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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™

Ultra-high performance liquid chromatography–high-resolution mass spectrometry (UPHLC–HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000’s of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

environmental monitoring↗
DOE OSTI2021

An Integrated View of Hydraulic Induced Fracture Geometry in Hydraulic Fracture Test Site 2

Until relatively recently, most of our understanding about the geometry of hydraulically induced fractures in unconventional reservoirs has been inferred by patterns observed via microseismic monitoring. Important information, such as fracture-azimuth, fracture-length, height-growth and other geometric data has been determined from microseismic “event clouds”, which in turn has been used to interpret a stimulated reservoir volume (SRV). It is important to note that relatively limited microseismic results have been acquired in conjunction with other diagnostics and validated in a consistent manner. The Hydraulic Fracture Test Site 2 (HFTS2) in the Permian Delaware Basin provides a unique opportunity to compare the frac geometry interpretations derived from multiple frac diagnostic tools and at multiple scales. The integrated view that emerges from all the frac diagnostic tools is that in HFTS2 the geometries of hydraulically induced fractures are not random and with a highly complex branching architecture. In fact, the fracs are mostly vertical, parallel plana domains, and both vertically and horizontally asymmetrical. The hydraulically induced fractures have a very consistent azimuth with a strike that matches SHmax. At the distance corresponding to the spacing between nearby wells in HFTS2, ~ 660 ft, the pattern of strain interceptions observed from Low Frequency Distributed Acoustic Sensing (LF-DAS) shows far-field dimensions that are consistent with the stage length dimensions at the stimulated well. In general, larger stages with higher number of clusters create larger number of interceptions and wider stimulated intervals than that of shorter stages with the same cluster to cluster spacing and fewer clusters. The hydraulically induced fractures also show a clear tendency to preferentially grow upwards as shown by the microseismic data, by the relative lower number of LF-DAS interceptions in nearby deeper wells and finally by the observed LF-DAS strain patterns in the vertical observation well. In the near field, the geometries of the fracture domains are also consistent. As shown by the distributed strain measurements obtained during production via the monitoring of Rayleigh Frequency Shift (RFS), each cluster has a separate, non-overlapping, frac-zone-domain that are generally centered around the locations of each perforation cluster. Overall, the hydraulically induced fractures in HFTS2 are interpreted as occurring in swarms associated with individual clusters, each with its own unique geometry but with similar predictable geometric and propagation tendencies. The comprehensive frac diagnostic program in HFTS2, the well-pad layout that permits the investigation of the Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S018R003/2477511/urtec-2021-5396-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5396 2 impact that nearby producing wells has on frac geometry, together with an innovative Design of Experiment (DofE) that includes sequential-fracing, consistent cluster-to-cluster distances and few single-perforation cluster stages, has allowed us to gain a unique perspective about the geometry of the induced hydraulic fractures on this pad. Finally, the HFTS2 dataset has also provided us with a better understanding of the applicability and limitations of the different diagnostic tools.

58 GEOSCIENCES↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Structural investigation of the temperature-stable relaxor dielectric Ba 0.8 Ca 0.2 TiO 3 -Bi(Mg 0.5 Ti 0.5 )O 3

Aberration corrected scanning transmission electron microscopy (STEM) and electron diffraction have been used to disclose local structure and nano-chemistry in a Ca modified BaTiO 3 -Bi(Mg 0.5 Ti 0.5 )O 3 relax or dielectric ceramic which exhibits high and near-invariant relative permittivity over a wide temperature range. High resolution, synchrotron X-ray diffraction indicated a globally cubic structure ($Pm$$\overline{3}$$m$), but direct atomic-scale imaging by STEM revealed local tetragonal distortions. Nanopolar clusters were identified from B-site atomic displacement vectors measured relative to oxygen ion positions along < 100 > and < 110 > zone axes of integrated differential phase contrast (iDPC) STEM images, highlighting cluster sizes of 2–5 nm. Chemical analysis by STEM-energy dispersive X-ray spectroscopy and full pattern refinements of X-ray powder diffraction data each implied high levels of Bi vacancies within the matrix. The possibility that these A-site vacancies modulate the nanopolar structure and promote flattening of the permittivity-temperature response in this class of dielectric is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analysis of Superconducting Magnet Quench Antenna Data

Quenching poses a serious problem for superconducting magnets operating at high currents. It occurs when the material transitions from the superconducting to the normal state, which leads to heating and potential damage to the magnet. To understand and mitigate quenching, the Magnet Department at Fermilab is developing and testing superconducting magnet quench antenna arrays. This study delves into the anomalous events preceding the quench during magnet training by analyzing the collected data. With the moving average and Fast Fourier Transform techniques, we investigate the trends and frequency patterns of the data. Moreover, we introduce an unsupervised anomaly detection algorithm based on Principal Component Analysis and DBSCAN clustering. It can autonomously identify events within background noise, without relying on any predefined event features. Our analysis reveals that the spatio-temporal distribution of these anomalous events has little connection to the quench location, indicating that a majority of them bear no relation to the quenching process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Jet fragmentation function and groomed substructure of bottom quark jets in proton-proton collisions at 5.02 TeV

A measurement of the substructure of bottom quark jets (b jets) in proton-proton (pp) collisions is presented. The measurement uses data collected in pp collisions at $\sqrt{s}=5.02$ TeV, with a low number of simultaneous interactions per bunch crossing, recorded by the CMS experiment in 2017, corresponding to an integrated luminosity of 301 pb −1 . An algorithm to identify and cluster the charged decay daughters of b hadrons is developed for this analysis, which facilitates the exposure of the gluon radiation pattern of b jets using iterative Cambridge-Aachen declustering. The soft-drop-groomed jet radius, R g , and momentum balance, z g , of b quark jets are presented. These observables can be used to test perturbative quantum chromodynamics predictions that account for mass effects. Because the b hadron is partially reconstructed from its charged decay daughters, only charged particles are used for the jet substructure studies. In addition, a jet fragmentation function, z b,ch , is measured, which is defined as the distribution of the ratio of the transverse momentum (p T ) of the partially reconstructed b hadron with respect to the charged-particle component of the jet p T . The substructure variable distributions are unfolded to the charged-particle level. The b jet substructure is compared to the substructure of jets in an inclusive jet sample that is dominated by light-quark and gluon jets in order to assess the role of the b quark mass. A strong suppression of emissions at small R g values is observed for b jets when compared to inclusive jets, consistent with the dead-cone effect. The measurement is also compared with theoretical predictions from Monte Carlo event generators. This is the first substructure measurement of b jets that clusters together the b hadron decay daughters independent of the b hadron species and decay channel.

boosted jets↗
DOE OSTI2023

Light-activated microtubule-based two-dimensional active nematic

We assess the ability of two light responsive kinesin motor clusters to drive dynamics of microtubule-based active nematics: opto-K401, a processive motor, and opto-K365, a non-processive motor. Measurements reveal an order of magnitude improvement in the contrast of nematic flow speeds between maximally- and minimally-illuminated states for opto-K365 motors when compared to opto-K401 construct. For opto-K365 nematics, we characterize both the steady-state flow and defect density as a function of applied light. We also examine the transient behavior as the system switches between steady-states upon changes in light intensities. Although nematic flows reach a steady state within tens of seconds, the defect density exhibits transient behavior for up to 10 minutes, showing a separation between small-scale active flows and system-scale structural states. Furthermore, our work establishes an experimental platform that can exploit spatiotemporally-heterogeneous patterns of activity to generate targeted dynamical states.

42 ENGINEERING↗
DOE OSTI2025

Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data

In this paper, we propose a personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce an order orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm guaranteed to converge to a stationary point. The unique and common representations learned by perTucker reveal intrinsic statistical patterns in data and provide valuable information for a wide range of downstream analytics, including anomaly detection, source classification, and clustering. We demonstrate perTucker’s effectiveness through a simulation study and two case studies on solar flare detection and tonnage signal classification.

14 SOLAR ENERGY↗
DOE OSTI2025

Fast calculation of diffraction patterns from an ensemble of aligned molecules

We report an algorithm to calculate electron diffraction patterns for molecules with anisotropic angular distribution, which is significantly faster than existing methods. The algorithm uses a transform to convert the molecular orientation distribution, which is a function of three Euler angles, to the atom-pair distribution functions which depend on the polar and azimuthal angles. The diffraction signal can then be calculated from the atom-pair distributions. We demonstrate the computation method numerically by calculating electron diffraction patterns for a symmetric top molecule (trifluoroiodomethane) and an asymmetric top molecule (formaldehyde) and show that it reduces the calculation time by approximately two orders of magnitude compared to the standard brute-force method. Here, the method can also be applied to the calculation of x-ray diffraction patterns.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2026

High-speed particle analysis using forward and backward two-dimensional angular optical scattering

Measurement of two-dimensional angle-resolved optical scattering (TAOS) patterns is an attractive technique for detecting and characterizing micron-sized high-speed flying particles. The elastic-scattering intensity pattern from a single particle as a function of spherical coordinate angles θ and ϕ provides detailed information on the particle’s morphology. By use of an ellipsoidal reflector and a high-speed CCD camera, scattering from a CW-laser was detected and resolved over a large angular range (θ from 12° to 168° and ϕ from 0° to 360°) from spherically shaped particles (e.g., amorphous silica and borosilicate glass) flowing through the reflector’s focal plane at speeds of 20 to 30 m/s. The scattering pattern from individual particles flying through the focal plane of the ellipsoid mirror provides insights about a particle’s size, clustering, roughness, and velocity.

Optics and optical instruments↗
DOE OSTI2023

Data-Driven Whole-Genome Clustering to Detect Geospatial, Temporal, and Functional Trends in SARS-CoV-2 Evolution

Current methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison method that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. We show that during the height of the original Omicron surge, countries across Europe, Asia, and the Americas had a spatially asynchronous distribution of Omicron sub-strains. Moreover, neighboring countries were often dominated by either different clusters of the same variant or different variants altogether throughout the pandemic. Analyses of this kind may suggest a different pattern of epidemiological risk than was understood from conventional data, as well as produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.

Jacobson, Daniel↗
DOE OSTI2020

Characterizing crystalline defects in single nanoparticles from angular correlations of single-shot diffracted X-rays

Characterizing and controlling the uniformity of nanoparticles is crucial for their application in science and technology because crystalline defects in the nanoparticles strongly affect their unique properties. Recently, ultra-short and ultra-bright X-ray pulses provided by X-ray free-electron lasers (XFELs) opened up the possibility of structure determination of nanometre-scale matter with Å spatial resolution. However, it is often difficult to reconstruct the 3D structural information from single-shot X-ray diffraction patterns owing to the random orientation of the particles. This report proposes an analysis approach for characterizing defects in nanoparticles using wide-angle X-ray scattering (WAXS) data from free-flying single nanoparticles. The analysis method is based on the concept of correlated X-ray scattering, in which correlations of scattered X-ray are used to recover detailed structural information. WAXS experiments of xenon nanoparticles, or clusters, were conducted at an XFEL facility in Japan by using the SPring-8 Ångstrom compact free-electron laser (SACLA). Bragg spots in the recorded single-shot X-ray diffraction patterns showed clear angular correlations, which offered significant structural information on the nanoparticles. The experimental angular correlations were reproduced by numerical simulation in which kinematical theory of diffraction was combined with geometric calculations. We also explain the diffuse scattering intensity as being due to the stacking faults in the xenon clusters.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Development of a three-dimensional APOLLO3 neutrons deterministic scheme for the CABRI reactor

CABRI is an experimental reactor to study the fuel behavior during reactivity injection transients. These transients being highly multiphysics, the development of suitable modeling and simulation tools to simulate them is important for the optimization of the tests and the control of the experimental conditions. This paper focuses on the development of an APOLLO3 deterministic core calculation tool dedicated to the CABRI transient analysis. It represents the first stage of the incremental process for the implementation of a multiphysics time-dependent modeling of the CABRI transient. The neutron calculation scheme is based on a classical two-step approach. The first step consists of a 281-energy group calculation flux with the TDT-MOC (Method Of Characteristics) solver for cross-section space and energy (23 groups) collapsing for the CABRI different assembly clusters. The bias on a 2D core neutron calculation due to the self-shielding calculation and collapsing on a restricted pattern are investigated thanks to a comparison with a direct full 2D calculation on a quarter of core. The second step relies on a pin-resolved transport 3D transport core calculation with the SN solver MINARET. A progressive numerical validation process is followed to quantify the calculation biases on reactivity and reaction rates at each step using reference calculations with the stochastic code TRIPOLI4. The next development stage toward a multiphysics scheme will be the implementation of the 3D-kinetics equation resolution and the coupling with a core thermal-hydraulics model. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

The Near-Gapless Penicillium fuscoglaucum Genome Enables the Discovery of Lifestyle Features as an Emerging Post-Harvest Phytopathogen

Penicillium spp. occupy many diverse biological niches that include plant pathogens, opportunistic human pathogens, saprophytes, indoor air contaminants, and those selected specifically for industrial applications to produce secondary metabolites and lifesaving antibiotics. Recent phylogenetic studies have established Penicillium fuscoglaucum as a synonym for Penicillium commune, which is an indoor air contaminant and toxin producer and can infect apple fruit during storage. During routine culturing on selective media in the lab, we obtained an isolate of P. fuscoglaucum Pf_T2 and sequenced its genome. The Pf_T2 genome is far superior to available genomic resources for the species. Our assembly exhibits a length of 35.1 Mb, a BUSCO score of 97.9% complete, and consists of five scaffolds/contigs representing the four expected chromosomes. It was determined that the Pf_T2 genome was colinear with a type specimen P. fuscoglaucum and contained a lineage-specific, intact cyclopiazonic acid (CPA) gene cluster. For comparison, a highly virulent postharvest apple pathogen, P. expansum strain TDL 12.1, was included and showed a similar growth pattern in culture to our Pf_T2 isolate but was far more aggressive in apple fruit than P. fuscoglaucum. The genome of Pf_T2 serves as a major improvement over existing resources, has superior annotation, and can inform forthcoming omics-based work and functional genetic studies to probe secondary metabolite production and disparities in aggressiveness during apple fruit decay.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Molecular orbital symmetry-driven trimer formation in Kagome correlated electron materials

Correlated electron materials with molecular orbital states extending over transition metal clusters can host multiferroicity, spin frustration, and unconventional insulating phases. However, the fundamental criteria that govern cluster formation and stability remain unclear. Here, we identify a symmetry, correlation, and electron-filling-driven criteria that stabilize triangular metal trimers in materials displaying transition metal Kagome patterns. Using density functional theory and chemical bonding analysis, we show that trimer formation emerges when 6–8 electrons occupy molecular orbitals derived from transition metal d-states, achieving near-complete filling of bonding states while avoiding antibonding occupation, and correlations are of intermediate strength. This principle explains the stability of Nb 3 X 8 (X = Cl, Br, I), and more broadly, our findings offer a general design rule to obtain quantum materials with quantum states extended across transition metal clusters.

Kumari, Varsha [Indiana Univ., Bloomington, IN (Un↗