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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics‐based iterative reconstruction for dual‐source and flying focal spot computed tomography

Purpose For single‐source helical Computed Tomography (CT), both Filtered‐Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual‐source CT with flying focal spot (DS‐FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics‐based iterative reconstruction method for DS‐FFS CT and assess its image quality. Methods Our algorithm uses precise physics models to reconstruct from the native cone‐beam geometry and interleaved dual‐source helical trajectory of a DS‐FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x‐rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x‐ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. Results We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS‐FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task‐Based Modulation Transfer Function (MTF task ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. Conclusions By modeling a precise geometry and avoiding data rebinning or interpolation, our physics‐based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.

Wang, Xiao↗
DOE OSTI2019

Modeling inter-particle magnetic correlations in magnetite nanoparticle assemblies using x-ray magnetic scattering data

Magnetic nanoparticles are increasingly used in nanotechnologies and biomedical applications, such as drug targeting, MRI, bio-separation. Magnetite (Fe 3 O 4 ) nanoparticles stand to be effective in these roles due to the non-toxic nature of magnetite and its ease of manufacture. To be more effective in these applications, a greater understanding of the magnetic behavior of a collection of magnetite nanoparticles is needed. This research seeks to discover the local magnetic ordering of ensembles of magnetite nanoparticles occurring under various external fields. To complete this study, we use x-ray resonant magnetic scattering (XRMS). Here we discuss the modeling of the magnetic scattering data using a one-dimensional chain of nanoparticles with a mix of ferromagnetic, anti-ferromagnetic, and random orders. By fitting the model to the experimental data, we extracted information about the magnetic correlations in the nanoparticle assembly.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2011

FORWARD MODELING CAVITY DENSITY: A MULTI-INSTRUMENT DIAGNOSTIC

The thermodynamic properties of coronal prominence cavities present a unique probe into the energy and mass budget of prominences. Using a three-dimensional morphological model, we forward model the polarization brightness and extreme-ultraviolet (EUV) emission of a cavity and its surrounding streamer. Using a genetic algorithm, we find the best-fit density model by comparing the models to Mauna Loa Solar Observatory MK4 and Hinode EUV Imaging Spectrometer data. The effect of temperature variations on the derived density is also measured. We have measured the density inside a cavity down to 1.05 R{sub sun} with height-dependent error bars. Our forward modeling technique compensates for optically thin projection effects. This method provides a complementary technique to traditional line ratio diagnostics that is useful for diffuse off-limb coronal structures.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Active galactic nuclei, neutrinos, and interacting cosmic rays in NGC 253 and NGC 1068

The galaxies M82, NGC 253, NGC 1068, and NGC 4945 have been detected in γ-rays by Fermi. Previously, we developed and tested a model for cosmic-ray interactions in the starburst galaxy M82. Now, we aim to explore the differences between starburst and active galactic nucleus (AGN) environments by applying our self-consistent model to the starburst galaxy NGC 253 and the Seyfert galaxy NGC 1068. Assuming a constant cosmic-ray acceleration efficiency by supernova remnants with Milky Way parameters, we calculate the cosmic-ray proton and primary and secondary electron/positron populations, predict the radio and γ-ray spectra, and compare with published measurements. We find that our models easily fit the observed γ-ray spectrum for NGC 253 while constraining the cosmic-ray source spectral index and acceleration efficiency. However, we encountered difficultly modeling the observed radio data and constraining the speed of the galactic wind and the magnetic field strength, unless the gas mass is less than currently preferred values. Additionally, our starburst model consistently underestimates the observed γ-ray flux and overestimates the radio flux for NGC 1068; these issues would be resolved if the AGN is the primary source of γ-rays. We discuss the implications of these results and make predictions for the neutrino fluxes for both galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗
DOE OSTI2017

Treatment with integrase inhibitor suggests a new interpretation of HIV RNA decay curves that reveals a subset of cells with slow integration

The kinetics of HIV-1 decay under treatment depends on the class of antiretrovirals used. Mathematical models are useful to interpret the different profiles, providing quantitative information about the kinetics of virus replication and the cell populations contributing to viral decay. We modeled proviral integration in short- and long-lived infected cells to compare viral kinetics under treatment with and without the integrase inhibitor raltegravir (RAL). We fitted the model to data obtained from participants treated with RAL-containing regimes or with a four-drug regimen of protease and reverse transcriptase inhibitors. Our model explains the existence and quantifies the three phases of HIV-1 RNA decay in RAL-based regimens vs. the two phases observed in therapies without RAL. Our findings indicate that HIV-1 infection is mostly sustained by short-lived infected cells with fast integration and a short viral production period, and by long-lived infected cells with slow integration but an equally short viral production period. We propose that these cells represent activated and resting infected CD4+ T-cells, respectively, and estimate that infection of resting cells represent ~4% of productive reverse transcription events in chronic infection. RAL reveals the kinetics of proviral integration, showing that in short-lived cells the pre-integration population has a half-life of ~7 hours, whereas in long-lived cells this half-life is ~6 weeks. We also show that the efficacy of RAL can be estimated by the difference in viral load at the start of the second phase in protocols with and without RAL. Altogether, we provide a mechanistic model of viral infection that parsimoniously explains the kinetics of viral load decline under multiple classes of antiretrovirals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

The Clustering of High-redshift (2.9 ≤ $z$ ≤ 5.1) Quasars in SDSS Stripe 82

We present a measurement of the two-point autocorrelation function of photometrically selected high-$z$ quasars over ~100 deg 2 on the Sloan Digital Sky Survey Stripe 82 field. Selection is performed using three machine-learning algorithms in a six-dimensional optical/mid-infrared color space. Optical data from the Sloan Digital Sky Survey are combined with overlapping deep mid-infrared data from the $Spitzer$ IRAC Equatorial Survey and the $Spitzer$-HETDEX Exploratory Large-Area survey. Our selection algorithms are trained on the colors of known high-$z$ quasars. The selected quasar sample consists of 1378 objects and contains both spectroscopically confirmed quasars and photometrically selected quasar candidates. These objects span a redshift range of 2.9 ≤ $z$ ≤ 5.1 and are generally fainter than $i$ = 20.2, a regime that has lacked sufficient number density to perform autocorrelation function measurements of photometrically classified quasars. We compute the angular correlation function of these data, marginally detecting quasar clustering. We fit a single power law with an index of $δ$ = 1.39 ± 0.618 and amplitude of $θ_0$ = 0$^{\prime}_{.}$71 ± 0$^{\prime}_{.}$546 . A dark matter model is fit to the angular correlation function to estimate the linear bias. At the average redshift of our survey ($\langle z\rangle =3.38$), the bias is $b$ = 6.78 ± 1.79. Using this bias, we calculate a characteristic dark matter halo mass of 1.70–9.83$\times {10}^{12}{h}^{-1}\,{M}_{\odot }$. Our bias estimate suggests that quasar feedback intermittently shuts down the accretion of gas onto the central supermassive black hole at early times. If confirmed, these results hint at a level of luminosity dependence in the clustering of quasars at high-$z$.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A hydride reorientation model for irradiated zirconium alloy cladding

This paper presents a hydride reorientation model for irradiated, but de-fueled, zirconium-based alloy cladding tubes for water cooled reactors subjected to temperature and stress conditions of interest to storage of commercial spent nuclear fuel under dry, inert conditions. The present development builds upon and updates an existing hydride precipitation model developed in 2003, and was motivated by recent findings that the old model underestimated the amount of radial hydrides for applied hoop stresses below 100 MPa. The data used to support the development of the new model were obtained from an NFIR-sponsored experimental program on de-fueled cladding tubes irradiated in light-water reactors. The irradiated material consisted of three types of zirconium-based alloys sampled from high burnup fuel rods: cold-worked stress-relieved (CWSR) Zircaloy-4 (Zr-4), CWSR Zirlo{sup TM}, and recrystallized annealed (RXA) Zircaloy-2 (Zr-2). The majority of the hydride re-orientation tests for the irradiated cladding specimens were conducted under constant pressure with a mean cooling rate of about 1 Celsius degree/mn. The remaining tests were conducted at slower cooling rates with the pressure being kept proportional to the temperature change during the cooling process to simulate dry storage conditions. The parameters of the hydride re-orientation model were fitted to the test data. Fitting parameters were determined for (CWSR) Zircaloy-4 (Zr-4) and CWSR Zirlo{sup TM} respectively. The cooling rate effect was empirically accounted for in the model. General good agreement between model prediction and measured radial hydride content is achieved, with a standard error less than 12 ppm. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A fully Lagrangian, non-parametric bias model for dark matter halos

Here, we present a non-parametric Lagrangian biasing model and fit the ratio of the halo and mass densities at the field level using the mass-weighted halo field in the AbacusSummit simulations at z=0.5. Unlike the perturbative halo bias model that has been widely used in interpreting the observed large-scale structure traced by galaxies, we find a non-negative halo-to-mass ratio that increases monotonically with the linear overdensity δ 1 in the initial Lagrangian space. The bias expansion, however, does not guarantee non-negativity of the halo counts, and may lead to rising halo number counts at negative overdensities. The shape of the halo-to-mass ratio is unlikely to be described by a polynomial function of δ 1 and other quantities. Especially for massive halos with 6×10 12 h -1 M ⊙ , the halo-to-mass ratio starts soaring up at δ 1 >0, substantially different from the predictions of the bias expansion. We show that for the halo masses we consider (M>3×10 11 h -1 M ⊙ ) a non-parametric halo-to-mass ratio as a function of δ 1 and its local derivative ∇^2δ 1 can recover the halo power spectra to sub-percent level accuracy for wavenumbers k=0.01-0.1 h Mpc -1 given a proper smoothing scale to filter the initial density field, even though we do not fit the power spectrum directly. However, there is mild dependence of the recovery of the halo power spectrum on the smoothing scale and other input parameters. At k<0.01 h Mpc -1 and for massive halos with M>6x10 12 h -1 M ⊙ , our non-parametric model leads to a few percent overestimation of the halo power spectrum, indicating the need for larger or multiple smoothing scales. The halo-to-mass ratios obtained qualitatively agree with intuitions from extended Press-Schechter theory. We compare our framework to the bias expansion and discuss possible extensions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Maximize, minimize or target - optimization for a fitted response from a designed experiment

One of the common goals of running and analyzing a designed experiment is to find a location in the design space that optimizes the response of interest. Depending on the goal of the experiment, we may seek to maximize or minimize the response, or set the process to hit a particular target value. After the designed experiment, a response model is fitted and the optimal settings of the input factors are obtained based on the estimated response model. Furthermore, the suggested optimal settings of the input factors are then used in the production environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Modeling of Precipitation over Africa: Progress, Challenges, and Prospects

In recent years, there has been an increasing need for climate information across diverse sectors of society. This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change. Likewise, this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa. By leveraging a large volume of climate model outputs, numerous studies have investigated the model representation of African precipitation as well as underlying physical processes. These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies. This paper provides a review of the progress in precipitation simulation over Africa in state-of-the-science climate models and discusses the major issues and challenges that remain.

CMIP6↗
DOE OSTI2016

Planck 2015 results: XXIII. The thermal Sunyaev-Zeldovich effect-cosmic infrared background correlation

In this paper, we use Planck data to detect the cross-correlation between the thermal Sunyaev-Zeldovich (tSZ) effect and the infrared emission from the galaxies that make up the the cosmic infrared background (CIB). We first perform a stacking analysis towards Planck-confirmed galaxy clusters. We detect infrared emission produced by dusty galaxies inside these clusters and demonstrate that the infrared emission is about 50% more extended than the tSZ effect. Modelling the emission with a Navarro-Frenk-White profile, we find that the radial profile concentration parameter is c 500 = 1.00 +0.18 -0.15 . This indicates that infrared galaxies in the outskirts of clusters have higher infrared flux than cluster-core galaxies. We also study the cross-correlation between tSZ and CIB anisotropies, following three alternative approaches based on power spectrum analyses: (i) using a catalogue of confirmed clusters detected in Planck data; (ii) using an all-sky tSZ map built from Planck frequency maps; and (iii) using cross-spectra between Planck frequency maps. With the three different methods, we detect the tSZ-CIB cross-power spectrum at significance levels of (i) 6σ; (ii) 3σ; and (iii) 4σ. We model the tSZ-CIB cross-correlation signature and compare predictions with the measurements. The amplitude of the cross-correlation relative to the fiducial model is A tSZ-CIB = 1.2 ± 0.3. Finally, this result is consistent with predictions for the tSZ-CIB cross-correlation assuming the best-fit cosmological model from Planck 2015 results along with the tSZ and CIB scaling relations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Evaluation and prediction of color-tunable organic light-emitting diodes based on carrier/exciton adjusting interlayer

A color tuning index (I{sub CT}) parameter for evaluating the color change capability of color-tunable organic light-emitting diodes (CT-OLEDs) was proposed and formulated. And a series of CT-OLEDs, consisting of five different carrier/exciton adjusting interlayers (C/EALs) inserted between two complementary emitting layers, were fabricated and applied to disclose the relationship between I{sub CT} and C/EALs. The result showed that the trend of electroluminescence spectra behavior in CT-OLEDs has good accordance with I{sub CT} values, indicating that the I{sub CT} parameter is feasible for the evaluation of color variation. Meanwhile, by changing energy level and C/EAL thickness, the optimized device with the widest color tuning range was based on N,N′-dicarbazolyl-3,5-benzene C/EAL, exhibiting the highest I{sub CT} value of 41.2%. Based on carrier quadratic hopping theory and exciton transfer model, two fitting I{sub CT} formulas derived from the highest occupied molecular orbital (HOMO) energy level and triplet energy level were simulated. Finally, a color tuning prediction (CTP) model was developed to deduce the I{sub CT} via C/EAL HOMO and triplet energy levels, and verified by the fabricated OLEDs with five different C/EALs. We believe that the CTP model assisted with I{sub CT} parameter will be helpful for fabricating high performance CT-OLEDs with a broad range of color tuning.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Extragalactic interstellar extinction curves: Indicators of local physical conditions

Normalized interstellar extinction curves (ISECs) in the Milky Way and other galaxies show a variety of shapes. This variety is attributed to differences along different sight lines in the abundances of the several dust and gas components contributing to extinction. In this paper we propose that these abundance differences are not arbitrary but are a specific consequence of the physical conditions on those sight lines. If this proposal is correct, then it implies that ISECs contain information about physical conditions in the regions generating extinction. This may be particularly important for high redshift galaxies where information on the conditions may be difficult to obtain. We adopt a model of extinction carriers in which the solid and gaseous components are not immutable but respond time-dependently to the local physics. We validate this model by fitting extinction curves measured on sight lines in the Magellanic Clouds and obtained for the gamma-ray burst afterglow GRB 080605. We present results for this model as follows: (1) we show that computed ISECs are controlled by a small number of physical parameters, (2) we demonstrate the sensitivity of computed ISECs to these parameters, (3) we compute as examples ISECs for particular galaxy types, and (4) we note that different galaxy types have different shapes of ISEC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Confronting the concordance model of cosmology with Planck data

We confront the concordance (standard) model of cosmology, the spatially flat ΛCDM Universe with power-law form of the primordial spectrum with Planck CMB angular power spectrum data searching for possible smooth deviations beyond the flexibility of the standard model. The departure from the concordance cosmology is modeled in the context of Crossing statistic and statistical significance of this deviation is used as a measure to test the consistency of the standard model to the Planck data. Derived Crossing functions suggest the presence of some broad features in angular spectrum beyond the expectations of the concordance model. Our results indicate that the concordance model of cosmology is consistent to the Planck data only at 2 to 3σ confidence level if we allow smooth deviations from the angular power spectrum given by the concordance model. This might be due to random fluctuations or may hint towards smooth features in the primordial spectrum or departure from another aspect of the standard model. Best fit Crossing functions indicate that there are lack of power in the data at both low-ℓ and high-ℓ with respect to the concordance model. This hints that we may need some modifications in the foreground modeling to resolve the significant inconsistency at high-ℓ. However, presence of some systematics at high-ℓ might be another reason for the deviation we found in our analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

How microscopic epistasis and clonal interference shape the fitness trajectory in a spin glass model of microbial long-term evolution

The adaptive dynamics of evolving microbial populations takes place on a complex fitness landscape generated by epistatic interactions. The population generically consists of multiple competing strains, a phenomenon known as clonal interference. Microscopic epistasis and clonal interference are central aspects of evolution in microbes, but their combined effects on the functional form of the population’s mean fitness are poorly understood. Here, we develop a computational method that resolves the full microscopic complexity of a simulated evolving population subject to a standard serial dilution protocol. Through extensive numerical experimentation, we find that stronger microscopic epistasis gives rise to fitness trajectories with slower growth independent of the number of competing strains, which we quantify with power-law fits and understand mechanistically via a random walk model that neglects dynamical correlations between genes. We show that increasing the level of clonal interference leads to fitness trajectories with faster growth (in functional form) without microscopic epistasis, but leaves the rate of growth invariant when epistasis is sufficiently strong, indicating that the role of clonal interference depends intimately on the underlying fitness landscape. The simulation package for this work may be found at https://github.com/nmboffi/spin_glass_evodyn .

59 BASIC BIOLOGICAL SCIENCES↗