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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Using neutrons and x rays to measure plasma conditions in a solid sphere of deuterated polyethylene compressed to densities of 35 g/cc at temperatures of 2 keV and pressures of 40 Gbar

This paper describes an experiment that shock compresses the center of a solid deuterated polyethylene sphere, CD 2 , to densities of 35 g/cc and temperatures of 2 keV with corresponding pressure of 40 Gbar. The design employs a strong spherically converging shock launched through a solid ball of material using a Hohlraum radiation drive. As the shock coalesces at the center it produces a hot spot that we characterize by measuring the x-ray self-emission and 2.45 MeV neutrons emitted. Two-dimensional images and time-resolved measurements of the x rays emitted determine the size and time duration of the hot spot, leading to an estimated 2k eV electron temperature. The neutron time of flight spectrometer measures an average ion temperature of 1.06 ± 0.15 keV and neutron yield of 7.0 (±0.5) × 10 9 DD neutrons. Our new distribution function tool enables us to create a forward model of the experimental data based on 1D radiation-hydrodynamic simulations, leading to a better understanding of the plasma conditions that produce the measured neutrons and x rays. Furthermore, our simulations indicate that the x rays are produced in a short-lived hot-dense core over tens of picoseconds, whereas the neutron emission continues for about 200 ps, as the hot core starts to expand, thereby leading to a lower mean temperature of the plasma during neutron production. This finding is in agreement with the experimental data, and we therefore conclude that the forward-modeling is a useful tool for inferring the conditions of the hot spot in a laser-driven implosion during burn.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Effects of mosaic crystal instrument functions on x-ray Thomson scattering diagnostics

Mosaic crystals, with their high integrated reflectivities, are widely employed in spectrometers used to diagnose high energy density systems. X-ray Thomson scattering (XRTS) has emerged as a powerful diagnostic tool of these systems, providing in principle direct access to important properties such as the temperature via detailed balance. However, the measured XRTS spectrum is broadened by the spectrometer instrument function (IF), and without careful consideration of the IF one risks misdiagnosing system conditions. Here, we consider in detail the IF of 40 and 100 μm mosaic Highly Annealed Pyrolytic Graphite crystals, and how the broadening varies across the spectrometer in an energy range of 6.7–8.6 keV. Notably, we find a strong asymmetry in the shape of the IF toward higher energies. As an example, we consider the effect of the asymmetry in the IF on the temperature inferred via XRTS for simulated 80 eV CH plasmas and find that the temperature can be overestimated if an approximate symmetric IF is used. We, therefore, expect a detailed consideration of the full IF will have an important impact on system properties inferred via XRTS in both forward modeling and model-free approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

SUPER STRONG MAGNETIC FIELDS OF NEUTRON STARS IN BE X-RAY BINARIES ESTIMATED WITH NEW TORQUE AND MAGNETOSPHERE MODELS

We re-estimate the surface magnetic fields of neutron stars (NSs) in Be X-ray binaries (BeXBs) with different models of torque, improved beyond Klus et al. In particular, a new torque model is applied to three models of magnetosphere radius. Unlike the previous models, the new torque model does not lead to divergent results for any fastness parameter. The inferred surface magnetic fields of these NSs for the two compressed magnetosphere models are much higher than that for the uncompressed magnetosphere model. The new torque model using the compressed magnetosphere radius leads to unique solutions near spin equilibrium in all cases, unlike other models that usually give two branches of solutions. Although our conclusions are still affected by the simplistic assumptions about the magnetosphere radius calculations, we show several groups of possible surface magnetic field values with our new models when the interaction between the magnetosphere and the infalling accretion plasma is considered. The estimated surface magnetic fields for NSs BeXBs in the Large Magellanic Cloud, the Small Magellanic Cloud and the Milk Way are between the quantum critical field and the maximum “virial” value by the spin equilibrium condition.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

NuSTAR OBSERVATIONS OF THE COMPTON-THICK ACTIVE GALACTIC NUCLEUS AND ULTRALUMINOUS X-RAY SOURCE CANDIDATE IN NGC 5643

We present two Nuclear Spectroscopic Telescope Array (NuSTAR) observations of the local Seyfert 2 active galactic nucleus (AGN) and an ultraluminous X-ray source (ULX) candidate in NGC 5643. Together with archival data from Chandra, XMM-Newton, and Swift-BAT, we perform a high-quality broadband spectral analysis of the AGN over two decades in energy (∼0.5–100 keV). Previous X-ray observations suggested that the AGN is obscured by a Compton-thick (CT) column of obscuring gas along our line of sight. However, the lack of high-quality ≳10 keV observations, together with the presence of a nearby X-ray luminous source, NGC 5643 X–1, have left significant uncertainties in the characterization of the nuclear spectrum. NuSTAR now enables the AGN and NGC 5643 X–1 to be separately resolved above 10 keV for the first time and allows a direct measurement of the absorbing column density toward the nucleus. The new data show that the nucleus is indeed obscured by a CT column of N{sub H} ≳ 5 × 10{sup 24} cm{sup −2}. The range of 2–10 keV absorption-corrected luminosity inferred from the best-fitting models is L{sub 2–10,int} = (0.8–1.7) × 10{sup 42} erg s{sup −1}, consistent with that predicted from multiwavelength intrinsic luminosity indicators. In addition, we also study the NuSTAR data for NGC 5643 X–1 and show that it exhibits evidence of a spectral cutoff at energy E ∼ 10 keV, similar to that seen in other ULXs observed by NuSTAR. Along with the evidence for significant X-ray luminosity variations in the 3–8 keV band from 2003 to 2014, our results further strengthen the ULX classification of NGC 5643 X–1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Learning how structures form in drift-wave turbulence

Drift-wave turbulence produces anomalous transport via cross-correlations between fluctuations. This transport has profound implications for confinement, structure formation, and virtually all aspects of the non-linear turbulent dynamics. Herein, we use a data-driven method based on deep learning in order to study turbulent transport in the 2D Hasegawa–Wakatani system and infer a reduced mean-field model from numerical solution. In addition to the usual turbulent diffusion, we find an effect which couples the particle flux to the local gradient of vorticity, which tends to modulate the density profile. The direct coupling to the shear is relatively weak. In addition, the deep learning method finds a model for spontaneous zonal flow generation by negative viscosity, stabilized by non-linear and hyperviscous terms. We compare these results to analytic calculations using quasilinear theory and wave kinetics, finding qualitative agreement, though the calculations miss certain higher-order effects. A simplified, 1-D model for the evolution of the profile, flow, and intensity based on the deep learning results is solved numerically and compared to previous models for staircasing based on bistability. We see that the physics uncovered by the deep learning method provided simple explanations for the formation of zonal structures in the density, flow, and turbulence fields. We highlight the important role of symmetry in the deep learning method and speculate on the portability of the method to other applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Probing cosmology and gravity with redshift-space distortions around voids

Cosmic voids in the large-scale structure of the Universe affect the peculiar motions of objects in their vicinity. Although these motions are difficult to observe directly, the clustering pattern of their surrounding tracers in redshift space is influenced in a unique way. This allows to investigate the interplay between densities and velocities around voids, which is solely dictated by the laws of gravity. With the help of N-body simulations and derived mock-galaxy catalogs we calculate the average density fluctuations around voids identified with a watershed algorithm in redshift space and compare the results with the expectation from general relativity and the ΛCDM model. We find linear theory to work remarkably well in describing the dynamics of voids. Adopting a Bayesian inference framework, we explore the full posterior of our model parameters and forecast the achievable accuracy on measurements of the growth rate of structure and the geometric distortion through the Alcock-Paczyński effect. Systematic errors in the latter are reduced from ∼15% to ∼5% when peculiar velocities are taken into account. The relative parameter uncertainties in galaxy surveys with number densities comparable to the SDSS MAIN (CMASS) sample probing a volume of 1h{sup −3}Gpc{sup 3} yield σ{sub f/b}/(f/b).∼2% (20%) and σ{sub D{sub A{sub H}}}/D{sub AH∼0}.2% (2%), respectively. At this level of precision the linear-theory model becomes systematics dominated, with parameter biases that fall beyond these values. Nevertheless, the presented method is highly model independent; its viability lies in the underlying assumption of statistical isotropy of the Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A Conditional Autoencoder for Galaxy Photometric Parameter Estimation

Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Simultaneous inference of plate boundary stresses and mantle rheology using adjoints: large-scale 2-D models

SUMMARY Plate motions are a primary surface constraint on plate and mantle dynamics and rheology, plate boundary stresses and the occurrence of great earthquakes. Within an optimization method, we use plate motion data to better constrain uncertain mantle parameters. For the optimization problem characterizing the maximum a posteriori rheological parameters we derive gradients using adjoints and expressions to approximate the posterior distributions for stresses within plate boundaries. We apply these methods to a 2-D cross section from the western to eastern Pacific, with temperature distributions and fault zone geometries developed primarily from seismic and plate motion data. We find that the best-fitting stress exponent, n, is about 2.8 and the yield stress about 100 MPa or less. The normal stress on the interplate fault zones is about 100 MPa and the shear stresses about 10 MPa or less.

Rudi, Johann (ORCID:0000000265639265)↗
DOE OSTI2022

The half-mass radius of MaNGA galaxies: effect of IMF gradients

Gradients in the stellar populations (SP) of galaxies – e.g. in age, metallicity, stellar initial mass function (IMF) – can result in gradients in the stellar-mass-to-light ratio, M * / L . Such gradients imply that the distribution of the stellar mass and light is different. For old SPs, e.g. in early-type galaxies at z ∼ 0, the M * / L gradients are weak if driven by variations in age and metallicity, but significantly larger if driven by the IMF. A gradient which has larger M * / L in the centre increases the estimated total stellar mass ( M * ) and reduces the scale which contains half this mass ( R e,* ), compared to when the gradient is ignored. For the IMF gradients inferred from fitting MILES simple SP models to the H β, 〈Fe〉, [MgFe], and TiO 2SDSS absorption lines measured in spatially resolved spectra of early-type galaxies in the MaNGA survey, the fractional change in R e,* can be significantly larger than that in M * , especially when the light is more centrally concentrated. The R e,* – M * correlation which results from accounting for IMF gradients is offset to smaller sizes by 0.3 dex compared to when these gradients are ignored. Comparisons with ‘quiescent’ galaxies at higher z must account for evolution in SP gradients (especially age and IMF) and in the light profile before drawing conclusions about how R e,* and M * evolve. Here, the implied merging between higher z and the present is less contrived if R e,* / R e at z ∼ 0 is closer to our IMF-driven gradient calibration than to unity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

A non-linear solution to the S 8 tension – II. Analysis of DES Year 3 cosmic shear

ABSTRACT Weak galaxy lensing surveys have consistently reported low values of the S8 parameter compared to the Planck lambda cold dark matter (ΛCDM) cosmology. Amon & Efstathiou used KiDS-1000 cosmic shear measurements to propose that this tension can be reconciled if the matter fluctuation spectrum is suppressed more strongly on non-linear scales than assumed in state-of-the-art hydrodynamical simulations. In this paper, we investigate cosmic shear data from the Dark Energy Survey (DES) Year 3. The non-linear suppression of the matter power spectrum required to resolve the S8 tension between DES and the Planck ΛCDM model is not as strong as inferred using KiDS data, but is still more extreme than predictions from recent numerical simulations. An alternative possibility is that non-standard dark matter contributes to the required suppression. We investigate the redshift and scale dependence of the suppression of the matter power spectrum. If our proposed explanation of the S8 tension is correct, the required suppression must extend into the mildly non-linear regime to wavenumbers $k\sim 0.2 \, h\, {\rm Mpc}^{-1}$. In addition, all measures of S8 using linear scales should agree with the Planck ΛCDM cosmology, an expectation that will be testable to high precision in the near future.

Preston, Calvin (ORCID:0000000281136916)↗
DOE OSTI2025

Generic Hilbert space fragmentation in Kogut-Susskind lattice gauge theories

At the heart of quantum many-body physics lies the understanding of mechanisms that avoid quantum thermalization in an isolated system quenched far from equilibrium. A prominent example is Hilbert space fragmentation, which has recently emerged as an ergodicity-breaking mechanism in constrained spin models. Here, we show that Kogut-Susskind formulations of lattice gauge theories in d+1D (d spatial and one temporal dimensions) give rise to Hilbert space fragmentation, and discuss possible implications for understanding continuum physics. Lastly, our findings not only prove that lattice gauge theories are a natural platform for Hilbert space fragmentation, they also serve as a guide to the conditions under which these models can be faithfully used to infer the thermalization properties of quantum chromodynamics.

Eigenstate thermalization↗
DOE OSTI2019

Diamond Magnetic Microscopy of Malarial Hemozoin Nanocrystals

Magnetic microscopy of malarial hemozoin nanocrystals is performed by optically detected magnetic resonance imaging of near-surface diamond nitrogen-vacancy centers. Hemozoin crystals are extracted from Plasmodium falciparum–infected human blood cells and studied alongside synthetic hemozoin crystals. The stray magnetic fields produced by individual crystals are imaged at room temperature as a function of the applied field up to 350 mT. More than 100 nanocrystals are analyzed, revealing the distribution of their magnetic properties. Most crystals (96%) exhibit a linear dependence of the stray-field magnitude on the applied field, confirming hemozoin’s paramagnetic nature. A volume magnetic susceptibility of 3.4 × 10 –4 is inferred with use of a magnetostatic model informed by correlated scanning-electron-microscopy measurements of crystal dimensions. A small fraction of nanoparticles (4/82 for Plasmodium falciparum–produced nanoparticles and 1/41 for synthetic nanoparticles) exhibit a saturation behavior consistent with superparamagnetism. Translation of this platform to the study of living Plasmodium-infected cells may shed new light on hemozoin formation dynamics and their interaction with antimalarial drugs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

GW190412: Observation of a binary-black-hole coalescence with asymmetric masses

We report the observation of gravitational waves from a binary-black-hole coalescence during the first two weeks of LIGO's and Virgo's third observing run. The signal was recorded on April 12, 2019 at 05:30:44 UTC with a network signal-to-noise ratio of 19. The binary is different from observations during the first two observing runs most notably due to its asymmetric masses: A ~30 M ⊙ black hole merged with a ~8 M ⊙ black hole companion. The more massive black hole rotated with a dimensionless spin magnitude between 0.22 and 0.60 (90% probability). Asymmetric systems are predicted to emit gravitational waves with stronger contributions from higher multipoles, and indeed we find strong evidence for gravitational radiation beyond the leading quadrupolar order in the observed signal. A suite of tests performed on GW190412 indicates consistency with Einstein's general theory of relativity. While the mass ratio of this system differs from all previous detections, we show that it is consistent with the population model of stellar binary black holes inferred from the first two observing runs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Relating Product Prices to Long‐Run Marginal Cost: Evidence from Solar Photovoltaic Modules

Abstract A basic tenet of microeconomics is that for a competitive industry in equilibrium the market price of a product will be equal to its marginal cost. This paper develops a model framework and a corresponding empirical inference procedure for estimating long‐run marginal cost in industries where production costs decline over time. In the context of the solar photovoltaic (PV) module industry, we rely primarily on firm‐level financial accounting data to estimate the long‐run marginal cost of PV modules for the years 2008–2013. During those years, the industry experienced both unprecedented price declines and significant expansions of manufacturing capacity. We compare the trajectory of average sales prices with the estimated long‐run marginal costs in order to quantify the extent to which actual price declines were attributable to reductions in production costs. The trajectory of estimated product costs is then extrapolated to forecast an equilibrium trend line for future PV module prices.

Reichelstein, Stefan↗
DOE OSTI2020

Foliar functional traits from imaging spectroscopy across biomes in eastern North America

Summary Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes. With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America. Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently. Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

Wang, Zhihui↗
DOE OSTI2016

Optimal test selection for prediction uncertainty reduction

Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecise data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Framework For Spatial Agricultural Crop Yield Prediction Model Development

This framework was developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modelling using artificial neural networks (ANNs) to predict subfield crop yield estimates. The software includes methods to train, validate, and test ANN models. It also include methods to infer on new remote sensing data.

Griffel, LloydM.↗
DOE OSTI2021

Multisensor Agile Adaptive Sampling of Convective Storms Driven by Real-time Analytics

Convective storms vertically transport water vapor and condensate from Earth’s surface to the upper troposphere. Life on Earth is fundamentally linked to this transport which determines the hydrological cycle, and the intensity of severe weather responsible for the destruction of life and property. Despite advances in high-resolution modeling and better observational capabilities, the scientific community continues to be confronted with knowledge gaps about convective storms that limit our predictive capabilities. The ongoing developments in the high-resolution Energy Exascale Earth System Model (E3SM), large eddy simulations, and AI-based analytics to evaluate uncertainties are expected to provide a comprehensive framework for new scientific discovery. The model-experiment (MODEX) approach suggests that the aforementioned advancements in model development and AI-based inference techniques should be complemented by similar advancements in the experimental (observational) side so that the former does not outstrip the ability of the latter to provide meaningful constraints. What are the recent advancements in observations that will provide the necessary leap forward in improving our predictive capabilities? To address this question, we propose a new experimental paradigm called Multisensor Agile Adaptive Sampling (MAAS) that capitalizes on advancements in communications (5G), computational resources (edge/fog computing), sensor capabilities, and machine learning (ML) and AI techniques (Kollias et al., 2020). The MAAS framework allows for the collection of higher spatiotemporal resolution and quality observations of convective storms than is traditionally possible. The MAAS framework is scalable and applicable to atmospheric observatories such as those operated by the Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) facility.

54 ENVIRONMENTAL SCIENCES↗