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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Godiva IV central cavity neutron environment characterization with threshold neutron detectors

Godiva IV is a cylindrical fast burst reactor comprised of approximately 65 kg of highly enriched uranium that is operated by Los Alamos National Laboratory and sited at the National Criticality Experiments Research Center at the Nevada National Security Site in Nevada in the United States. Godiva IV is typically operated at delayed critical and in the regime spanning from sub-prompt to super-prompt bursts. Godiva IV is used for sample irradiations, criticality safety demonstrations, dosimetry studies, and for studying super-prompt behavior. In preparation for both an upcoming experiment to reduce uncertainties in the prompt fission spectrum for 235U using threshold neutron detectors, and for future research using Godiva IV, it was desired to exercise the process of the selection of threshold neutron detectors/activation foils, radiation metrology, and the subsequent adjustment of the neutron spectrum. For this exercise, nine high purity threshold neutron detectors/activation foils were irradiated in a Godiva IV burst. The foils were then analyzed using a high-purity germanium detector in the NCERC counting laboratory to determine end of irradiation specific activities for available IRDFF-II reactions. This work summarizes the Godiva IV foil irradiation, radiation metrology results, and adjusted neutron spectrum. The results of this exercise ultimately characterized the neutron environment inside the sample irradiation cavity inside Godiva IV to a higher degree than previously performed, informed decisions for the upcoming larger scale experiment, and will inform future neutron spectrum characterizations at NCERC.

Whitman, Nicholas H.↗
DOE OSTI2026

Shower Reconstruction for Cross-Section Measurements with Photon(s) in the Final State at SBND

The Short-Baseline Near Detector (SBND) is a 112-ton liquid argon time projection chamber operating in the Booster Neutrino Beam at Fermilab as part of the Short-Baseline Neutrino Program. Precise reconstruction of electromagnetic showers from photons and electrons is essential for searches for neutrino oscillations, cross-section measurements, and rare-process studies in liquid argon. In this poster, we present a unified set of SBND analyses enabled by the SPINE (Scalable Particle Imaging with Neural Embeddings) end-to-end machine-learning–based reconstruction framework, highlighting charged-current neutral pion (CC π⁰) production and neutral-current (NC) Δ radiative decay as complementary physics cases. Charged-current π⁰ interactions provide a high-statistics benchmark for validating photon reconstruction, constraining the electromagnetic energy scale, and modeling backgrounds to other cross-section measurements and BSM searches with one or more photons in the final state. We summarize progress toward the ongoing development of a CC π⁰ selection in SBND and, report on the current status of a search for NC resonant Δ production followed by radiative decay (Δ→Nγ) in SBND. This rare process, previously investigated in MicroBooNE, provides a stringent test of photon-only final-state reconstruction and is of interest in the context of the MiniBooNE low-energy excess. Together, these analyses demonstrate how modern machine-learning–based shower reconstruction enables a diverse and scalable physics program at SBND.

Chung, Seokju [Nevis Labs, Columbia U.]↗
DOE OSTI2024

Probing shocks interacting with radiation waves with the Radishock experiment

Both radiation flows and shocks have been extensively studied in the laboratory in the past few decades due to their critical roles in many astrophysical and high-energy density physics processes. In the Radishock experiment, a halfraum-powered radiation wave is driven into a low-density foam and interacts with an ablatively driven, counter-propagating shock. The interacting waves produce a spike in energy density with a temperature greater than the local temperature of the individual waves. As in the successful predecessor experiment, COAX, the primary diagnostic uses absorption spectroscopy at many locations down the cylindrical target, enabling a spatial temperature inference of the radiation wave and its interactions with the shock. Combined with a radiography diagnostic that is capable of imaging the shock and interaction features, we are able to study and inform model predictions of the interaction spike phenomenon. We describe the underlying physics behind the shock interactions with the radiation front and the implications of this experimental study for a broad range of astrophysical phenomena.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Representing Fine‐Scale Topographic Effects on Surface Radiation Balance in Hyper‐Resolution Land Surface Models

Land surface models are increasingly used to simulate land surface processes at hyper-spatial resolutions (e.g., ∼1 km). As model resolution increases, grid-scale topographic effects on surface radiation fluxes and their interactions between adjacent grids become more pronounced. However, current land surface models routinely neglect the fine-scale topographic effects on surface radiation balance. This study developed physically-based and computationally-efficient parameterizations (fineTOP) that explicitly resolve fine-scale topographic effects on downward shortwave and longwave radiation as well as land surface radiative properties. The newly developed parameterizations were implemented and tested in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Multi-decadal km-resolution ELM simulations over the California Sierra Nevada show that fine-scale topography significantly impacts the surface energy balance and snow processes across seasons. Slope determines the magnitude of topographic effects, while aspect controls their sign. For slopes larger than 30°, topography-induced change in annual surface temperature can be as large as 3.3 K. Regionally, the mean value and standard deviation of topography-induced changes in annual surface temperature are −0.22 ± 0.38 K and +0.25 ± 0.37 K over north-facing and south-facing slopes, respectively. Topography-induced changes in surface radiative properties account for 3.5% ± 13.8% of total topographic effects on annual net radiation. With fineTOP, ELM captures the aspect-dependence of snow cover fraction, snow water equivalent, and land surface temperature found in MODIS satellite observations and a snow reanalysis data set, while the default ELM fails to capture this phenomenon. The enhanced capability to represent fine-scale topographic effects on surface radiation balance can be used to advance understanding of the role of fine-scale topography in land surface processes and land-atmosphere interactions over mountainous regions.

Hao, Dalei [Pacific Northwest National Laboratory ↗
DOE OSTI2025

Descriptor: High Temporal Resolution Meteorological Data at Oak Ridge Reservation (ORR-HiResMet)

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.

Steckler, Morgan R. [Oak Ridge National Laboratory↗
DOE OSTI2024

Purification Techniques for Actinide Radiolysis Studies

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial due to their unavoidable exposure to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. We have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques, including gamma irradiation, in-situ alpha irradiation and pulse radiolysis experiments. However, for the collection of accurate data, thorough purification and quantification of actinide-containing solutions is required. This presentation will cover the purification and quantification techniques employed for the radiolysis experiments described in our recent publications: Kynman et al., Multiscale Modeling of Plutonium Radiation Chemistry in Nitric Acid Solutions. 1. Cobalt-60 Gamma Irradiation of Pu(IV) [https://pubs.acs.org/doi/10.1021/acs.inorgchem.4c00138] and Kynman et al., Generation and Study of Am(IV) by Temperature-Controlled Electron Pulse Radiolysis [https://doi.org/10.1039/D4DT00991F].

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Resonant Slow Extraction Simulation using Bmad

Simulations of slow extraction and transport of charged particle beams from synchrotrons requires careful modeling and experimentation. In this paper, we outline how the Bmad modeling software was adapted and developed to run third-integer resonant extraction simulations of beams at Brookhaven National Laboratory’s Booster synchrotron. Further, we show experimental comparisons of the slowly extracted beams transferred to the NASA Space Radiation Laboratory (NSRL) transport line. In this process, beam passes through a stripping foil element at the extraction point, which, along with stripping remaining electrons from the beam ions, acts as a scatterer to modify the phase space, moving to a more Gaussian-like distribution. This modification to the beam helps generate a uniform beam at the beam line’s target location; which is necessary for the variety of experiments performed at the facility. During this work, beam energy loss and multiple scattering by foil routines were built in conjunction with the Bmad code developers and are now integrated into the software.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating Radiation-Induced Actinide Species in Solution

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial to nuclear fuel cycle development, due to the unavoidable exposure of these elements to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new nuclear technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. Consequently, we have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques. Temperature-controlled electron pulse radiolysis has been used to study Am for the first time, determining the feasibility of Am redox reactions under used nuclear fuel reprocessing conditions, (e.g. nitric acid, non-ambient temperature). Additionally, we have developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu redox chemistry due to radiolysis and disproportionation reactions in concentrated nitric acid solutions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Investigating Radiation-Induced Actinide Species in Solution

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial due to their unavoidable exposure to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new nuclear technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. Consequently, we have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques. Temperature-controlled electron pulse radiolysis has been used to study Am for the first time, determining the feasibility of Am redox reactions under used nuclear fuel reprocessing conditions, (e.g. nitric acid, non-ambient temperature). Additionally, we developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu redox chemistry due to radiolysis and disproportionation reactions in concentrated nitric acid solutions.

actinide↗
DOE OSTI2024

The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators

Abstract. Supercooled liquid clouds are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We take advantage of the Macquarie Island Cloud and Radiation Experiment (MICRE) to perform an analysis of observed and simulated cloud processes over the Southern Ocean in a region and season dominated by supercooled liquid clouds. Using a single-column version of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), we compare two different cloud microphysical schemes to ground-based observations of cloud, precipitation, and radiation over a 2.5-month period (1 January–17 March 2017). Both schemes are able to reproduce aspects of the cloud and radiation observations during MICRE to within the uncertainty of the data when the thermodynamic profile is prescribed with relaxation. There are differences in water mass and representation of reflectivity between the schemes. A sensitivity study of the cloud microphysics schemes, one a bulk one-moment scheme and the other a two-moment scheme with prediction of mass and number, indicates that several key processes create differences between the schemes. Surface radiative fluxes and total water path are highly sensitive to the formation and fall speed of precipitation. The prediction of hydrometeor number with the two-moment scheme yields a better comparison with observed reflectivity and radiative fluxes, despite predicting higher liquid water contents than observed. With the two-moment scheme, we are also able to test the sensitivity of the results to the input of liquid cloud condensation nuclei (CCN) and ice nuclei (IN). The cloud properties and resulting radiative effects are found to be sensitive to the CCN and IN concentrations. More CCN and IN increase liquid and ice water paths, respectively. Thus, both the dynamic environment and aerosols, integrated through the cloud microphysics, are important for properly representing Southern Ocean cloud radiative effects.

58 GEOSCIENCES↗
DOE OSTI2024

Impact of Temperature and Optical Error on the Combined Optical and Thermal Efficiency of Solar Tower Systems for Industrial Process Heat: Preprint

Concentrating solar thermal (CST) power towers can provide high flux concentrations at commercial scale. As a result, CST towers exhibit potential for high-temperature solar industrial process heat (SIPH) applications. However, at higher operating temperatures, thermal radiation losses can be significant. This study explores the trade-off between thermal and optical losses for SIPH applications using a collection of three case studies at operating temperatures that range from 900-1,550 degrees C. We assume blackbody radiation to represent the thermal losses at the receiver and we use ray tracing to estimate the optical losses. The results show the impact of process temperature on the maximum attainable system efficiency, as well as the higher flux concentration requirements as the temperature increases.

concentrating solar power↗
DOE OSTI2024

Quantum barriers engineering toward radiative and stable perovskite photovoltaic devices

Efficient photovoltaic devices must be efficient light emitters to reach the thermodynamic efficiency limit. Here, we present a promising prospect of perovskite photovoltaics as bright emitters by harnessing the significant benefits of photon recycling, which can be practically achieved by suppressing interfacial quenching. We have achieved radiative and stable perovskite photovoltaic devices by the design of a multiple quantum well structure with long (~3 nm) organic spacers with oleylammonium molecules at perovskite top interfaces. Our L-site exchange process (L: barrier molecule cation) enables the formation of stable interfacial structures with moderate conductivity despite the thick barriers. Compared to popular short (~1 nm) Ls, our approach results in enhanced radiation efficiency through the recursive process of photon recycling. This leads to the realization of radiative perovskite photovoltaics with both high photovoltaic efficiency (in-lab 26.0%, certified to 25.2%) and electroluminescence quantum efficiency (19.7 % at peak, 17.8% at 1-sun equivalent condition). Furthermore, the stable crystallinity of oleylammonium-based quantum wells enables our devices to maintain high efficiencies for over 1000 h of operation and >2 years of storage.

14 SOLAR ENERGY↗
DOE OSTI2025

Preliminary study of auto-differentiation algorithm in beam dynamics with stochastic process

Modern particle accelerator optimization requires sophisticated computational methods to address the inherently stochastic nature of beam dynamics. This research develops a framework applying AD to SDEs that specifically addresses beam dynamics challenges in particle accelerators, focusing on accurately modeling and optimizing beam behavior in regimes dominated by stochastic processes. By incorporating key physical phenomena such as synchrotron radiation, wakefield effects, and quantum excitation, the framework aims to provide auto differentiation on the figure of merit of the phase space evolution and beam dynamics. The methodology will enable effective optimization method in a dynamic system with stochastic process.

Accelerator Physics↗
DOE OSTI2025

Benchmark of the Fe xvv 𝓡 ratio in photoionized plasma during eclipse of Centaurus X-3 with XRISM/Resolve

The $\mathcal {R}$ ratio is a useful diagnostic of the X-ray emitting astrophysical plasmas and is defined as the intensity ratio of the forbidden over the inter-combination lines in the K$\alpha$ line complex of He-like ions. The value is altered by excitation processes (electron impact or UV photoexcitation) from the metastable upper level of the forbidden line, thereby constraining the electron density or UV field intensity. The diagnostic has been applied mostly in electron density constraints in collisionally ionized plasmas using low-Z elements, as was originally proposed for the Sun (Gabriel & Jordan, 1969a, MNRAS, 145, 241), but it can also be used in photoionized plasmas. To make use of this diagnostic, we need to know its value in the limit of no excitation of metastables ($\mathcal {R}_{0}$), which depends on the element, how the plasmas are formed, how the lines are propagated, and the spectral resolution affecting line blending principally with satellite lines from Li-like ions. We benchmark $\mathcal {R}_0$ for photoionized plasmas by comparing calculations using radiative transfer codes and observation data taken with the Resolve X-ray microcalorimeter onboard XRISM. We use the Fe xxv He$\alpha$ line complex of the photo-ionized plasma in Centaurus X-3 observed during eclipse, in which the plasma is expected to be in the limit of no metastable excitation. The measured $\mathcal {R} = 0.65 \pm 0.08$ is consistent with the value calculated using xstar for the plasma parameters derived from other line ratios of the spectrum. We conclude that the $\mathcal {R}$ ratio diagnostic can be used for high-Z elements such as Fe in photoionized plasmas, which has wide applications in plasmas around compact objects at various scales.

X-rays: binaries↗
DOE OSTI2024

Using MARCUS, MICRE, and COMBLE data to improve understanding and modeling of cloud, aerosol, and boundary layer processes at high-latitudes

Because it is believed general circulation (GCM) and numerical weather prediction (NWP) models underestimate shortwave radiation over the Southern Ocean (SO) due to inadequate representations of boundary layer (BL) and cloud processes, it is critical to improve our understanding of key aerosol, cloud, precipitation, and BL processes. Over the north Atlantic Ocean (NA), cold-air outbreaks are common, yet few studies of the aerosol and environmental controls of the associated convective BL clouds exist as needed to develop and evaluate GCM representations. Although processes cannot be observed, cloud and aerosol properties can be measured in-situ or remotely retrieved, which when combined with numerical simulations enable process level understanding required to improve model representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Global Simulations of Gravitational Instability in Protostellar Disks with Full Radiation Transport. I. Stochastic Fragmentation with Optical-depth-dependent Rate and Universal Fragment Mass

Fragmentation in a gravitationally unstable accretion disk can be an important pathway for forming stellar/planetary companions. To characterize quantitatively the condition for and outcome of fragmentation under realistic thermodynamics, we perform global 3D simulations of gravitationally unstable disks at various cooling rates and cooling types, including the first global simulations of gravitational instability that employ full radiation transport. We find that fragmentation is a stochastic process, with the fragment generation rate per disk area p frag showing an exponential dependence on the parameter β ≡ Ω K t cool , where Ω K is the Keplerian rotation frequency, and t cool is the average cooling timescale. Compared to a prescribed constant β, radiative cooling in the optically thin/thick regime makes p frag decrease slower/faster in β; the critical β corresponding to ∼1 fragment per orbit is ≈3, 5, and 2 for constant β, optically thin, and optically thick cooling, respectively. The distribution function of the initial fragment mass is remarkably insensitive to disk thermodynamics. Regardless of cooling rate and optical depth, the typical initial fragment mass is m frag ≈ 40M tot h 3 , with M tot being the total (star+disk) mass and h = H/R being the disk aspect ratio. Applying this result to typical Class 0/I protostellar disks, we find m frag ∼ 20M J , suggesting that fragmentation more likely forms brown dwarfs. Given the finite width of the m frag distribution, forming massive planets is also possible.

gravitational instability↗