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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The Origin of Power-law Spectra in Relativistic Magnetic Reconnection

Abstract Magnetic reconnection is often invoked as a source of high-energy particles, and in relativistic astrophysical systems it is regarded as a prime candidate for powering fast and bright flares. We present a novel analytical model—supported and benchmarked with large-scale three-dimensional kinetic particle-in-cell simulations in electron–positron plasmas—that elucidates the physics governing the generation of power-law energy spectra in relativistic reconnection. Particles with Lorentz factorγ≳ 3σ(here,σis the magnetization) gain most of their energy in the inflow region, while meandering between the two sides of the reconnection layer. Their acceleration time is t acc ∼ γ η rec − 1 ω c − 1 ≃ 20 γ ω c − 1 , whereη rec ≃ 0.06 is the inflow speed in units of the speed of light andω c =eB 0 /mcis the gyrofrequency in the upstream magnetic field. They leave the region of active energization aftert esc , when they get captured by one of the outflowing flux ropes of reconnected plasma. We directly measuret esc in our simulations and find thatt esc ∼t acc forσ≳ few. This leads to a universal (i.e.,σ-independent) power-law spectrum dN free / d γ ∝ γ − 1 for the particles undergoing active acceleration, and dN / d γ ∝ γ − 2 for the overall particle population. Our results help to shed light on the ubiquitous presence of power-law particle and photon spectra in astrophysical nonthermal sources.

Astronomy & Astrophysics↗
DOE OSTI2022

Long- and short-term temporal variability in cloud condensation nuclei spectra over a wide supersaturation range in the Southern Great Plains site

Abstract. When aerosol particles seed the formation of liquid water droplets in the atmosphere, they are called cloud condensation nuclei (CCN). Different aerosols will act as CCN under different degrees of water supersaturation (relative humidity above 100 %), depending on their size and composition. In this work, we build and analyze a best-estimate CCN spectrum product, tabulated at ∼ 45 min resolution, generated using high quality data from seven independent instruments at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site. The data product spans a large supersaturation range, from 0.0001 % to ∼ 30 %, and time period of 5 years, from 2009–2013, and is available on the ARM data archive. We leverage this added statistical power to examine relationships that are unclear in smaller datasets. Our analysis is performed in three main areas. First, probability distributions of many aerosol and CCN metrics are found to exhibit skewed log-normal distribution shapes. Second, clustering analyses of CCN spectra reveal that the primary drivers of CCN differences are aerosol number size distributions, rather than hygroscopicity or composition, especially at supersaturations above 0.2 %, while also allowing for a simplified understanding of seasonal and diurnal variations in CCN behavior. The predictive ability of using limited hygroscopicity data with accurate number size distributions to estimate CCN spectra is investigated, and the uncertainties of this approach are estimated. Third, the dynamics of CCN spectral clusters and concentrations are examined with cross-correlation and autocorrelation analyses. We find that CCN concentrations change rapidly on the timescale of 1–3 h, with some conservation beyond that which is greatest for the lower supersaturation region of the spectrum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Doppler spectra from ARM Doppler Lidar during ARMing the Edge

There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. In this dataset, we provide the Doppler spectra from the ARM Doppler lidar at C1 recorded in April 2022 during ARMing the Edge.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1965

The distinction of rock types on the basis of their mass spectra, with special reference to lunar-surface applications

It is shown that the rock types most commonly expected to be encountered on the lunar surface can for the most part be readily distinguished, chemically, by plotting their relative concentrations of Fe, Mg, and Al on a ternary variation diagram. The necessary data for characterizing as unknown as to rock type can be quite easily extracted from complete or partial mass spectra such as may be obtained by means of a robot mass spectrometer on the lunar surface. For most compositions, determination of only two nuclide or element rations will characterize the sample. Fo others, the determination of one additional ratio or comparison with a few standard spectra previously obtained in the laboratory may be necessary to clarify the unknown in terms of the chemistry of the terrestrial or meteoritic equivalents. No quantitative assay of element concentrations is necessary for such a first classification.

LUNAR SURFACE↗
NASA NTRS1968

Interferometric spectra of planets.

Planetary spectra using Fourier transform spectroscopy, determining solar spectrum, Venus atmosphere carbon dioxide band and spectra of Mars and Jupiter

Connes, J.↗
NASA NTRS1969

Detection of molecular infrared spectra

Total energy distribution measurements of field emitted electrons detect molecular infrared spectra of adsorbed molecules. Tunneling electron gives up energy to excite various modes of adsorbed molecule. These electrons, when energy-analyzed, show up on the collector and exhibit the spectra of various modes excited by tunneling electrons.

Swanson, L. W.↗
NASA NTRS1969

Exciton spectra of CaO and MgO.

CaO and MgO reflectance spectra measurements at high resolution and at low temperature including exciton spectra at 25 K, obtaining fine structure

Walker, W. C.↗