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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cherenkov and scintillation separation in water-based liquid scintillator using an LAPPDTM

Abstract This manuscript describes measurements of water-based liquid scintillators (WbLS), demonstrating separation of the Cherenkov and scintillation components using a low energy $$\beta $$ β source and the fast timing response of a Large Area Picosecond Photodetector (LAPPD). Additionally, the time profiles of three WbLS mixtures, defined by the relative fractions of scintillating compound, are characterized, with improved sensitivity to the scintillator rise-time. The measurements were made using both an LAPPD and a conventional photomultiplier tube (PMT). All samples were measured with an effective resolution $$O\left( 100~\text {ps}\right) $$ O 100 ps , which allows for the separation of Cherenkov and scintillation light (henceforth C/S separation) by selecting on the arrival time of the photons alone. The Cherenkov purity of the selected photons is greater than 60% in all cases, with greater than 80% achieved for a sample containing 1% scintillator. This is the first demonstration of the power of synthesizing low light yield scintillators, of which WbLS is the canonical example, with fast photodetectors, of which LAPPDs are an emerging leader, and has direct implication for future mid- and large-scale detectors, such as Theia , ANNIE, and AIT-NEO.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Fundamental Neutron Physics at Spallation Sources

Low-energy neutrons have been a useful probe in fundamental physics studies for more than 70 years. With advances in accelerator technology, many new sources are spallation based. These new, high-flux facilities are becoming the sites for many next-generation fundamental neutron physics experiments. In this review, we present an overview of the sources and the current and upcoming fundamental neutron physics programs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Streaked Sub-ps-resolution X-ray Line Shapes and Implications for Solid-density Plasma Dynamics

A high-resolution x-ray spectrometer was coupled with an ultrafast x-ray streak camera to produce time-resolved line shape spectra measured from hot, solid-density plasmas. A Bragg crystal was placed near a laser-produced plasma to maximize throughput; alignment tolerances were established by raytracing. The streak camera produced single-shot time-resolved spectra, heavily sloped due to photon time-of-flight differences, with sufficient reproducibility to accumulate photon statistics. The images are time-calibrated by the slope of streaked spectra and dewarped to generate spectra emitted at different times defined at the source. The streaked spectra demonstrate the evolution of spectral shoulders and other features on ps timescales, showing the feasibility of plasma parameter measurements on the rapid timescales necessary to study high-energy-density plasmas.

High-energy-density plasmas↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2020

Subseasonal prediction with and without a well-represented stratosphere in CESM1

There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. Furthermore, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Chimera D-Series Gravitational Wave Emission Sourced from Neutrino Anisotropy

Gravitational wave data sourced from the time-dependent anisotropic neutrino emission, as well as the time-dependent fluid quadrupole motion, in the Chimera D-Series three-dimensional core collapse supernova simulations. Data from three models initiated from three different progenitors are presented: D9.6-3D, D15-3D, and D25-3D. Please see the README for more information about the data structure and progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluation of high-stability optical beats in laser chaos by plasmonic photomixing

The stability of optical beats in a chaotically oscillating laser is compared to that of a free-running continuous-wave laser using a highly efficient plasmonic photomixer. Using a chaotically oscillating laser diode, stable optical beats are observed over an operation current range of 60-90 mA. The optical spectra are stable even with frequent mode hopping. In contrast, optical beats in a free-running continuous-wave laser are not stable compared to those of a chaotically oscillating laser, because of intermittent hopping of the laser modes. The high stability of chaotically oscillating lasers makes these lasers promising candidates for optical pump sources in terahertz time-domain spectroscopy systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

OCHRE

OCHRE™ uses a variety of input data sources to run time-series simulations. Building models can be taken from the ResStock™ database or generated using the Building Energy Optimization Tool (BEopt™) or other OpenStudio-HPXML workflows. EV charging profiles can be taken from datasets used in NLR's 2030 National Charging Network project. Weather data can be taken from the National Solar Radiation Database or EnergyPlus® weather files. There are no public datasets with OCHRE outputs at this time. However, a recent project dataset on water heater and EV demand flexibility can be requested. OCHRE is a Python-based energy modeling tool designed to model flexible loads in residential buildings. OCHRE includes detailed models and controls for flexible devices including HVAC equipment, water heaters, EVs, solar PV, and batteries. It is designed to run in co-simulation with custom controllers, aggregators, and grid models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.

Pappas, G. C.↗
DOE OSTI2021

Gamma Spectroscopy Detector Candidates and Radioisotope Identification Algorithms for Use in National Security Applications

In 2006, the United States Congress mandated for the Department of Homeland Security (DHS) to screen all cargo containers to protect against terrorist acts and ensure the safety and security of the nation. Containers are screened by gamma and neutron detectors to ensure threat material is not smuggled into the country. However, because commerce is radioactive, detecting the presence of radioactive materials is not sufficient in ensuring the safety and security of the nation. Radioactive materials must also be identified in real time, thus distinguishing threat sources (Uranium-235 and Plutonium-230) from non-threat sources (kitty litter, pot ash, medical isotopes). Screening cargo containers can be considered a two-step process (1) alerting to the presence of radioactive material when gamma counts exceed a threshold setting, and (2) once alerted, identifying the type of radioactive material, which is done by collecting a gamma spectra and analyzing it with an analysis tool/algorithm. For this reason, it is important to evaluate not only emerging technology in neutron and gamma detection, but also investigate new advances in algorithm development for radioisotope identification (RIID). New candidates in detection and on-board algorithm analysis might offer opportunities to make the scanning, detection, and identification process more efficient while still ensuring the health and safety of the public. This research will investigate emerging technology in radiation detection focused on gamma spectroscopy capabilities and RIID algorithms for DHS applications.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

A New Low-Temperature Approach for Efficient and Low-Cost Group V Doping in CdTe Thin Film Solar Cells (Final Technical Report)

The goal of this project is to develop a highly efficient low-temperature ex-situ Group V diffusion doping approach in the CdCl 2 treated polycrystalline CdTe film using the group V chlorides (i.e., PCl 3 , AsCl 3 , SbCl 3 , and BiCl 3 ) as dopants source. For the first time, our innovative doping strategies through the low-temperature ex-situ Group V doping process in polycrystalline CdSeTe successfully demonstrated a promising power conversion efficiency of ~19% compared to the Cu-doped counterparts. Our newly developed low-temperature ex-situ group V doping allows dedicated control of group V diffusion amount, generates a desirable depth profile, and is completely compatible with commercial CdTe module manufacturing. We built the partnership with the largest CdTe solar module manufacturing in the US, e.g., First Solar, Inc to further develop these low-temperature diffusion doped CdSeTe solar cells. Our technology was granted US Patent No. US11257977B2. In this project, we systematically investigate and characterize the new ex-situ group V doping mechanisms, defect chemistry, microstructure, and device physics in CdTe solar cells by combining experimental and theoretical means to pave the way to achieve lower cost toward $\$$0.02/kWh and higher power conversion efficiency (PCE) toward 25% in polycrystalline CdTe solar technology. This project will benefit the public by providing more affordable solar electricity and provide an effective way to address climate change.

14 SOLAR ENERGY↗
DOE OSTI2023

Evaluation of Historic Nuclear Cloud Measurement for Entrainment Parameter Determination

We evaluated the uncertainties associated with the entrainment parameters computed from nuclear cloud measurements taken during historic weapon testing campaigns. This evaluation found that variations in the way cloud heights and diameters were defined during the analysis of film records result in very large uncertainties in the entrainment parameters, severely limiting the utility of these data. For the measurements of atmospheric conditions at the time of the tests, the main sources of error are associated with the differences in the location and timing of the soundings.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Local-Scale Framework for Techno-Economic Analysis of Subsurface Hydrogen Storage

The energy sector is evolving toward increased reliance on renewable energy technologies to meet state, national, and organization decarbonization goals. This trend is creating challenges and opportunities with meeting current and future energy demand under the variable supply conditions that most renewables provide. Hydrogen (H 2 ) is a promising energy carrier that may meet the need for both on-demand and long-duration storage to maintain energy security and resilience. Underground hydrogen storage (UHS) is a method of storing H 2 in subsurface geological systems, such as depleted hydrocarbon reservoirs, salt caverns, saline aquifers, hard rock, and other engineered systems. UHS has the potential to store large quantities of H 2 over time, providing a reliable source of energy while minimizing surface footprints at a lower investment cost compared to surface storage. Earlier work estimated that, if converted and retrofitted, existing underground natural gas storage (UGS) facilities in the U.S. can store approximately 327 TWh, or 9.8 million metric tons, of pure H 2 . However, a shift to pure H 2 would decrease the collective working-gas energy of the UGS facilities by approximately 75% due to physical and chemical differences between natural gas and hydrogen. The same work also suggests that almost 75% of the existing UGS facilities in the U.S. could maintain current energy demand buffering using a blend of only 20% H 2 to 80% natural gas, by volume at surface conditions. If we can take advantage of the suite of mature technologies of existing UGS facilities and natural gas utility systems to accelerate the transition to a hydrogen economy in the U.S., a 20% H 2 blend could lead to a 6-7% reduction of greenhouse gas emissions for energy delivered through natural gas utility systems.

03 NATURAL GAS↗
DOE OSTI2024

Machine-Learning-Based Mapping and Modeling of Solar Energy with Ultra-High Spatiotemporal Granularity

Despite the rapid growth of solar energy, we still lack a dynamic, high-fidelity database that tracks the spatiotemporal variations of solar PVs and their associated infrastructures across different places at a spatially resolved scale. The absence of such data presents a barrier to various applications such as solar PV growth projection, solar energy integration, solar incentive design, and climate risk assessment. In this project, we aim to bridge this gap by developing AI-based algorithms to extract granular information about solar PV installations and their associated infrastructures (i.e., distribution grids) from widely available unstructured data like remote sensing images and street views. As a result, we have built the Solar Energy Atlas, a fine-grained, large-scale geospatial overlay of distributed solar PVs and distribution grids. On top of it, we have advanced the understanding of solar adoption and distribution grid vulnerability to climate-induced extremes. Our major contributions can be summarized as follow: (1) By developing new AI algorithms, we have built the most comprehensive solar PV spatiotemporal database covering the entire US. This is the first time we obtained the exact GPS locations, size, subtype, and installation year information for rooftop solar PVs across the US. This database can be used for solar PV growth projection, solar energy integration, solar energy policy analysis and design, and spatially-resolved climate risk assessment. (2) Leveraging this database, we have uncovered the socioeconomic driving factors that are correlated with earlier onset of solar adoption and higher saturated adoption levels. We have identified the heterogeneity in the effects of different types of financial incentives on solar adoption and provided implications for tailoring incentive design based on local income levels to promote equitable solar adoption. (3) We have developed a distribution grid GIS mapping algorithm which can obtain granular geospatial and topology information about distribution grids using multi-modal open data, reducing the dependency on hard-to-obtain smart meter data of conventional approaches. It shows effectiveness in both the U.S. and Sub-Saharan Africa. Using this algorithm, we have uncovered the non-uniform vulnerability of distribution grids to wildfires in California in the aspects of undergrounding protection and Distributed Energy Resources (DER) preparedness. This has provided important implications for improving the affordability and equity of grid adaptation approaches. (3) We have made our produced database publicly available and provided user-friendly interface to enable various stakeholders and the general public to interact with the data. We have also integrated the produced data into the Data Commons platform to enable the public to access the data and correlate it with other location-specific characteristics simply using natural language as queries. The impact of our project is three-fold: (1) New algorithms for mapping solar PVs and distribution grids across space and time, which are open source to facilitate researchers and industry; (2) New databases of solar PVs and distribution grids that have been made publicly available for engineering, social, and policy applications; (3) New understandings and actionable insights on the potential approaches to promoting solar adoption and reducing energy infrastructure vulnerabilities. In this report, we start by discussing the project background and motivation (section 5), followed by the overview of project objectives (section 6). Results and discussion for each task are presented in section 7. Significant accomplishments are summarized in section 8. This report will be concluded by discussing the paths forwards (section 9), products (section 10), and team roles (section 11).

14 SOLAR ENERGY↗
DOE OSTI2024

H3D Characterization Memo

The H3D was characterized using GADRAS 19.3.5 using data sent by the IAEA. Everything related to the measurement setup was provided by the IAEA. The sources measured were detailed and the distance from the detector was also provided. The data sheets of the sources were used to determine the source activity at the time of calibration, as needed for characterization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Bayesian Attack Model (BAM) User Story

This document presents a user story for the Bayesian Attack Model (BAM) tool designed to aggregate and analyze cyber-attack observables for operational technology (OT) systems. BAM aims to empower cybersecurity analysts by providing a streamlined interface for collecting observable data from various sources, enabling real-time analysis of potential adversary activity. By enhancing the response capabilities of security teams, BAM facilitates risk-informed decision-making and improves organizational security posture. This user story outlines the key functionalities, user interactions, and requirements necessary to successfully integrate BAM with other security information and event management (SIEM) technology and cybersecurity operations centers (CSOCs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A Review of Advanced Characterization Methods and Techniques for Amorphous and Poorly Crystalline Materials Applicable to Cementitious Waste Forms

This review covers advanced characterization methods of cementitious materials. The particular focus is to understand cementitious compositions with amorphous content because conventional techniques such as X-ray diffraction are more applicable to materials with long range order. Also of interest is advanced characterization that provides deeper insights into properties such as porosity. There are five groups of characterization methods discussed in this review which are: electron beam/microscopy, X-ray, neutron diffraction and scattering, nuclear magnetic resonance, and optical techniques. The techniques can be further grouped by the categories of nanoscale structural understanding (transmission electron microscopy and pair distribution function), microscale structural understanding (focused ion beam and x-ray tomography), microscopic elemental distributions (energy dispersive spectroscopy and X-ray fluorescence), and chemical binding information (electron energy loss spectroscopy, x-ray absorption spectroscopy, nuclear magnetic resonance, Raman spectroscopy, and infrared spectroscopy), with advantages and disadvantages of each method discussed. The recommendation for further research is to begin with techniques that are easier to prepare samples for, measure, and analyze, such as the optical techniques of Raman and infrared spectroscopy. If more information is needed, pursue collaborations, other techniques, or proposals for beam time at the synchrotron sources.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗