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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Impact of in situ nuclear networks and atomic opacities on neutron star merger ejecta dynamics, nucleosynthesis, and kilonovae

Context. Binary neutron star merger (BNSM) ejecta are key sites of rapid neutron capture (r-process) nucleosynthesis and they produce kilonovae powered by the radioactive decay of freshly synthesized nuclei. Modeling their evolution requires multi-physics simulations involving hydrodynamics, nuclear reactions, and radiative processes. The impact of nuclear burning and atomic opacity is poorly understood and often treated with simplified prescriptions. Aims. We systematically investigate different treatments of nuclear heating, particle thermalization, and atomic opacities in radiation-hydrodynamics simulations of BNSM ejecta and kilonova light curves. Methods. Ejecta profiles from long-term numerical-relativity simulations of asymmetric neutron star binaries with a massive neutron star remnant were evolved to ∼30 days using a 2D ray-by-ray approach. We compared simplified heating-rate and thermalization prescriptions with in situ Nuclear reaction Network (NN) calculations that track nuclear energy deposition and include a composition-dependent thermalization scheme. We also contrasted various gray opacity models with a frequency-dependent treatment based on atomic calculations. Results. Coupling NN and hydrodynamics significantly affects nucleosynthesis and kilonova emission. Assuming homologous expansion alters abundance evolution and produces a narrower, less populated second r-process peak and a third peak shifted to higher mass numbers. The back-reaction of nuclear heating affects the temperature evolution enough to delay and redden the early (t∼ hours) kilonova peaks. A constant thermalization efficiency underestimates and reddens the early emission while overestimating the late-time luminosity compared to the composition-dependent treatment. Analytical opacity prescriptions yield a more extended, colder photosphere, resulting in dimmer, redder kilonovae at early times (t≲ hour), while the delayed recession of the photosphere prolongs the red emission at t ≳ 5 days. Conclusions. Coupling hydrodynamics to an in situ NN is crucial for reliable nucleosynthesis and kilonova predictions. Resolving the first several hundred milliseconds of the hydrodynamics is essential for robust nucleosynthesis calculations. Composition-dependent thermalization and frequency-dependent, atomic-physics-based opacities are needed to accurately capture the temperature evolution of the ejecta and the brightness and color evolution of the kilonova. Calibrated analytic nuclear-power fits with simplified thermalization and opacity prescriptions can still reproduce the density and temperature evolution of the ejecta.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Physics-informed hybrid modeling methodology for building infiltration

Infiltration is responsible for one-third to one-half of the space conditioning load of a typical residential home, but the modeling of infiltration for building energy modeling is either represented by over-simplified equations or dependent on over-generalized rules of thumb. Here, this paper develops a physics-informed data-driven methodology for modeling infiltration using building-specific empirical measurements. The developed hybrid methodology combines machine-learning categorization and grey-box sub-modeling to improve the accuracy and generalization of commonly used grey-box infiltration models. The developed methodology excels at predicting infiltration by improving the ability to predict infiltration under unseen environmental conditions using machine learning algorithms with physical significance. In a case study conducted using the iUnit, a modular studio apartment experimental test facility located at the National Renewable Energy Laboratory, we use empirical airtightness measurements to fit an infiltration model using the developed methodology. We find that the developed methodology can improve the overall model accuracy by 43% and improve extrapolation by 38%, compared with the model based on the common grey-box infiltration equation. We also notice that the selected features can improve the performance of a pure machine-learning model, indicating that our methodology identifies the features with the most physical significance to infiltration modeling.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1978

OSO 8 X-ray spectra of clusters of galaxies. I - Observations of twenty clusters: Physical correlations

OSO 8 X-ray spectra from 2 to 20 keV have been analyzed for 26 clusters of galaxies. For 20 clusters temperatures, emission integrals, iron abundances, and low-energy absorption measurements are presented. The data give, in general, better fits to thermal bremsstrahlung than to power-law models. Eight clusters have positive iron emission-line detections at the 90% confidence level, and all 20 cluster spectra are consistent with Fe/H = 0.000014 by number with the possible exception of Virgo. Thus it is confirmed that X-ray emission in this energy band is predominantly thermal radiation from hot intracluster gas rather than inverse Compton radiation. Physical correlations between X-ray spectral parameters and other cluster properties are examined. It is found that (1) the X-ray temperature is approximately proportional to the square of the velocity dispersion of the galaxies; (2) the emission integral is a strong function of the X-ray temperature; (3) the X-ray temperature and emission integral are better correlated with cluster central-galaxy density than with richness; and (4) the fraction of galaxies which are spirals is correlated with the observed ram pressure in the cluster core.

Mushotzky, R. F.↗
DOE OSTI2021

Parton distributions and lattice-QCD calculations: Toward 3D structure

The strong force which binds hadrons is described by the theory of quantum chromodynamics (QCD). Determining the character and manifestations of QCD is one of the most important and challenging outstanding issues necessary for a comprehensive understanding of the structure of hadrons. Within the context of the QCD parton picture, the parton distribution functions (PDFs) have been remarkably successful in describing a wide variety of processes. However, these PDFs have generally been confined to the description of collinear partons within the hadron. New experiments and facilities provide the opportunity to additionally explore the transverse structure of hadrons which is described by generalized parton distributions (GPDs) and transverse-momentum-dependent parton distribution functions (TMD PDFs). In our previous report Lin et al. (2018), we compared and contrasted the two main approaches used to determine the collinear PDFs: the first based on perturbative QCD factorization theorems, and the second based on lattice-QCD calculations. In the present report, we provide an update of recent progress on the collinear PDFs, and also expand the scope to encompass the generalized PDFs (GPDs and TMD PDFs). We review the current state of the various calculations, and consider what new data might be available in the near future. We also examine how a shared effort can foster dialog between the PDF and lattice-QCD communities, and yield improvements for these generalized PDFs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Experimental Modal Analysis Using Phase Quantities from Phase-Based Motion Processing and Motion Magnification

Phase-based motion processing and the associated Motion Magnification that it enables has become popular not only for the striking videos that it can produce of traditionally stiff structures visualized with very large deflections, but also for its ability to pull information out of the noise floor of images so that they can be processed with more traditional optical techniques such as digital image correlation or feature tracking. While the majority of papers in the literature have utilized the Phase-based Image Processing approach as a pre-processor for more quantitative analyses, the technique itself can be used directly to extract modal parameters from an image, noting that the extracted phases are proportional to displacements in the image. Therefore, once phases are extracted, they can be fit using traditional experimental modal analysis techniques. This produces a mode “shape” where the degrees of freedom are phases instead of physical motions. These phases can be scaled to produce on-image visualizations of the mode shapes, rather than operational shapes produced by bandpass filtering. Modal filtering techniques can also be used to visualize motions from an environment on an image using the modal phases as a basis for the expansion.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantitative analysis of Cu XANES spectra using linear combination fitting of binary mixtures simulated by FEFF9

We report x-ray absorption near edge structure (XANES) is a powerful tool to probe the fingerprint of local structures, and when coupled with X-ray microscopy, the small spot size enables one to probe very specific regions of interest in a device or material, e.g. interfaces/bulk, different grains, good/bad electrical areas. In this work, we investigate the use of linear combination fitting (LCF) of XANES spectra for the particular case of Cu doping in CdTe. We show that the experimental data seem to be accurately represented by standards of Cu 2 Te and its substoichiometric counterpart, Cu 1.43 Te. We use Cu in CdTe as a case study to evaluate the accuracy of linear combination fitting using simulated standards, given that experimental standards for certain phases (e.g. Cu 1.43 Te) or defect structures (e.g. vacancies) cannot be readily obtained. We discuss how spectral features of the FEFF9-simulated standards, fitting ranges, and noise levels all dictate the accuracy of this type of analysis. We show that the greater the spectral difference between the two standards, the better the LCF is able to differentiate between the two structures and to tolerate experimental noise. Finally, we estimate the error of the fitted weights for different spectral features and noise levels and propose a framework to study local structures semi-quantitatively by using binary mixtures of FEFF9-simulated standards.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Thermal conductivity tensor of β -1,3,5,7-tetranitro-1,3,5,7-tetrazoctane ( β -HMX) as a function of pressure and temperature

Here, we have used reverse non-equilibrium molecular dynamics (RNEMD) simulations to determine the full thermal conductivity tensor for the monoclinic high explosive crystal β-1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (β-HMX). In order to do so for the monoclinic crystal, four directions for heat propagation are used. Effects of the temperature and pressure are investigated between 200 and 500 K and 0 and 5 GPa, respectively, which approximately covers the range where the β polymorph is stable. Simulations are carried out with the Smith–Bharadwaj non-reactive empirical potential [Smith and Bharadwaj, J. Phys. Chem. B 103, 3570 (1999)], which is known to reproduce well the thermo-elastic properties of HMX. Our results indicate that the thermal conductivity, κ, is highly anisotropic, with 36% difference between the two extreme values at 300 K and 0 GPa. A simple function is used to interpolate κ in the pressure-temperature regime considered in this study, which can be used in continuum models. The results from RNEMD simulations compare well with available experimental results from the literature and allow the determination of κ for any direction and temperature and pressure within or around the fitting interval.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A low-luminosity core-collapse supernova very similar to SN 2005cs

ABSTRACT We present observations and analysis of PSN J17292918+7542390, a low-luminosity Type II-P supernova (LL SN IIP). The observed sample of such events is still low, and their nature is still under debate. Such SNe are similar to SN 2005cs, a well-observed LL Type II-P event, having low expansion velocities, and small ejected 56Ni mass. We have developed a robust and relatively fast Monte Carlo code that fits semi-analytic models to light curves of core-collapse SNe. This allows the estimation of the most important physical parameters, like the radius of the progenitor star, the mass of the ejected envelope, the mass of the radioactive nickel synthesized during the explosion, among others. PSN J17292918+7542390 has $R_0 = 91_{-70}^{+119} \times 10^{11} \, \text{cm}$, $M_\text{ej} = 9.89_{-1.00}^{+2.10} \, \mathrm{ M}_{\odot }$, $E_{\mbox{kin}} = 0.65_{-0.18}^{+0.19} \, \text{foe}$, and $v_{\mbox{exp}} = 3332_{-347}^{+216}$ km s−1, for its progenitor radius, ejecta mass, kinetic energy, and expansion velocity, respectively. The initial nickel mass of the PSN J17292918+7542390 turned out to be $1.55_{-0.70}^{+0.75} \times 10^{-3} \, \mathrm{M}_{\odot }$. The measured photospheric velocity at the earliest observed phase is 7000 km s−1. As far as we can tell based on the small population of observed LL SNe IIP, the determined values are typical for these events.

Jäger, Jr, Zoltán↗
DOE OSTI2021

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Comparing the observed brightness of various buried objects is a straightforward way to characterize the performance of a ground penetrating radar (GPR) system. However, a limitation arises. A simple comparison of buried object brightness values does not disentangle the effects of the GPR system itself from the system's operating environment and the objects being observed. Therefore, with brightness values exhibiting an unknown synthesis of systemic, environmental, and object factors, GPR system analysis becomes a convoluted affair. In this work, we use an experimentally collected dataset of over 25,000 object observations from five different multistatic radar arrays to develop models of buried object brightness and control for these various effects. Additionally, our modeling efforts provide a means for quantifying the relative brightness of GPR systems, the objects they detect, and the physical properties of those objects that influence observed brightness. To evaluate the models' performance on new object observations, we repeatedly simulate fitting them to half the dataset and predicting the observed brightness values of the unseen half. In addition, we introduce a method for estimating the probability that individual observations constitute a visible object, which aids in failure analysis, performance characterization, and dataset cleaning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Applied Strategy for Using Empirical and Hybrid Models in Online Monitoring

The monitoring of plant equipment for failure prediction is one of the key contributors to operation and maintenance (O&M) costs for a nuclear power plant (NPP) because O&M monitoring depends on labor-intensive activities that are required to meet high equipment reliability standards. These activities rely primarily on humans for information gathering, condition diagnosis, and predictive analysis. Online monitoring aims to automate these activities by relying on sensors to replace human information gathering and machine learning to replace human analysis and decision making. To facilitate automated monitoring, a systematic strategy for anomaly detection is needed to optimally use the available sensor data, empirical models, and physics-supported models. This strategy is essential to provide credible reasoning on why and when an empirical (i.e., purely data-driven) versus hybrid (i.e., physics-supported) approach should be used and to determine the ideal mix of these two approaches for a defined anomaly detection scope. The extant methods usually adopt an ad hoc trial-and-error approach that, in addition to being time-consuming and costly, is also highly subjective; it is impacted by the background and the skill set of the personnel making the decisions. Thus, such an approach cannot guarantee an optimum outcome. This represents the motivation of the current research effort, which is focused on devising a scientifically supported strategy for the optimum selection of anomaly detection methods. This report presents a detailed assessment of the main anomaly detection techniques within the empirical or hybrid method streams. Empirical methods include pattern, statistical, and causal inference. Hybrid methods include the use of physics models to train and test data methods, reduce data dimensionality, reduce data-model complexity, augment data, and reduce empirical uncertainty; hybrid methods also include the use of data to tune physics models. The listed techniques within these two streams represent the vast majority of techniques performed for anomaly detection. Using the techniques as outcomes, a strategy was developed to enable a systematic decision-making process to lead to one of these techniques. The strategy is driven by key decision points related to data relevance, simple modeling feasibility, data inference, physics-modeling value, data dimensionality, physics knowledge, method of validation, performance, data availability and suitability for training and testing, cause-effect, entropy inference, and model fitting. Each of these decision points in the strategy is explained in detail in this report with examples, along with the scientific basis behind the decisions and outcomes in common and simplified terminology. The strategy is developed for use by any NPP staff with basic engineering or science knowledge. A user-friendly graphical state flow diagram was also developed as a visual presentation of the strategy. The strategy was tested and demonstrated through two pilot projects for the application of anomaly detection at an NPP. Each pilot had two use cases: an initial case in which certain decisions were made that resulted in one or more empirical techniques and a revised use case where one or more key decisions were modified resulting in using a set of hybrid methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1980

Repeatable, multiple-peaked structure in Type I X-ray bursts

A distinct, identifiable, multiple-peaked structure has been observed in Type I X-ray bursts from three sources. At energies below 6 keV, the light curves look like typical Type I bursts. At higher energies, the burst is double-peaked, with both the depth of the dip and the separation between the peaks increasing with energy. A light curve of the energy-integrated intensity shows no distinct double peak, suggesting that only a single energy release occurs. Blackbody fits to the evolving burst spectra yield changing radii and temperatures, inversely correlated, during the early part of the burst. The physical interpretation of these changes is uncertain. Burst decay spectra yield relatively constant radii with decreasing temperatures. It is proposed that Compton scattering may be responsible for the dips in the higher-energy light curves by shifting photons to lower energies.

Hoffman, J. A.↗
NASA NTRS2023

JWST Observations of the Enigmatic Y Dwarf WISE 1828+2650: I. Limits to a Binary Companion

The Y-dwarf WISE 1828+2650 is one of the coldest known brown dwarfs with an effective temperature of∼300K. Located at a distance of just 10 pc, previous model-based estimates suggest WISE1828+2650 has a mass of∼5–10MJ, making it a valuable laboratory for understanding the formation, evolution, and physical characteristics of gas giant planets. However, previous photometry and spectroscopy have presented a puzzle, with the near impossibility of simultaneously fitting both the short-(0.9–2.0μm)and long-wavelength(3–5μm)data. A potential solution to this problem has been the suggestion that WISE 1828+2650 is a binary system whose composite spectrum might provide a better match to the data. Alternatively, new models being developed to fit JWST/NIRSpec, and MIRI spectroscopy might provide new insights. This article describes JWST/NIRCam observations of WISE 1828+2650 in six filters to address the binarity question and to provide new photometry tobe used in model fitting. We also report adaptive optics imaging with the Keck I0 m telescope. We find no evidence for multiplicity for a companion beyond 0.5 au with either JWST or Keck. Companion articles will present low-and high-resolution spectra of WISE 1828 obtained with both NIRSpec and MIRI.

NIRCam↗
DOE OSTI2020

New method for fitting coefficients in standard model effective theory

We present an alternative method for carrying out a principal-component analysis of Wilson coefficients in standard model effective field theory (SMEFT). The method is based on singular-value decomposition (SVD). The SVD method provides information about the sensitivity of experimental observables to physics beyond the standard model that is not accessible in the Fisher-information method. In principle, the SVD method can also have computational advantages over diagonalization of the Fisher information matrix. We demonstrate the SVD method by applying it to the dimension-6 coefficients for the process of top-quark decay to a b quark and a W boson and use this example to illustrate some pitfalls in widely used fitting procedures. We also outline an iterative procedure for applying the SVD method to dimension-8 SMEFT coefficients.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1997

Attributes of GRB Pulses: Analysis of BATSE TTE Data

Our extensive program of modeling GRB profiles is aimed at elucidating the physical processes responsible for the burst itself, as well as possible extrinsic phenomena (e.g. time dilation) as discussed in another paper in this Symposium (Norris et al., "GRB PROFILES AS COSMIC PROBES"). We have developed special methods to extract the wealth of short time-scale information contained in the BATSE time-tag event (TTE) data. Our algorithm yields a piecewise-constant representation of the light curve -- using only the raw photon arrival times, and based on Bayesian change-point methods. This representation in effect lets the data determine the bin size and location, and avoids unwanted effects due to arbitrary choices of the bin parameters. We have determined widths, separations, and amplitudes of pulses contained in the bursts, without invoking a specific pulse model. The effect of cosmic time dilation can be easily seen in a direct plot of amplitude vs. time scale for individual pulses, without the need to lump the data into a small number of brightness classes. We are also performing noise equalization on these data (to reduce a well-known bias of pulse width as a function of signal-to-noise ratio), as well as fits of parametric pulse-shape models -- including explicit energy dependence of the pulse parameters. Such refinements are expected to improve the quality and physical significance of these results.

Norris, J. P.↗
DOE OSTI2020

Distributing User Code with the CernVM FileSystem

The CernVM FileSystem (CVMFS) is widely used in High Throughput Computing to efficiently distributed experiment code. However, the standard CVMFS publishing tools are designed for a small group of people from each experiment to maintain common software, and the tools are not a good fit for publishing software from numerous users in each experiment. As a result, most user code, such as code to do specific physics analyses, is still sent with every job to the place the job is run. That process is relatively inefficient, especially when the user code is large. To overcome these limitations, we have built a CVMFS user code publication system. This publication system enables users to still submit their code with their jobs but the code is distributed and accessed through the standard CVMFS infrastructure. The user code is automatically deleted from CVMFS after a period of no use. Most of the software for the system is available as a single self-contained open source rpm called cvmfs-user-pub and is available for other deployments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗
DOE OSTI2023

Optical polarization and spectral properties of the hydrogen-poor superluminous supernovae SN 2021bnw and SN 2021fpl

ABSTRACT New optical photometric, spectroscopic, and imaging polarimetry data are combined with publicly available data to study some of the physical properties of the two hydrogen-poor superluminous supernovae (SLSNe) SN 2021bnw and SN 2021fpl. For each SLSN, the best-fitting parameters obtained from the magnetar model with Modular Open-Source Fitter for Transients do not depart from the range of parameter obtained on other SLSNe discussed in the literature. A spectral analysis with SYN++ shows that SN 2021bnw is a W type, fast evolver, while SN 2021fpl is a 15bn type, slow evolver. The analysis of the polarimetry data obtained on SN 2021fpl at four epochs (+1.8, +20.6, +34.1, and +43.0 d, rest frame) shows >3σ polarization detections in the range of 0.8–1 per cent. A comparison of the spectroscopy data suggests that SN 2021fpl underwent a spectral transition a bit earlier than SN 2015bn, during which, similarly, it could have underwent a polarization transition. The analysis of the polarimetry data obtained on SN 2021bnw does not show any departure from symmetry of the photosphere at an empirical diffusion time-scale of ≈2 (+81.1 d rest frame). This result is consistent with those on the sample of W-type SLSN observed at empirical diffusion time-scale ≤ 1 with that technique, even though it is not clear the effect of limited spectral windows varying from one object to the other. Measurements at higher empirical diffusion time-scale may be needed to see any departure from symmetry as it is discussed in the literature for SN 2017egm.

Astronomy & Astrophysics↗