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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Development of Energy-Recovery Linacs

Energy-recovery linacs (ERLs) have been emphasised by the recent (2020) update of the European Strategy for Particle Physics as one of the most promising technologies for the accelerator base of future high-energy physics. The current paper has been written as a base document to support and specify details of the recently published European roadmap for the development of energy-recovery linacs. The paper summarises the previous achievements on ERLs and the status of the field and its basic technology items. The main possible future contributions and applications of ERLs to particle and nuclear physics as well as industrial developments are presented. The paper includes a vision for the further future, beyond 2030, as well as a comparative data base for the main existing and forthcoming ERL facilities. A series of continuous innovations, such as on intense electron sources or high-quality superconducting cavity technology, will massively contribute to the development of accelerator physics at large. Industrial applications are potentially revolutionary and may carry the development of ERLs much further, establishing another shining example of the impact of particle physics on society and its technical foundation with a special view on sustaining nature.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Geological characterization and ROZ potential of the Tar Springs Sandstone

This report focuses on regional and field scale geological characterization of the Tar Springs in Illinois using well records, including drilling and completion reports, well logs, and core data (core descriptions/core analyses) to analyze and interpret geological variations. In addition, oil saturation indicators (oil shows, oil saturation from core analysis, and drill stem test data) were analyzed to determine the ROZ potential of the Tar Springs Sandstone

02 PETROLEUM↗
DOE OSTI2022

TopTemp: Parsing Precipitate Structure from Temper Topology

Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties.

Kassab, Lara↗
DOE OSTI2024

Assessment of Bird Strike Likelihood to Refine Bird Strike Risk Models

In its most basic form, bird strike risk is comprised of a frequency component that reflects the likelihood of a collision and a severity component that reflects the cost (monetary or otherwise) of the incident. The bird strike risk model currently used by United State Department of Agriculture (USDA) Wildlife Services to evaluate the risk posed by individual bird species at airports and establish priorities for management was developed in 2018. The model uses airport-specific data on the number of reported strikes for a species recorded in the Federal Aviation Administration (FAA)’s National Wildlife Strike Database as a measure of frequency and the species’ relative hazard score as a measure of severity. The model was tested against independent data, found to perform well overall, and is being implemented widely across the United States. However, the model has limitations, including that species known to pose risk to aircraft locally, but not present in the strike record database, are not reflected as a major component of risk. Standard bird survey methodology commonly used at airports (e.g. point counts or transects) potentially can be used to complement wildlife strike records to calculate frequency or relative abundance of species. However, these methods generally focus on airport-wide population estimation and often ignore vital information that contributes to the true likelihood of a strike, such as use of runway protection zones and other critical areas, and spatial and temporal overlap with departing or approaching aircraft. As such, a more detailed understanding of space use by birds across landcovers and population fluctuations across the year is needed to accurately estimate the likelihood of bird strikes at airports. In this manuscript, we will review the extant risk model, including a discussion on its limitations. We then discuss approaches for refining our understanding of strike likelihood and briefly touch on needs for estimating probability of strike severity (cost).

bird strike, aircraft collision, damage by wildlif↗
DOE OSTI2025

LDMX -- The Light Dark Matter eXperiment

LDMX, The Light Dark Matter eXperiment, is a missing momentum search for hidden sector dark matter in the MeV to GeV range. It will be located at SLAC in End Station A, and will employ an 8 GeV electron beam parasitically derived from the SLAC LCLS-II accelerator. LDMX promises to expand the experimental sensitivity of these searches by between one and two orders of magnitude depending on the model. It also has sensitivity in visible ALP searches as well as providing unique data on electron-nucleon interactions.

Appert, Stephen [Caltech]↗
DOE OSTI2026

Thermal Performance of Spandrel Assemblies in Glazed Wall Systems: Laboratory Test Design – Challenges and Test Results

Accurate thermal performance calculation procedures for opaque spandrel areas in curtain wall and window wall systems are essential for rating systems when comparing spandrel systems. However, there is a lack of consensus in thermal modeling needed for accurately characterizing heat transfer through spandrel assemblies due to the complex arrangement of materials and structural components. Several studies indicate that conventional 2D thermal simulations may overestimate R-values by 30% compared to physical testing and 3D simulations. Detailed simulations and well-curated laboratory test data are necessary to build confidence in simulation models, which will later be used to develop correlations to improve widely used conventional 2D thermal simulations. This study aims to experimentally test heat transfer through various spandrel assemblies to validate 3D simulation models. Also, the challenges of conducting a thorough testing design along with the solutions would be documented. The team developed a design for testing spandrel assemblies, making appropriate modifications to the existing heat, air, and moisture (HAM) chamber to accommodate the testing needs. Two moveable baffles were designed and fabricated to guide airflow direction parallel to the test article surface. The data acquisition capabilities in the chamber were upgraded to add more than two hundred sensors to the climate and indoor side of the chamber. The goal is to provide a quality dataset for validating complex 3D modeling simulations, which will be used to develop improved thermal simulation techniques that more accurately represent the thermal behavior of spandrel assemblies and their integration within the building envelope. This paper will summarize the results for the boundary conditions of the testing and the temperature variation across different locations of the spandrel assemblies.

Kunwar, Niraj [ORNL] (ORCID:0000000263457652)↗
DOE OSTI2023

GOOML (Geothermal Operational Optimization with Machine Learning) [SWR-23-01]

The Geothermal Operational Optimization with Machine Learning (GOOML) is a partnership between NREL and Upflow, NZ, awarded in response to the U.S. Department of Energy's Geothermal Technologies Office's Funding Opportunity Announcement (FOA) to expand the role of advanced analytics and automation in geothermal operations through machine learning. Partnering with industry (Contact Energy Limited ("Contact"), Ngati Tuwharetoa Geothermal Assets Limited ("NTGA"), Ormat Technologies Inc. ("Ormat") and Flow State Solutions Limited ("FSS"), GOOML was created to improve the operational efficiency of geothermal power plant steam fields through the analysis of historical operational data and the application of custom machine learning algorithms. NREL's contributions include machine learning, coding, and data management expertise as well as access to high-performance compute solutions. GOOML can increase geothermal operational efficiency through development of a digital system twin that can be utilized to provide optimal geothermal operating conditions for real-world geothermal fields. GOOML allows users to analyze field production histories in detail, develop models, and train machine learning algorithms to identify opportunities for increased geothermal efficiency, detect potential trouble, and allow predictive scenario modeling. Preliminary experiments have demonstrated a potential to increase total generation by as much as 12% through ML optimization of the utilization of existing steam field resources.

Buster, Grant↗
DOE OSTI2022

Evaluated Nuclear Structure Data File (ENSDF)

Contains recommended nuclear structure and decay data for all the known nuclides, which are obtained following a critical review of all available experimental data, supplemented with systematic trend studies and theoretical models. ENSDF data include (i) nuclear level properties, such as observation source, energy, half-life, decay modes, spin and parity; (ii) gamma ray energies, intensities, multipolarities, mixing ratios and conversion coefficients; (iii) nuclear radiation energy and intensity as well as radiation-specific data for different radiation types, such as such as gammas, alphas, betas and neutrons. Data for about 220 nuclides are fully evaluated each year; additionally, many datasets are partially updated each year. Nearly all the evaluation work is supported by the US Nuclear Data Program. The National Nuclear Data Center at Brookhaven National Laboratory is responsible for coordinating the evaluation effort as well as its web and journal dissemination.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

40 Ca transverse response function from coupled-cluster theory

Here, we present calculations of the 40 Ca transverse response function obtained from coupled-cluster theory used in conjunction with the Lorentz integral transform method. We employ nuclear forces derived at next-to-next-to leading order in chiral effective field theory with and without Δ degrees of freedom. We first benchmark this approach on the 4 He nucleus and compare both the transverse sum rule and the response function to earlier calculations based on different methods. As expected from the power counting of the chiral expansion of electromagnetic currents and from previous studies, our results retaining only one-body term underestimate the experimental data for 4 He by about 20%. However, when the method is applied to 40 Ca at the same order of the expansion, response functions do not lack strength and agree well with the world electron scattering data. We discuss various sources of theoretical uncertainties and comment on the comparison of our results with the available experiments.

39 ≤ A ≤ 58↗
DOE OSTI2022

Lead isotopic compositions of airborne particulates collected from snow and from dry deposition in the East River Watershed, Colorado and at the Southern Sierra Critical Zone Observatory, California

The data set consists of lead (Pb) isotopic ratios (206Pb/207Pb and 208Pb/207Pb, as well as 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb) for particulate samples collected from snow and dry deposition. This was part of a study (O’Day et al. 2020) that was looking at the speciation of phosphorous in splits of these samples to evaluate the flux and bio-availability of phosphorous to the mountain sites in the study. The Pb isotopic data allow sourcing of long-range transported Pb, specifically the fraction of Pb sourced in Asia, and so a comparison of the relative distances of PM sources between the high and low elevation sites. Samples were collected at the East River Watershed, CO (dry deposition collected over Sept.-Oct. 2016, show sampling winter 2017) and at the Southern Sierra Critical Zone Observatory (dry deposition collected over Sept-Nov 2016, July-Sept 2017, June-July 2017). The data includes Pb concentration data, as well as calculated fraction of the Pb sourced in Asia. This data set was published in part in O’Day et al. (2020) Phosphorus Speciation in Atmospherically Deposited Particulate Matter and Implications for Terrestrial Ecosystem Productivity. ES&T, https://dx.doi.org/10.1021/acs.est.9b06150

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Utah FORGE: RESMAN Well 16A(78)-32 and 16B(78)-32 Stimulation and Circulation Tracer Test Results - 2024

This dataset contains tracer test results from stimulation and circulation experiments conducted on the Utah FORGE wells 16A(78)-32 and 16B(78)-32 during 2024. The data was collected by RESMAN Energy Technology and includes detailed tracer analysis from flowback, short- and extended-duration circulation tests, and reinjection sampling. Sampling included analysis of tracers during different stages of testing in April, August, and September 2024. The dataset is accompanied by an interpretation report and contains time-series tracer concentration data with identification of test phases and sampling conditions. It includes results for flowback from well 16A, commingling effects with water from well 16B, tracer data from short and extended circulation tests, and reinjection tracer corrections for the August/September test. Users should be aware that proprietary tracer methodologies were applied, and they should consult the interpretation report for insights into experimental procedures and data contextualization.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Robust Method to Identify Groundwater Affected By Redox Conditions at Los Alamos National Laboratory Legacy Cleanup Site - 20497

To facilitate the mission of the Department of Energy's (DoE's) Environmental Management Program at Los Alamos National Laboratory (LANL), a significant number of groundwater monitoring wells with depths ranging from 152-396 meter were completed for characterization and monitoring. Groundwater at Los Alamos tends to be oxygen saturated with very low concentrations of organic matter. Drilling fluids can introduce residual carbon causing an increase in microbial activity and local reducing conditions around the well, potentially impacting representativeness of groundwater data quality for redox sensitive constituents. We assert that the redox state of the wells can robustly be identified by aqueous solution concentrations of iron and manganese. An automated review process has been built into a computer-based data management system enabling an efficient screening process for identifying reducing conditions that are not caught using standard data-validation protocol, and ensures that important data quality issues are thoroughly identified and addressed in an efficient and timely manner. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Entanglement masquerading in the CMB

The simplest single-field inflation models capture all the relevant contributions to the patterns in the Cosmic Microwave Background (CMB) observed today. A key assumption in these models is that the quantum inflationary fluctuations that source such patterns are generated by a particular quantum state — the Bunch-Davies (BD) state. While this is a well-motivated choice from a theoretical perspective, the question arises of whether current data can rule out other, also well motivated, choices of states. In particular, as we previously demonstrated in [1], entanglement is naturally and inevitably dynamically generated during inflation given the presence of a "rolling" spectator scalar field — and the resulting entangled state will yield a primordial power spectrum with potentially measurable deviations compared to the canonical BD result. For this work we developed a perturbative framework to allow a systematic exploration of constraints on (or detection of) entangled states with Planck CMB data using Monte Carlo techniques. We have found that most entangled states accessible with our framework are consistent with the data. One would have to expand the framework to allow a greater variety of entangled states in order to saturate the Planck constraints and more systematically explore any preferences the data may have among the different possibilities.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Qualitative Risk Assessment of Legacy Wells within the Estimated Prairie State Generating Company Area of Review

This report details the digitization of a legacy wellbore database, including data processing assumptions, parameter estimation, and risk assessment methodology. The database, comprising 6,454 documents, was provided by ISGS. It includes valuable data from the Prairie State Generating Company (PSGC) and One Earth Energy (OEE) sites of the CarbonSAFE Phase III – Illinois Storage Corridor project. The report focuses on wells within a 15-mile radius from the Lively Grove #1 (LG#1) well at PSGC site, evaluating subsurface conditions and potential risks. A total of 4,386 wellbores within 15 miles of the LG#1 well were filtered based on depth and formation codes. LG#1 is the stratigraphic well at the PSGC site drilled in 2021. Ninety-four (94) wells penetrating the Maquoketa Shale Group (the primary confining unit) within the estimated area-of-review (AoR) for the PSGC site were evaluated using a qualitative risk assessment (QRA) methodology. The QRA developed by Arbad et al. 2022 focuses on legacy wells within the AoR and categorizes them based on well construction details. The QRA identifies wells that need immediate attention by categorizing them based on penetration depth and protection. Wells within the AoR were categorized into nine groups based on penetrations and protections. These categories range from Type 1 wells, with no documentation, to Type 9 wells, which do not penetrate the primary confining unit or storage reservoir (unit). Well accessibility within the AoR varies based on well status, including Dry & Abandoned (DA), Plugged & Abandoned (PA), Injection (INJ), Oil/Gas Producing (PROD), and Observation (Obs) wells. Accessibility levels were determined by well construction, with DA wells being the least accessible and Observation wells the most accessible, impacting gas leakage detection possibilities. Remedial action priority of wells decreases from Type 1 to Type 9 wells. Type 1 to Type 6 wells with status DA and PA require immediate attention, while Type 7 and Type 8 wells are low priority. A risk matrix used to prioritize corrective actions for legacy wells is proposed to categorize wells within an AoR based on penetrations, protections, and accessibility. The methodology involves data acquisition, well categorization into nine types, and determining CO 2 leakage pathways using well schematics and geospatial mapping. This approach is particularly useful for managing the integrity of legacy wells throughout the lifecycle of a Carbon Capture and Storage (CCS) project. A qualitative risk assessment of 94 wells within the AoR of the PSGC site identified 54 wells with high priority for corrective action due to penetration of the primary containment seal. The assessment utilizes color-coded maps to categorize well types and prioritize corrective actions, providing a comprehensive analysis. Schematics of wells penetrating the primary confining unit were drawn, and leakage pathways were identified. Details of all wells penetrating the confining zone are provided in the appendix, including information on well types, plugging, and casing status.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Report on Data Quality Required for Modeling an MSR

Calculations were performed to assess how uncertainties in molten salt property values impact the results of behavior models of molten salt reactor systems under steady-state conditions. The thermophysical and thermochemical properties of salt mixtures being considered for use in molten salt reactors are not yet well characterized and methods used to measure property values to molten salts are not yet well established. The limited property data for salts of interest that are available in the literature are inconsistent and unreliable. Many methods used to measure property values remain developmental in the sense that not all variables affecting measured values have been identified or controlled during the measurement and not all aspects of the measurements affecting data quality are calibrated. A prior report described the repeatability of measurements made at Argonne and the reproducibility of measurements made with the same salts at several institutions is being evaluated. There is significant uncertainty in measured property values due to instrumental limitations and the effects of contaminants in the salts used to make the measurements that impacts predictions of salt behavior in a reactor. Most salts of interest are multicomponent eutectic or near-eutectic mixtures and both uncertainty in the salt composition and uncertainty in measured values. Furthermore, the salt composition evolves over time due to the uptake of impurities from the atmosphere and corrosion of containment vessels. More significantly, fission products will accumulate during operation that change the salt compositions and property values. MSR developers need to understand how this uncertainty in the basic property data of the fuel will impact modeling predictions for normal reactor operation as well was in transient scenarios that will impact the safety basis. The calculations and analyses in this report address the impact of uncertainty in measured property values under steady-state conditions through a generalized parametric study. The pooled uncertainty from contributions by a number of factors were considered and quantified, including varying composition due to impurities and fission products, imprecision in the measured values, and uncontrolled aspects of the measurement. In this way, we can begin to gain an understanding of the magnitude of the impact uncertainty will have in modeling MSR systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Waterlevel, geochemical, and borehole data from Rifle, Colorado from 2006-2016

The files included in this data package provide site wide water level data, geochemical data, and borehole information associated with the Rifle site in Colorado during the Lawrence Berkeley National Lab (LBNL) led Integrated Field Research Challenge (IFRC) and Scientific Focus Area (SFA) research projects. The data informs hydrogeochemical interactions occurring in the watershed. The readme file contains detailed metadata for the files, including naming conventions, column headers, and data validation.The borehole information is provided in the file called rifle_boreholes.csv. It provides names and location for boreholes/wells at the Rifle. The waterlevel data is provided in the folder rifle_waterlevels. The geochemical data is provided in two folders: the rifle_floodplain_geochemistry folder has data which was collected from wells across the floodplain and a variety of offsite locations that contribute solutes to the floodplain given their upgradient locations. The rifle_plotC_geochemistry folder has data which was collected primarily in plot C, which was a focused study area in which a large number of groundwater manipulation / biostimulation experiments were conducted. While not all geochemical constituents are included for each well location and sampling date, they include anions, cations, trace metals, dissolved organic and inorganic carbon, stable isotopes of water and sulfur (as sulfate), and water quality parameters including pH, groundwater electrical conductivity (EC) and dissolved oxygen (DO). All files are in csv or txt format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Comparison of four learning-based methods for predicting groundwater redox status

Knowing the location where groundwater denitrification occurs, or by proxy the groundwater redox status (oxic, mixed, and anoxic), is valuable information for assessing and managing potential agricultural land-use impacts on freshwater quality. We compare the efficacy of supervised (Linear Discriminant Analysis LDA; Boosted Regression Trees, BRT; and Random Forest, RF) and unsupervised (Modified Self-Organizing Map, MSOM) learning-based methods to predict groundwater redox status in the agriculturally dominated Tasman, Waikato, and Wellington regions of New Zealand. Thresholds applied to regional groundwater-quality samples provide redox status variables and learn heuristics constrained by these variables and applied to spatial factors (climate, elevation, geologic, hydrology soils, and well depth) identify optimal sets of regional predictor variables. A split-sample approach is used to train and test the learning methods ability to predict redox status using the optimal predictor variables. Overall, the supervised methods demonstrate a prediction bias toward oxic conditions and inability to perform statistically well when using independent regional data; for example, consider kappa statistics for BRT (Tasman: 0.42, Waikato: 0.38, Wellington: 0.17), RF (Tasman: 0.42, Waikato: 0.47, Wellington: 0.17 and LDA (Tasman: 0.46, Waikato: 0.32, Wellington: 0.17). By contrast, the unsupervised method performs statistically well when predicting oxic, mixed, and anoxic conditions and corresponding depths when using independent regional data; for example, consider MSOM kappa statistics for Tasman: 0.78, Waikato: 0.80, Wellington: 0.76. The unsupervised learning method provides the added benefits of being (1) able to combine predictions into 3D regional anoxic probability plots for interpreting the spatial influence of paleosols and groundwater flowpaths on redox status, and (2) readily extended to map 3D redox status across New Zealand and other countries despite data bias and sparsity.

machine learning algorithms, Random Forest, booste↗
DOE OSTI2025

Use of Rig Parameter Data in Bit Constraint Models for Improved Drilling Performance at The Geysers

Surface parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. However, these measurements are of reduced value without a standard to aid in evaluation and decision making. A method is demonstrated whereby drill bit constraint models are used to interpret drilling response parameters. Drill rig parameter data for well GDC-36 at the Geysers Geothermal Field Power were acquired by Geysers Power Company and drilling contractor Kenai Drilling using Pason US DataHub and evaluated. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) along with other model constraints in computational algorithms. The method is used to evaluate overall bit performance, monitor bit integrity, and detect the presence of drillstring vibrations and other conditions contributing to bit failure; comparisons are made to observations of bit wear and damage. The method will be applied in real-time to improve decision-making on subsequent wells and has applicability to development of advanced analytics on future geothermal wells using real-time electronic drilling recorder (EDR) data for improved performance and reduced drilling costs.

15 GEOTHERMAL ENERGY↗