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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Reconstructing large-scale structure with neutral hydrogen surveys

Upcoming 21-cm intensity surveys will use the hyperfine transition in emission to map out neutral hydrogen in large volumes of the universe. Unfortunately, large spatial scales are completely contaminated with spectrally smooth astrophysical foregrounds which are orders of magnitude brighter than the signal. This contamination also leaks into smaller radial and angular modes to form a foreground wedge, further limiting the usefulness of 21-cm observations for different science cases, especially cross-correlations with tracers that have wide kernels in the radial direction. Here in this paper, we investigate reconstructing these modes within a forward modeling framework. Starting with an initial density field, a suitable bias parameterization and non-linear dynamics to model the observed 21-cm field, our reconstruction proceeds by {combining} the likelihood of a forward simulation to match the observations (under given modeling error and a data noise model) {with the Gaussian prior on initial conditions and maximizing the obtained posterior}. For redshifts z=2 and 4, we are able to reconstruct 21cm field with cross correlation, r c > 0.8 on all scales for both our optimistic and pessimistic assumptions about foreground contamination and for different levels of thermal noise. The performance deteriorates slightly at z=6. The large-scale line-of-sight modes are reconstructed almost perfectly. We demonstrate how our method also provides a technique for density field reconstruction for baryon acoustic oscillations, outperforming standard methods on all scales. We also describe how our reconstructed field can provide superb clustering redshift estimation at high redshifts, where it is otherwise extremely difficult to obtain dense spectroscopic samples, as well as open up a wealth of cross-correlation opportunities with projected fields (e.g. lensing) which are restricted to modes transverse to the line of sight.

21 cm↗
NASA NTRS2009

Strong Solar Control of Infrared Aurora on Jupiter: Correlation Since the Last Solar Maximum

Polar aurorae in Jupiter's atmosphere radiate throughout the electromagnetic spectrum from X ray through mid-infrared (mid-IR, 5 - 20 micron wavelength). Voyager IRIS data and ground-based spectroscopic measurements of Jupiter's northern mid-IR aurora, acquired since 1982, reveal a correlation between auroral brightness and solar activity that has not been observed in Jovian aurora at other wavelengths. Over nearly three solar cycles, Jupiter auroral ethane emission brightness and solar 10.7 cm radio flux and sunspot number are positively correlated with high confidence. Ethane line emission intensity varies over tenfold between low and high solar activity periods. Detailed measurements have been made using the GSFC HIPWAC spectrometer at the NASA IRTF since the last solar maximum, following the mid-IR emission through the declining phase toward solar minimum. An even more convincing correlation with solar activity is evident in these data. Current analyses of these results will be described, including planned measurements on polar ethane line emission scheduled through the rise of the next solar maximum beginning in 2009, with a steep gradient to a maximum in 2012. This work is relevant to the Juno mission and to the development of the Europa Jupiter System Mission. Results of observations at the Infrared Telescope Facility (IRTF) operated by the University of Hawaii under Cooperative Agreement no. NCC5-538 with the National Aeronautics and Space Administration, Science Mission Directorate, Planetary Astronomy Program. This work was supported by the NASA Planetary Astronomy Program.

Kostiuk, T.↗
DOE OSTI2018

Effects of the magnetic field and zinc isotopes on the colony forming ability and elemental composition of E. coli bacterial cells

The combined effects of external weak magnetic fields and nuclear spins of the magnetic isotope {sup 67}Zn on vital functions of E. coli bacterial cells were proved experimentally. Metabolism of the major elements in E. coli depends on the intensity of the external static magnetic field and zinc isotopes contained in the nutrient medium. The combined effects of the external static magnetic field and zinc magnetic isotope {sup 67}Zn are observed in a change in the number of colony-forming units of E. coli bacteria in a range of 25–35 mT and in the content of Na, Ca and Mg in a range of 65–80 mT. The obtained data confirm magnetic sensitivity of intracellular enzymatic processes to the static magnetic field and magnetic moments of atomic nuclei.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Ground-state energy trends in single and multilayered coupled InAs/GaAs quantum dots capped with InGaAs layers: Effects of InGaAs layer thickness and annealing temperature

Graphical abstract: - Highlights: • Investigation of ground state energy in single and multi-layered InAs/GaAs QD. • Strain reducing layer (InGaAs) prevents the formation of non-radiative. • Strain reducing layer (InGaAs) is responsible for high activation energy. • Significant deviation from the Varshni model, E(T) = E − αT{sup 2}/T + β. - Abstract: Vertically coupled, multilayered InAs/GaAs quantum dots (QDs) covered with thin InGaAs strain-reducing layers (SRLs) are in demand for various technological applications. We investigated low temperature photoluminescence of single and multilayered structures in which the SRL thickness was varied. The SRL layer was responsible for high activation energies. Deviation of experimental data from the Varshni (1967) model, E(T) = E − ∞ T{sup 2}/T + β, suggests that the InAs-layered QDs have properties different from those in bulk material. Anomalous ground-state peak linewidths (FWHM), especially for annealed multilayer structures, were observed. A ground-state peak blue-shift with a broadened linewidth was also observed. Loss of intensity was detected in samples annealed at 800 °C. Presence of SRLs prevents formation of non-radiative centers under high temperature annealing. The results indicate the potential importance of such structures in optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

X-ray CT characterization and fracture simulation of ASR damage of glass particles in alkaline solution and mortar

The alkali-silica reaction (ASR) damage in reactive aggregates affects the long-term durability of the concrete infrastructure. The generated ASR gel can expand by imbibing water from the pore solution and the resulting expansion pressure causes the aggregate fracture. This study aims to simulate the development of ASR damage in glass particles of two types of samples (glass in alkali solution and glass mortars). The dynamic micro X-ray CT technique was conducted to monitor the crack propagation in glass aggregates at different reaction stages (up to 64 hrs). The Boundary Element Method (BEM) and Displacement Discontinuity Method (DDM) were used to efficiently simulate the crack propagation within irregular glass particles under gel expansion pressures. The aggregate boundaries were built with the BEM elements and the initial cracks were meshed with the DDM elements. The estimated expansion pressure was applied to the initial crack surfaces. The discontinuous displacements and stresses were calculated along the crack path and crack tip. The mixed-mode Stress Intensity Factors (SIFs) were calculated based on plane stress conditions. The maximum circumferential stress criteria was used to simulate the propagation of cracks along a specific angle. The simulation results include the simulated crack path and the combined SIFs changing with the increments. With the estimate expansion pressure, the glass particle damages were simulated within conditions of alkali solution and confined motar samples. The predicted crack propagation path was compared with the X-ray CT imaging data. The comparison results demonstrate the DDM has the ability to predicting the ASR damage propagation inside aggregates.

36 MATERIALS SCIENCE↗
DOE OSTI2016

More frequent intense and long-lived storms dominate the springtime trend in central US rainfall

Here, the changes in extreme rainfall associated with a warming climate have drawn significant attention in recent years. Mounting evidence shows that sub-daily convective rainfall extremes are increasing faster than the rate of change in the atmospheric precipitable water capacity with a warming climate. However, the response of extreme precipitation depends on the type of storm supported by the meteorological environment. Here using long-term satellite, surface radar and rain-gauge network data and atmospheric reanalyses, we show that the observed increases in springtime total and extreme rainfall in 36 the central U.S. are dominated by mesoscale convective systems (MCSs), the largest type of convective storm, with increased frequency and intensity of long-lasting MCSs. A strengthening of the southerly low-level jet and its associated moisture transport in the Central/Northern Great Plains, in the overall climatology and particularly on days with long-lasting MCSs, accounts for the changes in the precipitation produced by these storms.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Surface-based inversions above central Greenland: SBI ABOVE CENTRAL GREENLAND

[1] Surface-based temperature inversions (SBIs) are studied at Summit Station in central Greenland during the period spanning July 2010 to May 2012. The frequency and intensity of SBI are examined using microwave radiometer (MWR) temperature retrievals, radiosonde profiles, and near-surface meteorological data. Using the MWRs' high temporal resolution, the diurnal, monthly, and annual cycles are investigated. Monthly mean values in SBI occurrence and intensity show that surface-based inversions are prevalent in the winter with decreasing values in the summer months. A case study on 20 February 2011 suggests that factors other than solar elevation angle influence the intensity of surface-based inversions. An increase in liquid water path corresponds to a decrease in SBI intensity, suggesting that liquid-bearing clouds, especially within the lowest 1 km, are associated with weaker surface-based inversions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Changes in Four Decades of Near‐CONUS Tropical Cyclones in an Ensemble of 12 km Thermodynamic Global Warming Simulations

We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40‐year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6‐hourly TC snapshots in the control and find a corresponding “twin” in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture‐related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short‐term intensity variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Excitation-emission spectra and fluorescence quantum yields for fresh and aged biogenic secondary organic aerosols

Certain biogenic secondary organic aerosols (SOA) become absorbent and fluorescent when exposed to reduced nitrogen compounds such as ammonia, amines and their salts. Fluorescent SOA may potentially be mistaken for biological particles by detection methods relying on fluorescence. This work quantifies the spectral distribution and effective quantum yields of fluorescence of SOA generated from two monoterpenes, limonene and a-pinene, and two different oxidants, ozone (O3) and hydroxyl radical (OH). The SOA was generated in a smog chamber, collected on substrates, and aged by exposure to ~100 ppb ammonia vapor in air saturated with water vapor. Absorption and excitation-emission matrix (EEM) spectra of aqueous extracts of aged and control SOA samples were measured, and the effective absorption coefficients and fluorescence quantum yields (~0.005 for 349 nm excitation) were determined from the data. The strongest fluorescence for the limonene-derived SOA was observed for excitation = 420+- 50 nm and emission = 475 +- 38 nm. The window of the strongest fluorescence shifted to excitation = 320 +- 25 nm and emission = 425 +- 38 nm for the a-pinene-derived SOA. Both regions overlap with the excitation-emission matrix (EEM) spectra of some of the fluorophores found in primary biological aerosols. Our study suggests that, despite the low quantum yield, the aged SOA particles should have sufficient fluorescence intensities to interfere with the fluorescence detection of common bioaerosols.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

A Benchmark to Test Generalization Capabilities of Deep Learning Methods to Classify Severe Convective Storms in a Changing Climate

Abstract This is a test case study assessing the ability of deep learning methods to generalize to a future climate (end of 21st century) when trained to classify thunderstorms in model output representative of the present‐day climate. A convolutional neural network (CNN) was trained to classify strongly rotating thunderstorms from a current climate created using the Weather Research and Forecasting model at high‐resolution, then evaluated against thunderstorms from a future climate and found to perform with skill and comparatively in both climates. Despite training with labels derived from a threshold value of a severe thunderstorm diagnostic (updraft helicity), which was not used as an input attribute, the CNN learned physical characteristics of organized convection and environments that are not captured by the diagnostic heuristic. Physical features were not prescribed but rather learned from the data, such as the importance of dry air at mid‐levels for intense thunderstorm development when low‐level moisture is present (i.e., convective available potential energy). Explanation techniques also revealed that thunderstorms classified as strongly rotating are associated with learned rotation signatures. Results show that the creation of synthetic data with ground truth is a viable alternative to human‐labeled data and that a CNN is able to generalize a target using learned features that would be difficult to encode due to spatial complexity. Most importantly, results from this study show that deep learning is capable of generalizing to future climate extremes and can exhibit out‐of‐sample robustness with hyperparameter tuning in certain applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Intra-Specific Variability in Plant Hydraulic Parameters Inferred From Model Inversion of Sap Flux Data

Understanding plant hydraulic regulation is critical for predicting plant and ecosystem responses to projected increases in drought stress. Plant hydraulic regulation is controlled by observable, diverse plant hydraulic traits that can vary as much across individuals of the same species as they do across different species. Direct measurements of plant hydraulic traits from a range of ecosystems remain limited in comparison to other, more readily measured traits (e.g., specific leaf area). Furthermore, plant hydraulic trait measurements, often made at leaf or branch levels, are not easily scaled to whole-plant values that are typically used to predict plant and ecosystem fluxes. In this study, multiple whole-plant hydraulic parameters are inferred from observations of plant water use (i.e., sap flow), soil properties, and meteorological data. We use a Markov Chain Monte Carlo model inversion approach to obtain the best estimates and uncertainty of plant hydraulic parameters that capture whole-plant effective embolism resistance and stomatal sensitivity to decreasing plant water potential. We then use the inferred values in the model to estimate whole-tree water use and isohydricity. This approach reliably infers whole-plant parameter values with enough specificity to resolve inter- and intra-specific differences, and thus supplements time- and labor-intensive direct measurements of traits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Plant a Trillion Trees Campaign to Reduce Global Warming – Fleshing Out the Concept

The campaign to plant a trillion trees provided an easily understood approach to reduce the threat of global warming. However, focusing on trees does not consider that a maturing forest releases carbon (C) from dying trees offsetting C intake from growth of other trees, and results in only a one-time carbon storage benefit. Under sustainable management that harvests before forest growth slows down, forest C is transferred to wood products that substitute for fossil fuel (FF)- intensive products, reducing the carbon dioxide (CO 2 ) emissions with every harvest. Sustainably managed forests maintain growth by replanting (or regenerating), sustaining the C stored in products, as well as avoiding FF-intensive product emissions with every harvest. This paper addresses the question: Could planting more trees significantly reduce atmospheric CO 2 ; and if so, how? Survey data from many wood-producing mills show life cycle inventory (LCI) data on C that has been tracked through every stage of processing for many different wood products specific to each United States (USA) supply region, as well as C displacement measurements when substituting wood for FFintensive products. Results show that more and better-managed forest lands and more efficient product uses provide significant opportunities to reduce CO 2 emissions, especially in highly productive forest regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Tianlai dish array low-z surveys forecasts

Here, we present the science case for surveys with the Tianlai dish array interferometer tuned to the [1300, 1400] MHz frequency range. Starting from a realistic generation of mock visibility data according to the survey strategy, we reconstruct maps of the sky and perform foreground subtraction. We estimate the level of residuals from imperfect subtraction, mostly due to mode mixing, i.e. distortions in the reconstructed 3D maps due to frequency-dependent instrument response. We show that a survey of the North Celestial Polar cap during a year of observations, covering an area of 150 deg 2 , would reach a sensitivity of $1.5-2 mK per 1 MHz x 0.25 2 deg 2 voxel and be marginally impacted by mode mixing. Tianlai would be able to detect ~10 nearby massive H i clumps as well as a very strong cross-correlation signal of 21 cm intensity maps with the North Celestial Cap Survey optical galaxies. We also studied the performance of a mid-latitude survey, covering ~ 1500 deg 2 overlapping the SDSS footprint. Despite a higher noise level for the mid-latitude survey, as well as significant distortions due to mode mixing, Tianlai would be able to detect a highly significant cross-correlation between the 21 cm signal and the Sloan spectroscopic galaxy sample. Using the extragalactic signals measured from either or both of these surveys, and comparing them with simulations such as those presented here will make it possible to assess the impact of various instrumental imperfections on the Tianlai dish array performance. This would pave the way for future intensity mapping surveys with higher sensitivity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Desmearing small-angle scattering data by central moment expansions of instrument resolution

This study introduces an enhanced numerical technique tailored specifically for refining 1D small-angle scattering (SAS) intensity profiles affected by smearing. Our primary objective is to address the resolution blurring commonly encountered in SAS data, particularly in systems with clearly defined correlation peaks whose spread aligns with the width of the resolution function at corresponding Q positions. Unlike previous approaches that expanded the SAS intensity using central moment expansion, the new method focuses on expanding the resolution function itself, thus eliminating artificial oscillations observed in smeared spectra due to limitations inherent in our earlier algorithm. This method is straightforward to implement, computationally efficient and consistently performs well in numerical benchmarking. To illustrate its effectiveness, we present a case study of a lamellar phase characterized by distinct peaks in its small-angle neutron scattering intensities.

central moment expansions↗
DOE OSTI2014

Magnetic defects in chemically converted graphene nanoribbons: electron spin resonance investigation

Electronic spin transport properties of graphene nanoribbons (GNRs) are influenced by the presence of adatoms, adsorbates and edge functionalization. To improve the understanding of the factors that influence the spin properties of GNRs, local (element) spin-sensitive techniques such as electron spin resonance (ESR) spectroscopy are important for spintronics applications. Here, we present results of multi-frequency continuous wave (CW), pulse and hyperfine sublevel correlation (HYSCORE) ESR spectroscopy measurements performed on oxidatively unzipped graphene nanoribbons (GNRs), which were subsequently chemically converted (CCGNRs) with hydrazine. ESR spectra at 336 GHz reveal an isotropic ESR signal from the CCGNRs, of which the temperature dependence of its line width indicates the presence of localized unpaired electronic states. Upon functionalization of CCGNRs with 4-nitrobenzene diazonium tetrafluoroborate, the ESR signal is found to be 2 times narrower than that of pristine ribbons. NH{sub 3} adsorption/desorption on CCGNRs is shown to narrow the signal, while retaining the signal intensity and g value. The electron spin-spin relaxation process at 10 K is found to be characterized by slow (163 ns) and fast (39 ns) components. HYSCORE ESR data demonstrate the explicit presence of protons and {sup 13}C atoms. With the provided identification of intrinsic point magnetic defects such as proton and {sup 13}C has been reported, which are roadblocks to spin travel in graphene-based materials, this work could help in advancing the present fundamental understanding on the edge-spin (or magnetic)-based transport properties of CCGNRs.

36 MATERIALS SCIENCE↗
DOE OSTI2015

ATLAS jet trigger update for the LHC run II

The CERN Large Hadron Collider is the biggest and most powerful particle collider ever built. It produces up to 40 million proton-proton collisions per second at unprecedented energies to explore the fundamental laws and properties of Nature. The ATLAS experiment is one of the detectors that analyses and records these collisions. It generates dozens of GB/s of data that has to be reduced before it can be permanently stored, the event selection is made by the ATLAS trigger system, which reduces the data volume by a factor of 105. The trigger system has to be highly configurable in order to adapt to changing running conditions and maximize the physics output whilst keeping the output rate under control. A particularly interesting pattern generated during collisions consists of a collimated spray of particles, known as a hadronic jet. To retain the interesting jets and efficiently reject the overwhelming background, optimal jet energy resolution is needed. Therefore the Jet trigger software requires CPU-intensive reconstruction algorithms. In order to reduce the resources needed for the reconstruction step, a partial detector readout scheme was developed, which effectively suppresses the low activity regions of the calorimeter. In this paper we describe the overall ATLAS trigger software, and the jet trigger in particular, along with the improvements made on the system. We then focus on detailed studies of the algorithm timing and the performance impact of the full and partial calorimeter readout schemes. We conclude with an outlook of the jet trigger plans for the next LHC data-taking period. (authors)

43 PARTICLE ACCELERATORS↗