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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2021

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Bayesian Inference applied to the Solubility of Phase-3 and Phase-5

This Section covers an introduction to the objectives and techniques used in this analysis. The objectives of the report are given in Subsection 1.1. An introduction to aqueous thermodynamics and how variance might propagate through the relevant thermodynamic equations is given in Subsection 1.2. An introduction to Bayesian inference and its application to thermodynamic modeling is given in Subsection 1.3.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Expanded genetic variation (SNP) and phenomics (image based) dataset for Populus trichocarpa

The image dataset consists of 11,791 images representing 1,219 genotypes of Populus trichocarpa undergoing in planta regeneration. Genotypes were imaged with a median of four weekly timepoints and a median of two replicates each. A representative and diverse subset of 249 images was annotated using the IDEAS annotation interface (ideas.eecs.oregonstate.edu) and these annotated images were used to train a deep semantic segmentation model (PSPNet), which was deployed for inference over the entire dataset. Annotated classes include specific stages of regeneration (callus and shoot) in addition to unregenerated plant material and background. Statistics of relative tissue area were extracted and used for downstream genetic association mapping in a genome-wide association study. The SNP dataset consists of over 40 million single-nucleotide polymorphisms across 1,323 wild accessions of Populus trichocarpa

09 BIOMASS FUELS↗
DOE OSTI2016

DATA-DRIVEN RADIATIVE HYDRODYNAMIC MODELING OF THE 2014 MARCH 29 X1.0 SOLAR FLARE

Spectroscopic observations of solar flares provide critical diagnostics of the physical conditions in the flaring atmosphere. Some key features in observed spectra have not yet been accounted for in existing flare models. Here we report a data-driven simulation of the well-observed X1.0 flare on 2014 March 29 that can reconcile some well-known spectral discrepancies. We analyzed spectra of the flaring region from the Interface Region Imaging Spectrograph ( IRIS ) in Mg ii h and k, the Interferometric BIdimensional Spectropolarimeter at the Dunn Solar Telescope (DST/IBIS) in H α 6563 Å and Ca ii 8542 Å, and the Reuven Ramaty High Energy Solar Spectroscope Imager ( RHESSI ) in hard X-rays. We constructed a multithreaded flare loop model and used the electron flux inferred from RHESSI data as the input to the radiative hydrodynamic code RADYN to simulate the atmospheric response. We then synthesized various chromospheric emission lines and compared them with the IRIS and IBIS observations. In general, the synthetic intensities agree with the observed ones, especially near the northern footpoint of the flare. The simulated Mg ii line profile has narrower wings than the observed one. This discrepancy can be reduced by using a higher microturbulent velocity (27 km s{sup −1}) in a narrow chromospheric layer. In addition, we found that an increase of electron density in the upper chromosphere within a narrow height range of ≈800 km below the transition region can turn the simulated Mg ii line core into emission and thus reproduce the single peaked profile, which is a common feature in all IRIS flares.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

A search for O{sub 2} in co-depleted molecular cloud cores with Herschel

The general lack of molecular oxygen in molecular clouds is an outstanding problem in astrochemistry. Extensive searches with the Submillimeter Astronomical Satellite, Odin, and Herschel have only produced two detections; upper limits to the O{sub 2} abundance in the remaining sources observed are about 1000 times lower than predicted by chemical models. Previous atomic oxygen observations and inferences from observations of other molecules indicated that high abundances of O atoms might be present in dense cores exhibiting large amounts of CO depletion. Theoretical arguments concerning the oxygen gas–grain interaction in cold dense cores suggested that, if O atoms could survive in the gas after most of the rest of the heavy molecular material has frozen out onto dust, then O{sub 2} could be formed efficiently in the gas. Using Herschel HIFI, we searched a small sample of four depletion cores—L1544, L694-2, L429, and Oph D—for emission in the low excitation O{sub 2} N {sub J} = 3{sub 3}–1{sub 2} line at 487.249 GHz. Molecular oxygen was not detected and we derive upper limits to its abundance in the range of N(O{sub 2})/N(H{sub 2}) ≈ (0.6--1.6) × 10{sup −7}. We discuss the absence of O{sub 2} in the light of recent laboratory and observational studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Fermi Bubble Edges: Spectrum and Diffusion Function

Current measurements of the γ-ray Fermi bubbles (FB) are based on model-dependent tracers, carry substantial systematic uncertainties, and contain some discrepancies between each other. We show that gradient filters pick out the FB edges, which are found to smoothly connect to the bipolar X-ray structure emanating from the Galactic center, thus supporting the interpretation of the FBs as a Galactic-scale phenomenon. The sharp edges facilitate a direct, model-free measurement of the peripheral FB spectrum. The result is strikingly similar to the full FB-integrated spectrum, softened by a power law of index η ≃ (0.2–0.3). This is naturally explained, in both hadronic and leptonic models, if cosmic rays are injected at the edge, and diffuse away preferentially at higher energies E. The inferred, averaged diffusion function in the (more plausible) leptonic model, D(E)≃10{sup 29.5}(E/10GeV){sup 0.48±0.02} cm{sup 2} s{sup −1}, is consistent with estimates for Kraichnan-like turbulence. Our results, in particular the minute spatial variations in η, indicate that the FB edge is a strong, Mach ≳5, forward shock.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. In this work, we train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Weak-lensing Detection of Intercluster Filaments in Three Nearby Cluster Systems

Abstract Direct detection of intercluster filaments is challenging due to their low surface density, resulting in a weak deflection field. We present weak-lensing detections of intercluster filaments using wide-field Dark Energy Camera observations from the Local Volume Complete Cluster Survey. A matched-filter method was applied to identify filamentary structures in three nearby ( z < 0.1) systems centered on A401, A2029, and A3558. We discover two filaments (>3 σ ) in each system, with the strongest detections (5.2 σ –5.8 σ ) around A401 and A2029. In particular, we report the first robust weak-lensing detections (≳5 σ ) of the intercluster bridges connecting the cluster pairs A401/399, A2029/2033, A2029/SIG, and A3558/3556. Adopting a filament convergence model motivated by numerical simulations, we infer the maximum convergence ( κ 0 ) and characteristic width ( h c ) for all six filaments, yielding κ 0 ∼ 0.016–0.040 and h c ∼ 0.23–0.43 Mpc. The performance of the matched-filter technique is validated using mock shear catalogs and further tested on a null field around A2351. We explore the potential of using the B-mode lensing signal of filaments to suppress cluster-induced shear contamination. We also quantify the biasing effect of closely separated terminal clusters to the filament signal. These results demonstrate the feasibility of directly mapping dark matter filaments with current and future wide-field weak-lensing datasets.

Shinde, Rahul [Brown University] (ORCID:0000000273↗