysis of the piston oscillation experiments that have been conducted in the MINERVE reactor in 2013 is performed. A new method which evolves effective terms that cancel out undesired drifts of the flux during the experiments is presented. The evaluation of the uncertainty on the values of the response function is also presented. Moreover, the effective delayed neutron fraction β{sub eff} is evaluated and is compared to results in previous works. As the analysis has led to an estimation of β{sub eff} with a large uncertainty, it has been deduced that the oscillation experiments that have been conducted in MINERVE are not a reliable method of experimentation to determine the value of β{sub eff} in the reactor and that the noise experiments are better suited for that purpose.
The angular dependance of the transverse Raman scattering in potassium dihydrogen phosphate (KDP) and its deuterated analogue (DKDP) for the entire range of crystal configurations suitable for laser beam polarization control has been investigated via experimental and modeling tools. This work was made possible by simultaneously rotating a spherical sample and the pump polarization to effectively measure the angular dependance of the transverse Raman signal in 360°. This novel method, which is applicable for the investigation of the Raman scattering in optically anisotropic materials, demonstrates that the spontaneous Raman scattering signal exhibits strong angular dependence that is modulated by depolarization and polarization rotation effects generated as the Raman signal traverses the material due to its birefringence. The results show that the total signal generated by the pump beam is the sum of the signals generated by the two components that have polarization parallel and orthogonal to the optic axis. The peak signal intensity, which is of importance for high-power laser applications, depends on the orientation of the optic axis and can vary by a factor of about 2. The excellent agreement between experimental data and modeling results validates the associated models and enables one to consider optimal crystal cut designs for specific applications.
This is the regional dataset compilation for the INnovative Geothermal Exploration through Novel Investigations Of Undiscovered Systems (INGENIOUS) project. The primary goal of this project is to accelerate discoveries of new, commercially viable hidden geothermal systems while reducing the exploration and development risks for all geothermal resources. These datasets will be used in INGENIOUS as input features for predicting geothermal favorability throughout the Great Basin study area. Datasets consist of shapefiles, geotiffs, tabular spreadsheets, and metadata that describe: 2-meter temperature probe surveys, quaternary faults and volcanic features, geodetic shear and dilation models, heat flow, magnetotellurics (conductance), magnetics, gravity, paleogeothermal features (such as sinter and tufa deposits), seismicity, spring and well temperatures, spring and well aqueous geochemistry analyses, thermal conductivity, and fault slip and dilation tendency. For additional project information, see the INGENIOUS project site linked in the submission. Terms of use: These datasets are provided "as is", and the contributors assume no responsibility for any errors or omissions. The user assumes the entire risk associated with their use of these data and bears all responsibility in determining whether these data are fit for their intended use. These datasets may be redistributed with attribution (see citation information below). Please refer to the license information on this page for full licensing terms and conditions.
This document is the final report for project OR16-ML-3DTomography-PD3Jb, “3D Tomography and Image Processing Using Fast Neutrons.” This project was motivated by the availability of three-dimensional (3D) tomographic neutron transmission and induced-reaction data following the development of associated-particle imaging (API) systems with two-dimensional (2D) or “area” neutron detectors such as the Advanced Portable Neutron Imaging System, which is capable of simultaneous measurement of transmission and induced-reaction neutrons.
This report details the modeling efforts consistent with the HYBRID repository of the Integrated Energy Systems (IES) program, motivated to develop information data tables characterizing the thermodynamic losses associated with transporting thermal energy over large distances. The described model establishes a methodology to currently characterize the impacts of certain parameters such as distance, piping geometry, and heat transfer fluid among others as well as sets up ability to interconnect the delivery model into Modelica IES simulations in the future. This report describes the modeling efforts completed and describes some of the future studies that will be done using results produced by the modeling efforts.
Surface tension affects all aspects of fluid flow in porous media. Through measurements of surface tension interaction under multiphase conditions, a relative permeability relationship can be determined. Relative permeability is a numerical description of the interplay between two or more fluids and the porous media they flow through. It is a critical parameter for various tools used to characterized subsurface multiphase flow systems, such as numerical simulation for oil and gas development, carbon sequestration, and groundwater contamination remediation. Therefore, it is critical to get a good statistic distribution of relative permeability in the porous media under study. Empirical relationships for determining relative permeability from capillary pressure are already well established but do not provide the needed flexibility required to match laboratory derive relative permeability relationships. By expanding the existing methods for calculating relative permeability from capillary pressure data, it is possible to create both two and three-phase relative permeability relationships. Existing laboratory-measured relative permeability data along with mercury intrusion capillary (MICP) data coupled with interfacial tension and contact angle measurements were used to determine the efficacy of this approach to relative permeability curve creation. The relative permeability relationships determined with this method were fit to existing laboratory data to elucidate common fitting parameters that were then used to create relative permeability relationships from MICP data that does not have an associated laboratory-measured relative permeability relationship.
The socioeconomic impacts of pipeline incidents have escalated over the past three decades, revealing the limitation of traditional risk modeling methods when applied to extensive pipeline networks. This research aims to develop machine learning (ML) models that effectively identify, rank, and predict the diverse hazards and socioeconomic consequences associated with pipeline incidents. Utilizing historical data on pipeline incidents alongside weather and oceanographic data from the 1980s onward, the Houston metropolitan area serves as a testbed for the proposed methodologies. The research segments the combined datasets into three consecutive periods, demonstrating the efficacy of the updated model in predicting future events, particularly concerning precipitation rate data. Despite the challenges posed by a relatively limited dataset, local-level ML modeling offers valuable insights into the spatial and temporal dynamics of multiple hazards that contribute to pipeline incidents. These findings hold significant implications for future research, particularly in understanding and mitigating risks in various locations across the Gulf Coast and other coastal regions.
This report details the production of tensile specimens from in-service irradiated baffle former bolts (BFBs) and presents the results of a pilot in-situ SEM/EBSD test conducted at Oak Ridge National Laboratory’s Low Activation Materials Development and Analysis (LAMDA) facility. The SEM/EDS results confirmed that the material is AISI 316L steel, not AISI 347, and that no Nb addition was detected in several analyzed locations. EBSD analysis of the microstructure revealed in-grain misorientation gradients indicative of cold work, likely caused by material processing, and no retained ferrite was identified. During straining, dislocation channel formation was the primary deformation mechanism, with twinning also observed in favorably oriented grains at local strains as small as ~1.5%. No strain-induced phase instability was observed, likely due to the fact that the Ni and Cr content reached the upper limits for the 316L steel specification. Multiple microfracture events were observed during the tensile test. Fine strain-induced pores formed at the channel-grain boundary intersection points, while coarser microcracks were associated with non-metallic inclusions. Data analysis is ongoing, and additional tests are planned in the near future to provide more statistics and details.
In March of 2005, the Department of Energy (DOE), National Nuclear Security Administration (NNSA), Los Alamos Site office (LASO) requested the U.S. Army Corps of Engineers Albuquerque District (USACE) to identify and delineate wetlands at Los Alamos National Laboratory (LANL). The results of this study were intended to support the preparation of the Supplemental Site-Wide Environmental Impact Statement (EIS) and various mitigative measures that would become part of the National Environmental Policy Act (NEPA) compliance process for LANL. The term “wetland” will be used throughout the report to refer to the potential wetlands listed in Table 1. Many of these potential "wetlands” were not determined to be wetlands based on 2025 data.
The purpose of this pilot study was to determine if military service members with histories of hundreds to thousands of low-level blast exposures (i. e., experienced breachers) had different levels of serum and neuronal-derived extracellular vesicle (EV) concentrations of interleukin (IL)-6, IL-10, and tumor necrosis factor alpha (TNFα), compared to matched controls, and if these biomarkers related to neurobehavioral symptoms. Participants were experienced breachers (n = 20) and matched controls without blast exposures (n = 14). Neuronal-derived EVs were isolated from serum and identified with mouse anti-human CD171. Serum and neuronal-derived EVs were analyzed for IL-6, IL-10, and TNFα using an ultra-sensitive assay. Serum TNFα concentrations were decreased in breachers when compared to control concentrations (p < 0.01). There were no differences in serum concentrations of IL-6, IL-10, or the IL-6/IL-10 ratio between breachers and controls (p's > 0.01). In neuronal-derived EVs, TNFα and IL-6 levels were increased in breachers compared to controls (p's < 0.01), and IL-10 levels were decreased in the breacher group compared to controls (p < 0.01). In breachers the IL-6/IL-10 ratio in neuronal-derived EVs was higher compared to controls, which correlated with higher total Rivermead Post-concussion Questionnaire (RPQ) scores (p's < 0.05). These findings suggest that exposure of personnel to high numbers of low-level blast over a career may result in enduring central inflammation that is associated with chronic neurological symptoms. The data also suggest that peripheral markers of inflammation are not necessarily adequate surrogates for central neuroinflammation.
Wood-based pellets are produced in the southeastern United States (SE US) and shipped to Europe for the generation of heat and power. Effects of pellet production on selected Sustainability Development Goals (SDGs) are evaluated using industry information, available energy consumption data, and published research findings. Challenges associated with identifying relevant SDG goals and targets for this particular bioenergy supply chain and potential deleterious impacts are also discussed. We find that production of woody pellets in the SE US and shipments to displace coal for energy in Europe generate positive effects on affordable and clean energy (SDG 7), decent work and economic growth (SDG 8), industry innovation and infrastructure (SDG 9), responsible consumption and production (SDG 12), and life on land (SDG 15). Primary strengths of the pellet supply chain in the SE US are the provisioning of employment in depressed rural areas and the displacement of fossil fuels. Weaknesses are associated with potential impacts on air, water, and biodiversity that arise if the resource base and harvest activities are improperly managed. The SE US pellet supply chain provides an opportunity for transition to low-carbon industries and innovations while incentivizing better resource management.
Although it is generally accepted that massive galaxies form in a two-phased fashion, beginning with a rapid mass buildup through intense starburst activities followed by primarily dry mergers that mainly deposit stellar mass at outskirts, the late time stellar mass growth of brightest cluster galaxies (BCGs), the most massive galaxies in the universe, is still not well understood. Several independent measurements have indicated a slower mass growth rate than predictions from theoretical models. We attempt to resolve the discrepancy by measuring the frequency of BCGs with multiple cores, which serve as a proxy of the merger rates in the central region and facilitate a more direct comparison with theoretical predictions. Using 79 BCGs at z = 0.06–0.15 with integral field spectroscopic data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) project, we obtain a multiple-core fraction of 0.11 ± 0.04 at z ≈ 0.1 within an 18 kpc radius from the center, which is comparable to the value of 0.08 ± 0.04 derived from mock observations of 218 simulated BCGs from the cosmological hydrodynamical simulation IllustrisTNG. We find that most cores that appear close to the BCGs from imaging data turn out to be physically associated systems. Anchoring on the similarity in the multiple-core frequency between the MaNGA and IllustrisTNG, we discuss the mass growth rate of BCGs over the past 4.5 Gyr.
We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.
Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.
Data from the Belle Collaboration for associated production (with a light unpolarized hadron) and single-inclusive production of transversely polarized Λ-hyperons in e + e − annihilation processes allowed to extract within a TMD approach, for the first time, the Λ polarizing fragmentation function. Recent theoretical developments on the computation of cross sections for single-inclusive hadron production in e + e − annihilation processes, within a Soft Collinear Effective Theory approach, combined with the CSS formalism for the double-hadron production case, can be used to perform a renewed analysis, adopting a full TMD factorization scheme. Preliminary results and a comparison between the two analyses are presented.
This paper reports on the development of a self-synchronizing underwater acoustic network developed for remote monitoring of mooring loads in Wave Energy Converters (WECs). This network uses Time Division Multiple Access and operates self-contained with the ability for users to remotely transmit commands to the network as needed. Each node is a self-contained unit, consisting of a protocol adaptor board, an FAU-DPAM underwater acoustic modem and a battery pack. A node can be connected to a load cell, to a topside user or to the WEC. Every node is swapable. The protocol adaptor board, named Protocol Adaptor for Digital LOad Cell (PADLOC) supports a variety of digital load cell message formats (CAN, MODBUS, custom ASCII) and underwater acoustic modem serial formats. PADLOC enables topside users to connect to separate load cells through a user-specific command. This is especially important if the user is monitoring multiple load cells during deployment or maintenance, when the primary data system may be offline. Each PADLOC board handles formatting, buffering and has a one-on-one serial connection with each pair (node) of a digital load cell and acoustic modem. In addition, each PADLOC board handles the timekeeping and power saving features for each node. The only limitation is the data bit rate and delay limitations associated with the underwater acoustic modem. A four node self-synchronizing network has been developed to demonstrate the load cell monitoring capability using the PADLOC technology on the CalWave WEC.
We present the development of a new concept of fast timing gaseous detector, the ?RWELL-PICOSEC detector based on Resistive Micro-Well (?RWELL) technology to provide timing resolution in the tens of picosecond range for application as time-of-flight (TOF) technology in the particle physics and medical instrumentation fields. The ?RWELL-PICOSEC technology combined a Cerenkov radiator for the generation of Cerenkov photons from high energy charged particles, a photocathode for the conversion of the produced photons into primary electrons and the ?RWELL foil for the multiplication of the electron to produce large signal on pad segmentation readout. The proof of concept of ?RWELL-PICOSEC is demonstrated with a small single-channel prototype and preliminary timing performance of 90 ps has been measured. Optimization study the amplification structure of ?RWELL-PICOSEC for the improvement of timing resolution is discussed and results on timing performance studies in beam at CERN are presented. The development of large area (100 mm × 100 mm) ?RWELL-PICOSEC and associated multichannel fast readout electronics and data acquisition system are also reported