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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Gravitational Lenses and the Structure and Evolution of Galaxies

The grant has supported the completion of 16 papers and 4 conference proceedings to date. During the first year of the project we completed five papers, each of which represents a new direction in the theory and interpretation of gravitational lenses. In the first paper, "The Importance of Einstein Rings", we developed the first theory for the formation and structure of the Einstein rings formed by lensing extended sources like the host galaxies of quasar and radio sources. We applied the theory to three lenses with lensed host galaxies. For the time delay lens PG 1115+080 we found that the structure of the Einstein ring ruled out models of the gravitational potential which permitted a large Hubble constant (70 km/s Mpc). In the second paper, :Cusped Mass Models Of Gravitational Lenses", we introduced a new class of lens models where the central density is characterized by a cusp ( rho proportional to tau(sup -gamma), 1 less than gamma less than 2) as in most modern models and theories of galaxies rather than a finite core radius. In the third paper, "Global Probes of the Impact of Baryons on Dark Matter Halos", we made the first globally consistent models for the separation distribution of gravitational lenses including both galaxy and cluster lenses. We show that the key physics for the origin of the sharp separation cutoff in the separation distribution near 3 arc sec is the effect of the cooling baryons in galaxies on the density structure of the system.

Kochanek, Christopher↗
NASA NTRSDate not supplied

Autonomous Nanosatellite Swarming (ANS) Using Radio Frequency and Optical Navigation

The Autonomous Nanosatellite Swarming (ANS) subsystem is a dynamics, guidance, navigation, and control software unit based on commercial-off-the-shelf avionics, which is distributed and deployed on a cluster of cooperative nanosatellites to enable future exploration missions to near-Earth objects (NEOs). ANS allows multiple nanosatellites to act together as a single large spacecraft through radio-frequency (RF) cross-links, optical navigation, and low-thrust control. In particular, ANS-equipped nanosatellites can utilize images of a NEO taken from multiple viewpoints to achieve stereo-vision capabilities and characterize the target body’s shape, gravity field, and dynamical properties. All this is enabled by the fusion of major advances to the state of the art in the astrodynamics of relative motion, RF and vision-based navigation, as well as low-thrust control.

Simone D'Amico↗
NASA NTRSDate not supplied

Genetic Determinants of Microbial Survival in Space

Space flight agencies envision a future for humankind beyond Earth, including missions back to the Moon and to Mars in the coming decades. Sending humans into space inevitably includes their microbiomes as well, leading to trillions of bacteria being shed in their living areas. These bacteria shape the lives of their hosts as well as their environment; thus, it is crucial to understand the adaptations of these microbial spacefarers in spaceflight conditions. We aimed to elucidate the genetic determinants of microbial survival in space using a pan-genome analysis of 12 genera cultured from the International Space Station (ISS) from 2017 to 2018. Analysis was performed on each of the genera individually with terrestrial analogs to identify the core and accessory genomes of the spaceflight and terrestrial strains. We then compared the flight and terrestrial core and accessory genomes for each genera using a Bray-Curtis index and visualized the resulting dissimilarity using an Non-Metric Dimensional Scaling plot. The core proteins available in only the spaceflight organisms were then manually characterized for function and genomic location. In every core genome comparison in each genus, there was significant dissimilarity in the core of the spaceflight organisms when compared to the terrestrial organisms. This trend was present in some of the accessory genomes, but was not ubiquitous. Functional analysis of the core content of the ISS genomes showed the majority of genes unique to the core were clustered by location. These gene clusters suggested a set of genetic determinants confer survival in spacecraft-built environments, notably through the uptake of extracellular DNA such as bacteriophage and plasmids. The clear difference between spaceflight and terrestrial microorganisms shows that spaceflight conditions are selective, which has long term implications for their human hosts and environments.

MoBE↗
DOE OSTI2021

Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

Residential electricity load profiles and their diversity have become increasingly important to realize the benefits of Smart or Transactive Energy Networks (TENs). An important element of TENs will be practical, accurate, and implementable residential load forecasting techniques. While there have been many approaches to short-term load forecasting, few have included forecasting for individual households, partly because the high volatility and idiosyncrasies present in individual household load data can pose significant challenges. In this study, we develop a Convolutional Long Short-Term Memory-based neural network with Selected Autoregressive Features (termed a CLSAF model) to improve short-term household electricity load forecasting accuracy by employing three strategies: autoregressive features selection, exogenous features selection, and a “default” state to avoid overfitting at times of high load volatility. We include aggregations of apartments to floor and building level, because utilities may favor transactive approaches that rely on aggregator models, e.g., a cluster of consumers as opposed to an individual. We demonstrate that the CLSAF model, by virtue of its enhanced feature representation and modest computational resources, can accomplish load forecasting in a multi-family residential building across three spatial granularities (individual apartment/household, floor, and building levels), with an accuracy improvement of up to 25% compared to a persistence model. We propose a data screening technique to characterize time-series electricity-load data. This technique is suitable for integration into a TEN ecosystem and allows one to estimate confidence levels of the load forecasts to optimize computational resources and the risks associated with uncertain forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Modulating CO 2 Electrocatalytic Conversion to the Organics Pathway by the Catalytic Site Dimension

Electrochemical reduction of carbon dioxide to organic chemicals provides a value-added route for mitigating greenhouse gas emissions. Here, we report a family of carbon-supported Sn electrocatalysts with the tin size varying from single atom, ultrasmall clusters to nanocrystallites. High single-product Faradaic efficiency (FE) and low onset potential of CO 2 conversion to acetate (FE = 90% @ –0.6 V), ethanol (FE = 92% @ –0.4 V), and formate (FE = 91% @ –0.6 V) were achieved over the catalysts of different active site dimensions. The CO 2 conversion mechanism behind these highly selective, size-modulated p-block element catalysts was elucidated by structural characterization and computational modeling, together with kinetic isotope effect investigation.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Molecular Models of Atomically Dispersed Uranium at MoS 2 Surfaces Reveal Cooperative Mechanism of Water Reduction

Single atoms of uranium supported on molybdenum sulfide surfaces (U@MoS 2 ) have been recently demonstrated to facilitate the hydrogen evolution reaction (HER) through electrocatalysis. Theoretical calculations have predicted uranium hydroxide moieties bound to edge-sulfur atoms of MoS 2 as a proposed transition state involved in the HER process. However, the isolation of relevant intermediates involved in this process remains a challenge, rendering mechanistic hypotheses unverified. The present work describes the isolation and characterization of a uranium-hydroxide intermediate on molybdenum sulfide surfaces using [(Cp* 3 Mo 3 S 4 )UCp*], a molecular model of a reduced uranium center supported at MoS 2 . Mechanistic investigations highlight the metalloligand cooperativity with uranium involved in the water activation pathway. The corresponding uranium-oxo analogue, [(Cp* 3 Mo 3 S 4 )Cp*U(═O)], was also accessed from the hydroxide cluster via hydrogen atom transfer and from [(Cp* 3 Mo 3 S 4 )UCp*] through an alternative direct oxygen atom transfer. These results provide an atomistic perspective on the reactivity of low-valent uranium at molybdenum sulfide surfaces toward water, modeling key intermediates associated with the HER of U@MoS 2 catalysts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Universality class for loopless invasion percolation models and a percolation avalanche burst model for hydraulic fracturing

Invasion percolation is a model that was originally proposed to describe growing networks of fractures. In this work, we describe a loopless algorithm on random lattices, coupled with an avalanche-based model for bursts. The model reproduces the characteristic b-value seismicity and spatial distribution of bursts consistent with earthquakes resulting from hydraulic fracturing (“fracking”). We test models for both site invasion percolation and bond invasion percolation. These have differences on the scale of site and bond lengths l. But since the networks are characterized by their large-scale behavior, l << L, we find small differences between scaling exponents. Though data may not differentiate between models, our results suggest that both models belong to different universality classes.

58 GEOSCIENCES↗
NASA NTRS1986

Temperature fluctuations and infrared emission from dust particles in a hot gas

In this paper, the effect of temperature fluctuations in very small dust particles caused by electronic collisions with an ambient hot gas, is calculated. The dust-temperature distribution differs strongly from that derived on the basis of equilibrium heating models, peaking at lower temperatures in the stochastically heated case. The resulting infrared spectrum is broadened at short wavelengths, giving much more emission than expected from equilibrium calculations, and at long wavelengths exhibits a lower color temperature. Dust-temperature fluctuations are thus expected to play an important role in determining the infrared spectrum of dusty, X-ray-emitting plasmas. A power-law distribution of grain radii extended to very small grain sizes will have a distinct infrared spectrum characterized by an infrared excess at the Wien side of the spectrum. This excess emission represents a high-temperature component in the spectrum, resulting from the stochastic heating of very small dust particles. Its magnitude depends on the grain-size distribution and the temperature and density of the ambient hot gas. This excess emission may be observed by IRAS in supernova remnants and rich clusters of galaxies.

Dwek, E.↗
NASA NTRS2003

The expression and function of the achaete-scute genes in Tribolium castaneum reveals conservation and variation in neural pattern formation and cell fate specification

The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ac/sc genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ac/sc genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we find that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Tribolium and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Tribolium proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-ase is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Tribolium ac/sc genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

Non-NASA Center↗
NASA NTRS2003

The Expression and Function of the Achaete-Scute Genes in Tribolium castaneum Reveals Conservation and Variation in Neural Pattern Formation and Cell Fate Specification

SUMMARY The study of achaete-scute (ac/sc) genes has recently become a paradigm to understand the evolution and development of the arthropod nervous system. We describe the identification and characterization of the ache genes in the coleopteran insect species Tribolium castaneum. We have identified two Tribolium ache genes - achaete-scute homolog (Tc-ASH) a proneural gene and asense (Tc-ase) a neural precursor gene that reside in a gene complex. Focusing on the embryonic central nervous system we fmd that Tc-ASH is expressed in all neural precursors and the proneural clusters from which they segregate. Through RNAi and misexpression studies we show that Tc-ASH is necessary for neural precursor formation in Triboliurn and sufficient for neural precursor formation in Drosophila. Comparison of the function of the Drosophila and Triboliurn proneural ac/sc genes suggests that in the Drosophila lineage these genes have maintained their ancestral function in neural precursor formation and have acquired a new role in the fate specification of individual neural precursors. Furthermore, we find that Tc-use is expressed in all neural precursors suggesting an important and conserved role for asense genes in insect nervous system development. Our analysis of the Triboliurn ache genes indicates significant plasticity in gene number, expression and function, and implicates these modifications in the evolution of arthropod neural development.

Wheeler, Scott R.↗
NASA NTRS2009

Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves

Bayesian networks (BNs) are used to represent and efficiently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagation in a clique tree compiled from a Bayesian network. There is a lack of understanding of how clique tree computation time, and BN computation time in more general, depends on variations in BN size and structure. On the one hand, complexity results tell us that many interesting BN queries are NP-hard or worse to answer, and it is not hard to find application BNs where the clique tree approach in practice cannot be used. On the other hand, it is well-known that tree-structured BNs can be used to answer probabilistic queries in polynomial time. In this article, we develop an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN's non-root nodes to the number of root nodes, or (ii) the expected number of moral edges in their moral graphs. Our approach is based on combining analytical and experimental results. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for each set. For the special case of bipartite BNs, we consequently have two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, we systematically increase the degree of the root nodes in bipartite Bayesian networks, and find that root clique growth is well-approximated by Gompertz growth curves. It is believed that this research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms, and presents an aid for analytical trade-off studies of clique tree clustering using growth curves.

Mengshoel, Ole Jakob↗
DOE OSTI2020

Mononuclear to Polynuclear U(IV) Structural Units: Effects of Reaction Conditions on U-Furoate Phase Formation

In this work, Uranium(IV) complexation by 2-furoic acid was examined to better understand the effects of ligand identity and reaction conditions on species formation and stability. Five compounds were isolated: [UCl 2 (2-FA) 2 (H 2 O) 2 ] n (1), [U 4 Cl 10 O 2 (THF) 6 (2-FA) 2 ]•2(THF) (2), [U 6 O 4 (OH) 4 (H 2 O) 3 (2-FA) 12 ]•7(THF)•(H 2 O) (3), [U 6 O 4 (OH) 4 (H 2 O) 2 (2-FA) 12 ]•8.76(H 2 O) (4), and [U 38 Cl 42 O 54 (OH) 2 (H 2 O) 20 ]•m(H 2 O)•n(THF) (5). The structures were determined by single crystal X-ray diffraction and further characterized by Raman, IR, and optical absorption spectroscopy. The thermal stability and magnetic behavior of the compounds were also examined. Variations in the synthetic conditions led to notable differences in the structural units observed in the solid state. At low H 2 O/THF ratios, a tetranuclear oxo bridged [U 4 O 2 ] core was isolated. Aging of this solution resulted in the formation a U 38 oxo cluster capped by chloro and water ligands. However, at increasing water concentrations only hexanuclear units were observed. In all cases, at temperatures of 100-120 °C, UO 2 nanoparticles formed.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2020

Jet energy drop

We study the jet energy drop, which is the relative difference between the groomed and ungroomed jet energy or transverse momentum. It is one of the fundamental quantities that characterizes the impact of grooming on jets produced in high energy collisions. We consider three different grooming algorithms i) soft drop, ii) iterated soft drop, and iii) trimming. We carry out the resummation of large logarithms of the jet energy drop, the jet radius as well as relevant grooming parameters at next-to-leading logarithmic (NLL') accuracy. In addition, we account for non-global and clustering logarithms, and determine the next-to-leading order corrections. For soft drop we perform a joint resummation of the jet energy drop and the groomed jet radius, which is necessary to achieve the correct all-order structure of the cross section, in particular for the Sudakov-safe case of soft drop with β = 0. We present numerical results for LHC energies and compare to Pythia simulations as well as CMS data. Our factorization framework predicts the onset of nonperturbative effects in the jet energy distribution, in line with what we find in Pythia. The jet energy drop observables stand out because they only probe soft radiation, making them ideal candidates for the tuning of parton shower Monte Carlo event generators and for probing medium effects in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. Here we introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Two-component dynamics in supercritical $\text {CO}_2$ from inelastic X-ray scattering

Supercritical fluids are characterized by unique thermodynamic properties. One of these properties is the existence of two-component dynamics that is associated with distinct low-frequency and high-frequency vibrational responses of the fluid. However, the origin of this behavior remains unknown. By combining inelastic X-ray scattering and molecular dynamics simulations, we show that this behavior can be connected to density heterogeneities arising from molecular clusters. Analyses of measurements and molecular trajectories suggest that the two-component dynamics emerges due to distinct momentum fluctuations of clustered and unbound molecules. This connection between clusters and two-component dynamics highlights the importance of molecular-structural heterogeneities in supercritical fluids, colloids, and condensed-matter systems.

biophysics↗
DOE OSTI2020

Two Ultra-faint Milky Way Stellar Systems Discovered in Early Data from the DECam Local Volume Exploration Survey

We report the discovery of two ultra-faint stellar systems found in early data from the DECam Local Volume Exploration survey (DELVE). The first system, Centaurus I (DELVE J1238-4054), is identified as a resolved overdensity of old and metal-poor stars with a heliocentric distance of ${\rm D}_{\odot} = 116.3_{-0.6}^{+0.6}$ kpc, a half-light radius of $r_h = 2.3_{-0.3}^{+0.4}$ arcmin, an age of $\tau > 12.85$ Gyr, a metallicity of $Z = 0.0002_{-0.0002}^{+0.0001}$, and an absolute magnitude of $M_V = -5.55_{-0.11}^{+0.11}$ mag. This characterization is consistent with the population of ultra-faint satellites, and confirmation of this system would make Centaurus I one of the brightest recently discovered ultra-faint dwarf galaxies. Centaurus I is detected in Gaia DR2 with a clear and distinct proper motion signal, confirming that it is a real association of stars distinct from the Milky Way foreground; this is further supported by the clustering of blue horizontal branch stars near the centroid of the system. The second system, DELVE 1 (DELVE J1630-0058), is identified as a resolved overdensity of stars with a heliocentric distance of ${\rm D}_{\odot} = 19.0_{-0.6}^{+0.5} kpc$, a half-light radius of $r_h = 0.97_{-0.17}^{+0.24}$ arcmin, an age of $\tau = 12.5_{-0.7}^{+1.0}$ Gyr, a metallicity of $Z = 0.0005_{-0.0001}^{+0.0002}$, and an absolute magnitude of $M_V = -0.2_{-0.6}^{+0.8}$ mag, consistent with the known population of faint halo star clusters. Here, given the low number of probable member stars at magnitudes accessible with Gaia DR2, a proper motion signal for DELVE 1 is only marginally detected. We compare the spatial position and proper motion of both Centaurus I and DELVE 1 with simulations of the accreted satellite population of the Large Magellanic Cloud (LMC) and find that neither is likely to be associated with the LMC.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Characterizing Drought Behavior in the Colorado River Basin Using Unsupervised Machine Learning

Drought is a pressing issue for the Colorado River Basin (CRB) due to the social and economic value of water resources in the region and the significant uncertainty of future drought under climate change. Here, we use climate simulations from various Earth System Models (ESMs) to force the Variable Infiltration Capacity hydrologic model and project multiple drought indicators for the sub-watersheds within the CRB. We apply an unsupervised machine learning (ML) based on Non-Negative Matrix Factorization using K-means clustering (NMFk) to synthesize the simulated historical, future, and change in drought indicators. The unsupervised ML approach can identify sub-watersheds where key changes to drought indicator behavior occur, including shifts in snowpack, snowmelt timing, precipitation, and evapotranspiration. While changes in future precipitation vary across ESMs, the results indicate that the Upper CRB will experience increasing evaporative demand and surface-water scarcity, with some locations experiencing a shift from a radiation-limited to a water-limited evaporation regime in the summer. Large shifts in peak runoff are observed in snowmelt-dominant sub-watersheds, with complete disappearance of the snowmelt signal for some sub-watersheds. The work demonstrates the utility of the NMFk algorithm to efficiently identify behavioral changes of drought indicators across space and time and to quickly analyze and interpret hydro climate model results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structural characterization of three noncanonical NTF2-like superfamily proteins: implications for polyketide biosynthesis

Proteins belonging to the NTF2-like superfamily are present in the biosynthetic pathways of numerous polyketide natural products, such as anthracyclins and benzoisochromanequinones. Some have been found to be bona fide polyketide cyclases, but many of them have roles that are currently unknown. Here, the X-ray crystal structures of three NTF2-like proteins of unknown function are reported: those of ActVI-ORFA from Streptomyces coelicolor A3(2) and its homologs Caci_6494, a protein from an uncharacterized biosynthetic cluster in Catenulispora acidiphila , and Aln2 from Streptomyces sp. CM020, a protein in the biosynthetic pathway of alnumycin. The presence of a solvent-accessible cavity and the conservation of the His/Asp dyad that is characteristic of many polyketide cyclases suggest a potential enzymatic role for these enzymes in polyketide biosynthesis.

Vuksanovic, Nemanja (ORCID:0000000328836312)↗