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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Disordering, clustering, and laning transitions in particle systems with dispersion in the Magnus term

We numerically examine a two-dimensional system of repulsively interacting particles with dynamics that are governed by both a damping term and a Magnus term. The magnitude of the Magnus term has one value for half of the particles and a different value for the other half of the particles. In the absence of a driving force, the particles form a triangular lattice, while when a driving force is applied, we find that there is a critical drive above which a Magnus-induced disordering transition can occur even if the difference in the Magnus term between the two particle species is as small as one percent. The transition arises due to the different Hall angles of the two species, which causes their motion to decouple at the critical drive. At higher drives, the disordered state can undergo both species and density phase separation into a density-modulated stripe that is oriented perpendicular to the driving direction. We observe several additional phases that occur as a function of drive and Magnus force disparity, including a variety of density-modulated diagonal-laned phases. In general, we find a much richer variety of states compared to systems of oppositely driven overdamped Yukawa particles. Here, we discuss the implications of our work for skyrmion systems, where we predict that even for small skyrmion dispersities, a drive-induced disordering transition can occur along with clustering phases and pattern-forming states.

36 MATERIALS SCIENCE↗
DOE OSTI2019

De novo Characterization of the Platycladus orientalis Transcriptome and Analysis of Photosynthesis-Related Genes during Aging

In China, Platycladus orientalis has a lifespan of thousands of years. The long lifespan of these trees may be relevant for the characterization of plant aging at the molecular level. However, the molecular mechanism of the aging process of P. orientalis is still unknown. To explore the relationship between age and growth of P. orientalis , we analyzed physiological changes during P. orientalis senescence. The malondialdehyde content was greater in 200-, 700-, and 1100-year-old ancient trees than in 20-year-old trees, whereas the peroxidase and superoxide dismutase activities, as well as the soluble protein content, exhibited the opposite trend. Furthermore, we performed a de novo transcriptome assembly using RNA-Seq and obtained 48,044 unigenes with an average length of 896 bp. A total of 418 differentially expressed genes were identified in different stages of aging of P. orientalis . Clustering analysis revealed distinct timepoints at which the oxidation–reduction and photosynthesis pathways changed. Eight clusters with distinct expression patterns were identified. The expression levels of photosynthesis-, oxidation–reduction-, and transporter-related genes were down-regulated, whereas those of transcription-, signaling-, and senescence-related genes were up-regulated during aging. In addition, consistent with the most obviously down-regulated genes of photosynthesis-related genes, the photosynthetic indexes including chlorophyll a and b levels decreased steadily during P. orientalis aging. This study combined transcriptome with physiological and biochemical data, revealing potential candidate genes influencing senescence during P. orientalis aging.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

On the Hunt for the Origins of the Orphan–Chenab Stream: Detailed Element Abundances with APOGEE and Gaia

Stellar streams in the Galactic halo are useful probes of the assembly of galaxies like the Milky Way. Many tidal stellar streams that have been found in recent years are accompanied by a known progenitor globular cluster or dwarf galaxy. However, the Orphan–Chenab (OC) stream is one case where a relatively narrow stream of stars has been found without a known progenitor. In an effort to find the parent of the OC stream, we use astrometry from the early third data release of ESA's Gaia mission (Gaia EDR3) and radial velocity information from the Sloan Digital Sky Survey (SDSS)-IV Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey to find up to 13 stars that are likely members of the OC stream. We use the APOGEE survey to study the chemical nature (for up to 10 stars) of the OC stream in the α (O, Mg, Ca, Si, Ti, and S), odd-Z (Al, K, and V), Fe-peak (Fe, Ni, Mn, Co, and Cr), and neutron-capture (Ce) elemental groups. We find that the stars that make up the OC stream are not consistent with a monometallic population and have a median metallicity of -1.92 dex with a dispersion of 0.28 dex. Our results also indicate that the α elements are depleted compared to the known Milky Way populations and that its [Mg/Al] abundance ratio is not consistent with second-generation stars from globular clusters. The detailed chemical pattern of these stars, namely the [α/Fe]–[Fe/H] plane and the metallicity distribution, indicates that the OC stream progenitor is very likely to be a dwarf spheroidal galaxy with a mass of ~10 6 M ⊙ .

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Incipient fault detection study for advanced spacecraft systems

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

Milner, G. Martin↗
DOE OSTI2025

Clustering of DESI galaxies split by thermal Sunyaev-Zeldovich effect

The thermal Sunyaev-Zeldovich (tSZ) effect is associated with galaxy clusters - extremely large and dense structures tracing the dark matter with a higher bias than isolated galaxies. We propose to use the tSZ data to separate galaxies from redshift surveys into distinct subpopulations corresponding to different densities and biases independently of the redshift survey systematics. Leveraging the information from different environments, as in density-split and density-marked clustering, is known to tighten the constraints on cosmological parameters, like $\Omega_m$, $\sigma_8$ and neutrino mass. We use data from the Dark Energy Spectroscopic Instrument (DESI) and the Atacama Cosmology Telescope (ACT) in their region of overlap to demonstrate informative tSZ splitting of Luminous Red Galaxies (LRGs). We discover a significant increase in the large-scale clustering of DESI LRGs corresponding to detections starting from 1-2 sigma in the ACT DR6 + Planck tSZ Compton-$y$ map, below the cluster candidate threshold (4 sigma). We also find that such galaxies have higher line-of-sight coordinate (and velocity) dispersions and a higher number of close neighbors than both the full sample and near-zero tSZ regions. We produce simple simulations of tSZ maps that are intrinsically consistent with galaxy catalogs and do not include systematic effects, and find a similar pattern of large-scale clustering enhancement with tSZ effect significance. Moreover, we observe that this relative bias pattern remains largely unchanged with variations in the galaxy-halo connection model in our simulations. This is promising for future cosmological inference from tSZ-split clustering with semi-analytical models. Thus, we demonstrate that valuable cosmological information is present in the lower signal-to-noise regions of the thermal Sunyaev-Zeldovich map, extending far beyond the individual cluster candidates.

Astronomy data analysis↗
DOE OSTI2014

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models. The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRSDate not supplied

Investigation of the Impedance Characteristics of Perforate Sheet Hole Clustering Over an Array of Uniform Depth Chambers

A series of normal incidence impedance tube tests are conducted to characterize the facesheet impedance differences between uniformly distributed and nonuniformly tightly packed hole layout patterns, defined here as "hole clustering." These hole clustered samples contain the same number of holes as their uniformly distributed counterparts but target edge to edge distances between holes down to 0.01". Resonant frequency shifts between uniformly distributed and hole clustered samples are presented to demonstrate potential design benefits. Mass end correction differences between samples are also shown to better quantify differences in designs, including the effect of acoustic particle velocity. It is found that hole clustered facesheets over an array of uniform depth chambers can provide significant reactance increases at minimal expense to resistance, thereby lowering resonant frequencies. Additionally, moving these hole clustered layouts toward common chamber corners provides additional resonance shifts to lower frequencies.

acoustic liner impedance hole clustering perforate↗
DOE OSTI2018

Fluorescence Color by Data-Driven Design of Genomic Silver Clusters

DNA nucleobase sequence controls the size of DNA-stabilized silver clusters, leading to their well-known yet little understood sequence-tuned colors. The enormous space of possible DNA sequences for templating clusters has challenged the understanding of how sequence selects cluster properties and has limited the design of applications that employ these clusters. Here, we investigate the genomic role of DNA sequence for fluorescent silver clusters using a data-driven approach. Employing rapid parallel silver cluster synthesis and fluorimetry, we determine the fluorescence spectra of silver cluster products stabilized by 1432 distinct DNA oligomers. By applying pattern recognition algorithms to this large experimental data set, we discover certain DNA base patterns, or “motifs,” that correlate to silver clusters with similar fluorescence spectra. These motifs are employed in machine learning classifiers to predictively design DNA template sequences for specific fluorescence color bands. Our method improves selectivity of templates by 330% for silver clusters with peak emission wavelengths beyond 660 nm. The discovered base motifs also provide physical insights into how DNA sequence controls silver cluster size and color. Finally, this predictive design approach for color of DNA-stabilized silver clusters exhibits the potential of machine learning and data mining to increase the precision and efficiency of nanomaterials design, even for a soft-matter-inorganic hybrid system characterized by an extremely large parameter space.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Facile and dynamic cleavage of every iron–sulfide bond in cuboidal iron–sulfur clusters

Nature employs weak-field metalloclusters to support a wide range of biological processes. The most ubiquitous metalloclusters are the cuboidal Fe–S clusters, which are comprised of Fe sites with locally high-spin electronic configurations. Such configurations enhance rates of ligand exchange and imbue the clusters with a degree of structural plasticity that is increasingly thought to be functionally relevant. Here, we examine this phenomenon using isotope tracing experiments. Specifically, we demonstrate that synthetic [Fe 4 S 4 ] and [MoFe 3 S 4 ] clusters exchange their Fe atoms with Fe 2+ ions dissolved in solution, a process that involves the reversible cleavage and reformation of every Fe–S bond in the cluster core. This exchange is facile—in most cases occurring at room temperature on the timescale of minutes—and documented over a range of cluster core oxidation states and terminal ligation patterns. In addition to suggesting a highly dynamic picture of cluster structure, these results provide a method for isotopically labeling pre-formed clusters with spin-active nuclei, such as 57 Fe. Such a protocol is demonstrated for the radical S -adenosyl- l -methionine enzyme, RlmN.

Science & Technology - Other Topics↗
DOE OSTI2015

Metabolite fingerprinting of pennycress ( Thlaspi arvense L.) embryos to assess active pathways during oil synthesis

Pennycress ( Thlaspi arvense L.), a plant naturalized to North America, accumulates high levels of erucic acid in its seeds, which makes it a promising biodiesel and industrial crop. The main carbon sinks in pennycress embryos were found to be proteins, fatty acids, and cell wall, which respectively represented 38.5, 33.2, and 27.0% of the biomass at 21 days after pollination. Erucic acid reached a maximum of 36% of the total fatty acids. Together these results indicate that total oil and erucic acid contents could be increased to boost the economic competitiveness of this crop. Understanding the biochemical basis of oil synthesis in pennycress embryos is therefore timely and relevant to guide future breeding and/or metabolic engineering efforts. For this purpose, a combination of metabolomics approaches was conducted to assess the active biochemical pathways during oil synthesis. First, gas chromatography-mass spectrometry (GC-MS) profiling of intracellular metabolites highlighted three main families of compounds: organic acids, amino acids, and sugars/sugar alcohols. Secondly, these intermediates were quantified in developing pennycress embryos by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring mode. Finally, partitional clustering analysis grouped the intracellular metabolites that shared a similar pattern of accumulation over time into eight clusters. In conclusion, this study underlined that: (i) sucrose might be stored rather than cleaved into hexoses; (ii) glucose and glutamine would be the main sources of carbon and nitrogen, respectively; and (iii) glycolysis, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and the Calvin cycle were active in developing pennycress embryos.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

43 PARTICLE ACCELERATORS↗
NASA NTRS2000

Real Time Intelligent Target Detection and Analysis with Machine Vision

We present an algorithm for detecting a specified set of targets for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and nontarget objects in a scene by evaluating 40x40 image blocks belonging to an image. Each image block is first projected onto a set of templates specifically designed to separate images of targets embedded in a typical background scene from those background images without targets. These filters are found using directed principal component analysis which maximally separates the two groups. The projected images are then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. These cluster prototypes have previously been identified using a modified clustering algorithm based on prior sensed data. Each projected image pattern is then fed into the associated cluster's trained neural network for classification. A detailed description of our algorithm will be given in this paper. We outline our methodology for designing the templates, describe our modified clustering algorithm, and provide details on the neural network classifiers. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.

Howard, Ayanna↗
DOE OSTI2016

Data depth based clustering analysis

Here, this paper proposes a new algorithm for identifying patterns within data, based on data depth. Such a clustering analysis has an enormous potential to discover previously unknown insights from existing data sets. Many clustering algorithms already exist for this purpose. However, most algorithms are not affine invariant. Therefore, they must operate with different parameters after the data sets are rotated, scaled, or translated. Further, most clustering algorithms, based on Euclidean distance, can be sensitive to noises because they have no global perspective. Parameter selection also significantly affects the clustering results of each algorithm. Unlike many existing clustering algorithms, the proposed algorithm, called data depth based clustering analysis (DBCA), is able to detect coherent clusters after the data sets are affine transformed without changing a parameter. It is also robust to noises because using data depth can measure centrality and outlyingness of the underlying data. Further, it can generate relatively stable clusters by varying the parameter. The experimental comparison with the leading state-of-the-art alternatives demonstrates that the proposed algorithm outperforms DBSCAN and HDBSCAN in terms of affine invariance, and exceeds or matches the ro-bustness to noises of DBSCAN or HDBSCAN. The robust-ness to parameter selection is also demonstrated through the case study of clustering twitter data.

58 GEOSCIENCES↗
DOE OSTI2023

Analysis of Real-World Preignition Data Using Neural Networks

Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure. Further, this indicates that the alternating pattern of preignition cycles within clusters is influenced by the thermodynamic state as reflected in the pressure, but that the trigger for the initial preignition cycle is not thermodynamic in nature, but more likely tied to a critical threshold in the chemistry of the fuel/lubricant mixture in the upper crevice or other factors related to the presence of an ignition source.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

A tale of two anomalies: Depletion, dispersion, and the connection between the stellar lithium spread and inflated radii on the pre-main sequence

We investigate lithium depletion in standard stellar models (SSMs) and main sequence (MS) open clusters, and explore the origin of the Li dispersion in young, cool stars of equal mass, age, and composition. We first demonstrate that SSMs accurately predict the Li abundances of solar analogs at the zero-age main sequence (ZAMS) within theoretical uncertainties. We then measure the rate of MS Li depletion by removing the [Fe/H]-dependent ZAMS Li pattern from three well-studied clusters, and comparing the detrended data. MS depletion is found to be mass-dependent, in the sense of more depletion at low mass. A dispersion in Li abundance at fixed T{sub eff} is nearly universal, and sets in by ∼200 Myr. We discuss mass and age dispersion trends, and the pattern is mixed. We argue that metallicity impacts the ZAMS Li pattern, in agreement with theoretical expectations but contrary to the findings of some previous studies, and suggest Li as a test of cluster metallicity. Finally, we argue that a radius dispersion in stars of fixed mass and age, during the epoch of pre-MS Li destruction, is responsible for the spread in Li abundances and the correlation between rotation and Li in young cool stars, most well known in the Pleiades. We calculate stellar models, inflated to match observed radius anomalies in magnetically active systems, and the resulting range of Li abundances reproduces the observed patterns of young clusters. We discuss ramifications for pre-MS evolutionary tracks and age measurements of young clusters, and suggest an observational test.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Integrating Data From In Vitro New Approach Methodologies for Developmental Neurotoxicity

Abstract In vivo developmental neurotoxicity (DNT) testing is resource intensive and lacks information on cellular processes affected by chemicals. To address this, DNT new approach methodologies (NAMs) are being evaluated, including: the microelectrode array neuronal network formation assay; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work addresses 3 hypotheses: (1) a broad screening battery provides a sensitive marker of DNT bioactivity; (2) selective bioactivity (occurring at noncytotoxic concentrations) may indicate functional processes disrupted; and, (3) a subset of endpoints may optimally classify chemicals with in vivo evidence for DNT. The dataset was comprised of 92 chemicals screened in all 57 assay endpoints sourced from publicly available data, including a set of DNT NAM evaluation chemicals with putative positives (53) and negatives (13). The DNT NAM battery provides a sensitive marker of DNT bioactivity, particularly in cytotoxicity and network connectivity parameters. Hierarchical clustering suggested potency (including cytotoxicity) was important for classifying positive chemicals with high sensitivity (93%) but failed to distinguish patterns of disrupted functional processes. In contrast, clustering of selective values revealed informative patterns of differential activity but demonstrated lower sensitivity (74%). The false negatives were associated with several limitations, such as the maximal concentration tested or gaps in the biology captured by the current battery. This work demonstrates that this multi-dimensional assay suite provides a sensitive biomarker for DNT bioactivity, with selective activity providing possible insight into specific functional processes affected by chemical exposure and a basis for further research.

Carstens, Kelly E.↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗