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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High-pressure structural systematics of dysprosium metal compressed in a neon pressure medium to 182 GPa

Here we present an experimental and theoretical study of dysprosium metal compressed in the soft pressure transmitting medium Ne up to 182 and 300 GPa, respectively. Angle-dispersive x-ray powder diffraction data from each of the high-pressure polymorphs shows anisotropic compression behavior indicating changes to the electron density distribution throughout its polymorphic landscape. We compare the monoclinic (mC4) and orthorhombic (oF16) structures for the collapsed structure for Dy above 82 GPa and verify that the oF16 structure offers a better fit to our data than the previously reported mC4 structure. Further, we have found that the oF16 structure undergoes similar anisotropic compression of its lattice parameters, with a turning point above 160 GPa; suggesting a potential phase transition at pressures much higher than achieved in this study. Density functional theory calculations show the likely candidate for this new high-pressure phase is the isosymmetric oF8 structure, which is predicted to be lower in energy than the oF16 structure above 275 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

NeoN: Neuromorphic Control for Autonomous Robotic Navigation

In this paper we describe the use of a new neuromorphic computing framework to implement the navigation system for a roaming, obstacle avoidance robot. Using a Dynamic Adaptive Neural Network Array (DANNA) structure, our TENNLab (Laboratory of Tennesseans Exploring Neural Networks) hardware/software co-design framework and evolutionary optimization (EO) as the training algorithm, we create, train, implement, and test a spiking neural network autonomous robot control system using an array of neuromorphic computing elements built on an FPGA. The simplicity and flexibility of the DANNA neuromorphic computing elements allow for sufficient scale and connectivity on a Xilinx Kintex-7 FPGA to support sensory input and motor control for a mobile robot to navigate a dynamically changing environment. We further describe how more complex capabilities can be added using the same platform, e.g. object identification and tracking.

Mitchell, J. Parker↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Type Ia Supernova Explosions From Hybrid Carbon–Oxygen–Neon White Dwarf Progenitors

Motivated by recent results in stellar evolution that predict the existence of hybrid white dwarf (WD) stars with a C–O core inside an O–Ne shell, we simulate thermonuclear (Type Ia) supernovae from these hybrid progenitors. We use the FLASH code to perform multidimensional simulations in the deflagration-to-detonation transition (DDT) explosion paradigm. Our hybrid progenitor models were produced with the MESA stellar evolution code and include the effects of the Urca process, and we map the progenitor model to the FLASH grid. We performed a suite of DDT simulations over a range of ignition conditions consistent with the progenitor's thermal and convective structure assuming multiple ignition points. To compare the results from these hybrid WD stars to previous results from C–O WDs, we construct a set of C–O WD models with similar properties and similarly simulate a suite of explosions. We find that despite significant variability within each suite, trends distinguishing the explosions are apparent in their ${}^{56}\mathrm{Ni}$ yields and the kinetic properties of the ejecta. We compare our results with other recent work that studies explosions from these hybrid progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Neon Cluster Formation and Phase Separation during White Dwarf Cooling

Recent observations of Galactic white dwarfs (WDs) with Gaia suggest there is a population of massive crystallizing WDs exhibiting anomalous cooling—the Q branch. While single-particle 22 Ne sedimentation has long been considered a possible heat source, recent work suggests that 22 Ne must separate into clusters, enhancing diffusion, in order for sedimentation to provide heating on the observed timescale. We show definitively that 22 Ne cannot separate to form clusters in C/O WDs using molecular dynamics simulations, and we further present a general C/O/Ne phase diagram showing that strong 22Ne enrichment is not achievable for 22 Ne abundance ≲30%. Here, we conclude that the anomalous heating cannot be due to 22Ne cluster sedimentation and that Q branch WDs may have an unusual composition, possibly rich with heavier elements.

79 ASTRONOMY AND ASTROPHYSICS↗