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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Upper limits for X-ray and energetic neutral particle emission from Jupiter - Voyager-1 results

The X-ray sensitivity of the low energy proton channels in the Low Energy Charged Particle (LECP) experiment and its directional measurements determined whether X-rays from Jupiter can be recognized as additional flux above background as Voyager 1 approached the planet. Between about 230 and about 100 R(J), a statistically significant count rate was detected interpreted as X-rays and/or neutral particles. Such an X-ray flux leads to estimates of precipitating electrons over the Jovian polar caps which are 200 times larger than over observed electron fluxes in the Io plasma torus.

Kirsch, E.↗
DOE OSTI2025

Automated Image Segmentation and Processing Pipeline Applied to X–Ray Computed Tomography Studies of Pitting Corrosion in Aluminum Wires

Understanding pitting corrosion is critical, yet its kinetics and morphology remain challenging to study from X-ray computed tomography (XCT) due to manual segmentation barriers. To address this, an automated pipeline leveraging deep learning for efficient large-scale XCT analysis is developed, revealing new corrosion insights. The pipeline enables pit segmentation, 3D reconstruction, statistical characterization, and a topological transformation for visualization. Here, the pipeline is applied to 87 648 XCT images capturing commercial purity aluminum (1100 Al) wire exposed to sodium chloride (NaCl) salt particles over a period of 122 h. The pipeline achieves complete feature extraction and statistical quantification across the entire XCT dataset, leveraging distributed computing environment for high efficiency. Global growth kinetics such as high-level stepwise sigmoidal volume loss patterns and granular individual pit developments are both captured for 36 detected pits. By combining automation, computer vision, and extensive XCT datasets, this research accelerates precise corrosion assessment to enable materials science discoveries at scale.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Major improvements on the longwave radiative interactions between surface and clouds in the Polar Regions in atmospheric global circulation model (GCM). Final Project Report

1. Summary of Work and Accomplishments in Year 1 (01/15/2015-01/14/2016). We developed new ice cloud parameterizations based on state-of-the-art MODIS Collection 6 (MC6) ice particle model. Features of the model include the consideration of surface roughness of ice crystals and spectral consistency in cloud property retrievals. Using more than 14,000 particle size distributions (PSDs) from in-situ measurements and the latest ice particle single-scattering property library, our parameterizations provide band-averaged bulk-scattering properties. According to statistics of fittings, the parameterization schemes represent the band-averaged bulk-scattering properties very well. Compared to the ice particle model developed by Fu (1998), flux simulations with the MC6 particle model result in larger upward flux at the top of the atmosphere (TOA) and smaller at the surface for typical cirrus clouds. In the assessment of the effect of longwave light scattering, our results also support the conclusions of previous studies. The error in flux calculations reaches about 10 W/m 2 when a cirrus cloud is thin and its particle size is small. 2. Summary of Work and Accomplishments in Year 2 (01/15/2016-01/14/2017) Clouds are a major modulator of the global radiation budgets. However, representations of clouds in general circulation models (GCMs) neglect many important processes, particularly those related to ice clouds. One major uncertainty source is the radiative transfer scheme in GCMs. To reduce the computational burden in most GCMs, absorption is the only radiative transfer process considered in longwave spectral bands. Recently, a state-of-the-art MC6 ice model has been available, which provides spectral consistency in ice cloud retrievals. With an advanced light scattering library for ice particles, we performed sensitivity studies of the potential impacts on the climate including light scattering for MC6 model and other 11 ice cloud models. Specifically, we modified RRTMG_LW (the GCM version of Longwave Rapid Radiative Transfer Model), a radiative transfer module widely used in GCMs and numerical prediction models, to identify flux simulation differences with and without considering light scattering processes. The simulations with light scattering come from a rigorous radiative solver, DISORT (Discrete Ordinates Radiative Transfer Program for a Multi-Layered Plane-Parallel Medium), are used as benchmarks. The results show that the weighted annual mean biases of RRTMG_LW for the upward flux at the top of the atmosphere, the downward flux at the surface, and the net flux into the atmosphere are about 0.8 ± 0.3, -0.1 ± 0.05, and -0.7 ± 0.3 W m-2, respectively. According to the different the ice crystal shapes, the weighted annual mean bias for heating rate is about -0.006 ± 0.02 to 0.04 K/day. 3. Summary of Work and Accomplishments in Year 3 (01/15/2017-01/14/2018) Since absorption dominates optical properties of clouds in the longwave (LW) spectrum, most of general circulation models (GCMs) only take absorption properties of clouds into account in the radiative transfer modules in order to reduce computational costs. To quantify the biases of excluding LW scattering when clouds exist, we simulated fluxes and heating rates by using satellite data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Clouds and the Earth’s Radiant Energy System (CERES) and Moderate Resolution Imaging Spectrometer (MODIS) merged products (CCCM) in 2010. In annual global average, neglecting LW scattering causes overestimation by 2.6 W/m 2 for the top-of-atmosphere (TOA) upward fluxes and underestimation by 1.2 W/m 2 for surface downward fluxes. In particular, regional extreme biases are approximately 12 W/m 2 for TOA upward fluxes and are approximately -3.6 W/m 2 for surface downward fluxes, or are approximately 5% of the global averaged outgoing longwave radiation (OLR) and 1% of the global averaged surface downward fluxes, respectively. In terms of heating rates, on average, LW scattering heats the whole atmosphere column by 0.0045 K/day, heats the cloud layers by 0.0420 K/day, heats the surface by 0.028 K/day, and cools the tropopause by 0.018 K/day.

58 GEOSCIENCES↗
DOE OSTI2022

The Forward Physics Facility: Sites, experiments, and physics potential

The Forward Physics Facility (FPF) is a proposal to create a cavern with the space and infrastructure to support a suite of far-forward experiments at the Large Hadron Collider during the High Luminosity era. Located along the beam collision axis and shielded from the interaction point by at least 100 m of concrete and rock, the FPF will house experiments that will detect particles outside the acceptance of the existing large LHC experiments and will observe rare and exotic processes in an extremely low-background environment. In this work, we summarize the current status of plans for the FPF, including recent progress in civil engineering in identifying promising sites for the FPF and the experiments currently envisioned to realize the FPF’s physics potential. We then review the many Standard Model and new physics topics that will be advanced by the FPF, including searches for long-lived particles, probes of dark matter and dark sectors, high-statistics studies of TeV neutrinos of all three flavors, aspects of perturbative and non-perturbative QCD, and high-energy astroparticle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Statistical Study of Energy Dissipation in Magnetic Structures During Turbulent Reconnection in the Earth's Magnetotail

Magnetic reconnection is a ubiquitous plasma phenomenon that plays a critical role in particle heating and energization. During reconnection, the topology of magnetic field rearranges, depositing energy into the surrounding plasma through bulk flow, thermal heating, or non-thermal particle acceleration. While the pathways of this transformation from magnetic energy into kinetic have been studied extensively in recent years through theoretical or case-by-case observations, comprehensive statistical studies remain limited. In this paper, we present a statistical investigation using data from the Magnetospheric Multiscale (MMS) mission, and detail the particle energization mechanisms in magnetic structures found near reconnecting regions in turbulent Earth's magnetotail. We find that electrons with motion perpendicular to the magnetic field dominate $\vec{j}$ ⋅ $\vec{E}$ dissipation. In contrast to the conventional picture of unidirectional energy transfer to particles by laminar two-dimensional (2D) reconnection, we find that energy exchange within magnetic structures during turbulent reconnection tends to be bidirectional with only a small positive bias from electromagnetic fields to particles. Specific electron energization mechanisms are quantified, including those due to parallel electric field, Fermi energization from curvature drift, betatron heating from magnetic field inhomogeneity, and polarization drift.

Wang, Rachel [Princeton University, NJ (United Sta↗
DOE OSTI2020

The MFiX Particle-in-Cell Method (MFiX-PIC) Theory Guide

MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy

This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic capability of the filter reduced the efficiency of challenging particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Efficient computation of N -point correlation functions in D dimensions

We present efficient algorithms for computing the N-point correlation functions (NPCFs) of random fields in arbitrary D-dimensional homogeneous and isotropic spaces. Such statistics appear throughout the physical sciences and provide a natural tool to describe stochastic processes. Typically, algorithms for computing the NPCF components have $\mathscr O$(n N ) complexity (for a dataset containing n particles); their application is thus computationally infeasible unless N is small. By projecting the statistic onto a suitably defined angular basis, we show that the estimators can be written in a separable form, with complexity $\mathscr O$(n 2 ) or $\mathscr O$(n g log n g ) if evaluated using a Fast Fourier Transform on a grid of size n g . Our decomposition is built upon the D-dimensional hyperspherical harmonics; these form a complete basis on the (D – 1) sphere and are intrinsically related to angular momentum operators. Concatenation of (N – 1) such harmonics gives states of definite combined angular momentum, forming a natural separable basis for the NPCF. As N and D grow, the number of basis components quickly becomes large, providing a practical limitation to this (and all other) approaches: However, the dimensionality is greatly reduced in the presence of symmetries; for example, isotropic correlation functions require only states of zero combined angular momentum. We provide a Julia package implementing our estimators and show how they can be applied to a variety of scenarios within cosmology and fluid dynamics. The efficiency of such estimators will allow higher-order correlators to become a standard tool in the analysis of random fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Study of Whisper Statistics for Criticality Safety Code Validation

Whisper is computational software designed to assist with validation studies with the Monte Carlo particle transport package MCNP. Historical approaches rely on expert based judgment to select benchmarks while Whisper uses sensitivity/uncertainty (S/U) methods to select relevant benchmarks to a particular application model. Whisper will also derive a calculational margin and margin of subcriticality (MOS) that may be used to generate an upper subcritical limit (USL) for a particular application model. Whisper finds benchmark experiments whose isotope sensitivity profiles align best with the MCNP model in question, and assign a correlation coefficient ('ck' in Whisper parlance) to each selected benchmark as a measure of how closely those profiles agree. When ck values are high, there is less uncertainty between model and benchmarks and a higher USL is possible, relative to cases when ck values are low. The default Whisper settings recommends a 100,000 particles per cycle run for 500 active cycles, resulting in 50 M active neutron histories per application run. This is computationally intensive and may not be necessary to produce accurate results. A Whisper statistical study was done varying the neutron histories per cycle from 10,000 to the default 100,000. A lower number of neutron histories will allow Whisper to be used on low memory systems such as laptops. The intent of this paper is to present the changes in the Whisper results as the number of neutrons histories run is varied.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Numerical error analysis of SOLPS-ITER simulations of EAST

Abstract Plasma edge simulations with codes like SOLPS-ITER are widely employed to interpret fusion experiments. However, numerical errors appearing in such simulations are rarely investigated, despite their potential large impact on simulation results. These errors consist of the statistical error and the bias, both resulting from the finite number of employed EIRENE Monte Carlo particles and incomplete convergence, and the discretization error due to the finite resolution of the computational grids. In this contribution, the resulting numerical errors on simulations of pure deuterium and neon seeded H-mode EAST discharges are examined. The statistical error can be kept small compared to other numerical error contributions by averaging the plasma profiles. This allows investigating the bias and discretization errors using Richardson extrapolation. It is shown that grid refinement and the number of employed Monte Carlo particles have the largest influence on the result, in agreement with similar studies of an ITER deuterium case. For the first time, numerical error bars on the entire simulated target profiles are determined showing that the largest numerical error is 17.9%, mainly due to the plasma grid discretization. On top, also numerical errors on simulated neutral pressures are investigated in detail, for which the statistical error is dominant. The analysis demonstrates which setup is needed to keep numerical errors limited: the SOLPS-ITER averaging procedure should be employed including enough EIRENE particles, and the involved grids should be sufficiently refined to reduce discretization errors.

Boeyaert, Dieter (ORCID:0000000309208660)↗
DOE OSTI2020

Sterilization of N95 Respirators via Gamma Radiation: Comparison of Post-sterilization Efficacy

This study evaluated gamma irradiation for sterilization and reuse of two models of N95 respirators after gamma radiation sterilization as a method to increase availability of N95 respirators during a shortage. The Sandia National Laboratories Gamma Irradiation Facility was used to irradiate two different models of N95 filtering facepiece respirators at doses ranging from 0 kGy(tissue) to 50 kGy(tissue). The following tests were used to determine the efficacy of the respirator after irradiation sterilization: Ambient Aerosol Condensation Nuclei Counter Quantitative Fit Test, tensile test, strain cycling, oscillatory dynamic mechanical analysis, microscopic image analysis of fiber layers, and electrostatic field measurements. Both of the respirator models exhibited statistically significant changes after gamma irradiation as shown by the Quantitative Fit Test, electrostatic testing and the aerosol testing. The change in electrostatic charge of the filter was correlated with a reduction in capturing particles near the 200 nm size by approximately 40-50%. Both tested respirators showed statistically significant changes associated with gamma sterilization. However, our results indicate that choices in materials and manufacturing methods to achieve N95 filtration lead to different magnitudes of damage when exposed to gamma radiation at sterilization relevant doses. This damage results in lower filtration performance. While our sample size (2 different types of respirators) was small, we did observe a change in electrostatic properties on a filter layer that coincided with the failure on the Quantitative Fit Test and reduction in aerosol filtering efficiency. Key Words: N95 respirators, respirators, airborne transmission, pandemic prevention, COVID-19, gamma sterilization

COVID-19↗
DOE OSTI2023

Measurement of the production of (anti)nuclei in p–Pb collisions at s NN = 8.16 TeV

Measurements of (anti)proton, (anti)deuteron, and (anti)3He production in the rapidity range -1< y < 0 as a function of the transverse momentum and event multiplicity in p–Pb collisions at a center-of-mass energy per nucleon–nucleon pair $\sqrt{S_{NN}}$ = 8.16 TeV are presented. The coalescence parameters B 2 and B 3 , measured as a function of the transverse momentum per nucleon and of the mean charged-particle multiplicity density, confirm a smooth evolution from low to high multiplicity across different collision systems and energies. The ratios between (anti)deuteron and (anti) 3 He yields and those of (anti)protons are also reported as a function of the mean charged-particle multiplicity density. A comparison with the predictions of the statistical hadronization and coalescence models for different collision systems and center-of-mass energies favors the coalescence description for the deuteron-to-proton yield ratio with respect to the canonical statistical model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

3D Quantification of Elemental Gradients within Heterostructured Particles of Battery Cathodes

Heterogenous architectures with elemental gradients tailored within particles have been pursued to combat the instabilities limiting Ni-rich cathode materials for lithium-ion batteries. The growth of different compositional layers is accomplished during the synthesis of hydroxide precursors. However, the extent to which these concentration gradients are modified during high-temperature reactions is difficult to establish in their intact, spherical form. Here, we show the entire three-dimensional structure of a secondary particle can be resolved nondestructively with differential X-ray absorption spectroscopy (XAS) through transmission X-ray microscopy (TXM). The relationship between particle location and elemental content was fully quantified, with high statistical significance, for heterostructures possessing different compositional gradients in the precursors with 90:5:5 Ni:Mn:Co core compositions. Reduced elemental heterogeneity was observed after high-temperature synthesis, but gradients remained. In conclusion, the methodology presented should be used to guide synthesis while assuring that gains in electrochemical performance are linked to precise elemental distributions at the nanoscale.

25 ENERGY STORAGE↗
DOE OSTI2026

Stress and Strain Heterogeneity and Persistence in Uniaxially‐ and Triaxially‐Loaded Sandstone

Two critical questions in brittle rock mechanics are how rocks developed localized strains and to what extent internal stress heterogeneity controls this localization and subsequent macroscopic failure. Definitive answers have not yet been found, but would provide insight into rock fracture mechanics as relevant to hydrocarbon extraction and sequestration. Here, we use synchrotron X‐ray tomography (XRT) and 3D X‐ray diffraction (3DXRD) during uniaxial and triaxial tests on Nugget and Bentheimer sandstones to examine strain and stress localization prior to mechanical failure. 3DXRD was used to measure intra‐granular lattice strains which were used to compute elastic stress tensors of each grain. Digital volume correlation (DVC) was applied to XRT images to determine the strain field in the sample. Both samples featured marked spatial heterogeneity, localization, and temporal persistence of elevated stresses and strains during their mechanical deformation toward failure. Both samples featured a majority of grains with at least one principal stress component that was tensile, a signature of the influence of heterogeneity on stress transmission. Measurements further revealed that compressive stress orientations and statistics evolved in a similar manner to those of inter‐particle forces in loose granular materials, with triaxially‐compressed rock exhibiting enhanced grain stress heterogeneity compared to uniaxially‐compressed rock. Our results complement recent work by others who employed XRT and scanning 3DXRD to study triaxially‐compressed sandstone, but extend those results to uniaxial compression, sandstones of varied porosity, and grain stress measurements throughout the 3D full extent of the samples rather than in a single layer examined with scanning 3DXRD.

58 GEOSCIENCES↗
DOE OSTI2021

Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning

Abstract Particle accelerators are invaluable discovery engines in the chemical, biological and physical sciences. Characterization of the accelerated beam response to accelerator input parameters is often the first step when conducting accelerator-based experiments. Currently used techniques for characterization, such as grid-like parameter sampling scans, become impractical when extended to higher dimensional input spaces, when complicated measurement constraints are present, or prior information known about the beam response is scarce. Here in this work, we describe an adaptation of the popular Bayesian optimization algorithm, which enables a turn-key exploration of input parameter spaces. Our algorithm replaces the need for parameter scans while minimizing prior information needed about the measurement’s behavior and associated measurement constraints. We experimentally demonstrate that our algorithm autonomously conducts an adaptive, multi-parameter exploration of input parameter space, potentially orders of magnitude faster than conventional grid-like parameter scans, while making highly constrained, single-shot beam phase-space measurements and accounts for costs associated with changing input parameters. In addition to applications in accelerator-based scientific experiments, this algorithm addresses challenges shared by many scientific disciplines, and is thus applicable to autonomously conducting experiments over a broad range of research topics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Background rejection in NEXT using deep neural networks

Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

The use of minimal spanning trees in particle physics

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Machine learning techniques in searches for$t\bar{t}$h in the h → $b\bar{b}$ decay channel

Study of the production of pairs of top quarks in association with a Higgs boson is one of the primary goals of the Large Hadron Collider over the next decade, as measurements of this process may help us to understand whether the uniquely large mass of the top quark plays a special role in electroweak symmetry breaking. Higgs bosons decay predominantly to b$\bar{_b}$, yielding signatures for the signal that are similar to t$\bar{_t}$ + jets with heavy flavor. Though particularly challenging to study due to the similar kinematics between signal and background events, such final states (t$\bar{_t}$b$\bar{b}$) are an important channel for studying the top quark Yukawa coupling. This paper presents a systematic study of machine learning (ML) methods for detecting t$\bar{_t}$h in the h → b$\bar{b}$ decay channel. Among the seven ML methods tested, we show that neural network models outperform alternative methods. In addition, two neural models used in this paper outperform NeuroBayes, one of the standard algorithms used in current particle physics experiments. We further study the effectiveness of ML algorithms by investigating the impact of feature set and data size, as well as the depth of the networks for neural models. We demonstrate that an extended feature set leads to improvement of performance over basic features. Furthermore, the availability of large samples for training is found to be important for improving the performance of the techniques. For the features and the data set studied here, neural networks of more layers deliver comparable performance to their simpler counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗