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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of thin-gap GEM- μ RWELL hybrid detectors

Micro Pattern Gaseous Detectors (MPGDs) are used for tracking in High Energy Physics and Nuclear Physics because of their large area, excellent spatial resolution capabilities and low cost. However, for high energy charged particles impacting at a large angle with respect to the axis perpendicular to detector plane, the spatial resolution degrades significantly because of the long trail of ionization charges produced in clusters all along the track in the drift region of the detector. The long ionization charge trail results in registering hits from large number of strips in the readout plane which makes it challenging to precisely reconstruct the particle’s position using simple center of gravity algorithm. As a result, the larger the drift gap, the more severe the deterioration of spatial resolution for inclined tracks. For the same reason, the position resolution is also severely degraded in a large magnetic field, where the Lorentz E x B effect causes the ionization charges to follow a curved and longer path in the detector gas volume. In this paper, we report on the development of thin-gap MPGDs as a way to maintain excellent spatial resolution capabilities of MPGD detectors over a wide angular range of incoming particles. In a thin-gap MPGD, the thickness of the gas volume in the drift region is reduced from typically 3 mm to 1 mm or less. We present preliminary test beam results demonstrating the improvement in spatial resolution from 400 m with a standard 3 mm gap μ RWELL prototype to ~140 μ m with a double amplification GEM- μ RWELL thin-gap hybrid detector. Here, we also discuss the impact of a thin-gap drift volume on other aspects of the performance of MPGD technologies such as the efficiency and detector stability.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2005

Use of multiresolution wavelet feature pyramids for automatic registration of multisensor imagery

The problem of image registration, or the alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast, and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times and that would provide subpixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the bandpass wavelets obtained from the steerable pyramid due to Simoncelli performs best in terms of accuracy and consistency, while the low-pass wavelets obtained from the same pyramid give the best results in terms of the radius of convergence. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

Evaluation Studies↗
NASA NTRS2011

Space Radiation Effects on Human Cells: Modeling DNA Breakage, DNA Damage Foci Distribution, Chromosomal Aberrations and Tissue Effects

Future long-tem space travel will face challenges from radiation concerns as the space environment poses health risk to humans in space from radiations with high biological efficiency and adverse post-flight long-term effects. Solar particles events may dramatically affect the crew performance, while Galactic Cosmic Rays will induce a chronic exposure to high-linear-energy-transfer (LET) particles. These types of radiation, not present on the ground level, can increase the probability of a fatal cancer later in astronaut life. No feasible shielding is possible from radiation in space, especially for the heavy ion component, as suggested solutions will require a dramatic increase in the mass of the mission. Our research group focuses on fundamental research and strategic analysis leading to better shielding design and to better understanding of the biological mechanisms of radiation damage. We present our recent effort to model DNA damage and tissue damage using computational models based on the physics of heavy ion radiation, DNA structure and DNA damage and repair in human cells. Our particular area of expertise include the clustered DNA damage from high-LET radiation, the visualization of DSBs (DNA double strand breaks) via DNA damage foci, image analysis and the statistics of the foci for different experimental situations, chromosomal aberration formation through DSB misrepair, the kinetics of DSB repair leading to a model-derived spectrum of chromosomal aberrations, and, finally, the simulation of human tissue and the pattern of apoptotic cell damage. This compendium of theoretical and experimental data sheds light on the complex nature of radiation interacting with human DNA, cells and tissues, which can lead to mutagenesis and carcinogenesis later in human life after the space mission.

Ponomarev, A. L.↗
DOE OSTI2020

Clustering-Based Predictive Analytics to Improve Scientific Data Discovery

Given the sheer volume of scientific data archived within the data-intensive projects at the US Department of Energy's Oak Ridge National Laboratory, finding precisely what data we are looking for may not be a trivial task; conversely, we may also miss a more prominent data product. To address such issues, we propose improving the data discovery system and using data analytics methods to comprehend what specific users might be interested in based on their physiological state, search patterns, and past data usage history. This work's primary goal is to prune the complexity, increase the visibility of popular data products, and direct users toward the data that best meet their needs. The proposed algorithm constructs a user profile based on the user's explicit or implicit interactions with the system, such as items they are currently looking at on-site and the key metadata mappings related to the data set. The pattern is then used to build a training data set, which will help find relevant data to recommend to the user.

Devarakonda, Ranjeet↗
DOE OSTI2013

Ruprecht 106: The first single population globular cluster?

All old Galactic globular clusters (GCs) studied in detail to date host at least two generations of stars, where the second is formed from gas polluted by processed material produced by massive stars of the first. This process can happen if the initial mass of the cluster exceeds a threshold above which ejecta are retained and a second generation is formed. A determination of this mass threshold is mandatory in order to understand how GCs form. We analyzed nine red giant branch stars belonging to the cluster Ruprecht 106. Targets were observed with the UVES@VLT2 spectrograph. Spectra cover a wide range and allowed us to measure abundances for light (O, Na, Mg, Al), α (Si, Ca, Ti), iron-peak (Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn), and neutron-capture (Y, Zr, Ba, La, Ce, Pr, Nd, Sm, Eu, Dy, Pb) elements. Based on these abundances, we show that Ruprecht 106 is the first convincing example of a single-population GC (i.e., a true simple stellar population), although the sample is relatively small. This result is supported also by an independent photometric test and by the horizontal branch morphology and the dynamical state. It is old (∼12 Gyr) and, at odds with other GCs, has no α-enhancement. The material it formed from was contaminated by both s- and r-process elements. The abundance pattern points toward an extragalactic origin. Its present-day mass (M = 10{sup 4.83} M {sub ☉}) can be assumed as a strong lower limit for the initial mass threshold below which no second generation is formed. Clearly, its initial mass must have been significantly greater, but we have no current constraints on the amount of mass loss during its evolution.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Directivity of planar array feeds for satellite reflector applications

Array-fed reflector antennas are used extensively in today's contour and multiple beam satellite antennas. To determine the directivity of these antennas theoretically, the total radiated power of the array feed must be accurately computed. In this paper, a closed-form expression for the radiated power is obtained for arrays with the well-known (cos theta q-type element patterns. The formulation is general, and takes into account polarization, nonsymmetric E- and H-plane element patterns, nonuniform element spacings and arbitrary complex excitation coefficients. Selective numerical data are presented to demonstrate the usefulness of this result. Comparisons are made with the available results obtained using direct numerical integration techniques, and with other available data based on less general formulations. Excellent agreement is observed for all cases. In particular, data are presented on the directivity of seven-element cluster feeds used in multiple beam designs, and an array feed for producing a contour beam covering the United States Eastern Time Zone (ETZ).

Rahmat-Samii, Y.↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2019

Deciphering the Stress State of Seismogenic Faults in Oklahoma and Southern Kansas Based on an Improved Stress Map

Fault location and geometry are prime considerations in the reactivation of preexisting faults. Here, we combine relocated earthquake catalogs and focal mechanisms to delineate seismogenic faults in Oklahoma and southern Kansas and analyze their stress state. Initially, we identify and map seismogenic faults based on earthquake clustering. We then obtain an improved stress map using 2,047 high-quality focal mechanisms. The regional stress map shows a gradual transition from oblique normal faulting in western Oklahoma to strike-slip faulting in central and eastern Oklahoma. Stress amplitude ratio shows a strong correlation with pore pressure from hydrogeologic models, suggesting that pore pressure exhibits a measurable influence on stress patterns. Finally, we assess fault stress state via 3-D Mohr circles; a parameter understress is used to quantify the level of fault criticality (with 0 meaning critically stressed faults and 1 meaning faults with no applied shear stress). Our findings suggest that most active faults have near vertical planes (planarity >0.8 and dip >70°), and there is a strong correlation between fault length and maximum magnitude on each fault. The fault trends show prominent conjugate sets that strike [55–75°] and [105–125°]. A comparison with mapped sedimentary faults and basement fractures reveals common tectonic control. Based on 3-D Mohr circles, we find that 78% of the faults are critically stressed (understress ≤0.2), while several seismogenic faults are misoriented with high understress (>0.4). Fault geometry and local stress fields may be used to evaluate potential seismic hazard, as the largest earthquakes tend to occur on long, critically stressed faults.

58 GEOSCIENCES↗
NASA NTRS2004

Phylogenetic and physiological diversity of microorganisms isolated from a deep greenland glacier ice core

We studied a sample from the GISP 2 (Greenland Ice Sheet Project) ice core to determine the diversity and survival of microorganisms trapped in the ice at least 120,000 years ago. Previously, we examined the phylogenetic relationships among 16S ribosomal DNA (rDNA) sequences in a clone library obtained by PCR amplification from genomic DNA extracted from anaerobic enrichments. Here we report the isolation of nearly 800 aerobic organisms that were grouped by morphology and amplified rDNA restriction analysis patterns to select isolates for further study. The phylogenetic analyses of 56 representative rDNA sequences showed that the isolates belonged to four major phylogenetic groups: the high-G+C gram-positives, low-G+C gram-positives, Proteobacteria, and the Cytophaga-Flavobacterium-Bacteroides group. The most abundant and diverse isolates were within the high-G+C gram-positive cluster that had not been represented in the clone library. The Jukes-Cantor evolutionary distance matrix results suggested that at least 7 isolates represent new species within characterized genera and that 49 are different strains of known species. The isolates were further categorized based on the isolation conditions, temperature range for growth, enzyme activity, antibiotic resistance, presence of plasmids, and strain-specific genomic variations. A significant observation with implications for the development of novel and more effective cultivation methods was that preliminary incubation in anaerobic and aerobic liquid prior to plating on agar media greatly increased the recovery of CFU from the ice core sample.

Bacteria/classification/genetics/isolation & purif↗
NASA NTRS2005

Identification of a typical flight patterns

Method and system for analyzing aircraft data, including multiple selected flight parameters for a selected phase of a selected flight, and for determining when the selected phase of the selected flight is atypical, when compared with corresponding data for the same phase for other similar flights. A flight signature is computed using continuous- valued and discrete-valued flight parameters for the selected flight parameters and is optionally compared with a statistical distribution of other observed flight signatures, yielding a typicality scores for the same phase for other similar flights. A cluster analysis is optionally applied to the flight signatures to define an optimal collection of clusters. A level of atypicality for a selected flight is estimated, based upon an index associated with the cluster analysis.

Irving C Statler↗
NASA NTRS2005

Identification of atypical flight patterns

Method and system for analyzing aircraft data, including multiple selected flight parameters for a selected phase of a selected flight, and for determining when the selected phase of the selected flight is atypical, when compared with corresponding data for the same phase for other similar flights. A flight signature is computed using continuous-valued and discrete-valued flight parameters for the selected flight parameters and is optionally compared with a statistical distribution of other observed flight signatures, yielding atypicality scores for the same phase for other similar flights. A cluster analysis is optionally applied to the flight signatures to define an optimal collection of clusters. A level of atypicality for a selected flight is estimated, based upon an index associated with the cluster analysis.

Statler, Irving C.↗
DOE OSTI2021

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Using co-expression analysis and stress-based screens to uncover Arabidopsis peroxisomal proteins involved in drought response

Peroxisomes are essential organelles that house a wide array of metabolic reactions important for plant growth and development. However, our knowledge regarding the role of peroxisomal proteins in various biological processes, including plant stress response, is still incomplete. Recent proteomic studies of plant peroxisomes significantly increased the number of known peroxisomal proteins and greatly facilitated the study of peroxisomes at the systems level. The objectives of this study were to determine whether genes that encode peroxisomal proteins with related functions are co-expressed in Arabidopsis and identify peroxisomal proteins involved in stress response using in silico analysis and mutant screens. Using microarray data from online databases, we performed hierarchical clustering analysis to generate a comprehensive view of transcript level changes for Arabidopsis peroxisomal genes during development and under abiotic and biotic stress conditions. Many genes involved in the same metabolic pathways exhibited co-expression, some genes known to be involved in stress response are regulated by the corresponding stress conditions, and function of some peroxisomal proteins could be predicted based on their coexpression pattern. Since drought caused expression changes to the highest number of genes that encode peroxisomal proteins, we subjected a subset of Arabidopsis peroxisomal mutants to a drought stress assay. Mutants of the LON2 protease and the photorespiratory enzyme hydroxypyruvate reductase 1 (HPR1) showed enhanced susceptibility to drought, suggesting the involvement of peroxisomal quality control and photorespiration in drought resistance. Lastly, our study provided a global view of how genes that encode peroxisomal proteins respond to developmental and environmental cues and began to reveal additional peroxisomal proteins involved in stress response, thus opening up new avenues to investigate the role of peroxisomes in plant adaptation to environmental stresses.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

Chandra X-ray Observations of Young Clusters: Orion Flanking Fields Data - Volume II

We present results of Chandra observations of two flanking fields (FFs) in Orion, outside the Orion Nebula Cluster (ONC). The observations were taken with the ACIS-I camera with an exposure time of about 48 ks each field. We present a catalog of 417 sources, which includes X-ray luminosity, optical and infrared photometry, and X-ray variability information. We have found 91 variable sources, 33 of which have a flarelike light curve and 11 of which have a pattern of a steady increase or decrease over a 10 hr period. The optical and infrared photometry for the stars identified as X-ray sources are consistent with most of these objects being pre-main-sequence stars with ages younger than 10 Myr. We present evidence for an age difference among the X-ray-selected samples of NGC 2264, Orion FFs, and ONC, with NGC 2264 being the oldest and ONC being the youngest.

pre-main sequence↗
DOE OSTI2022

Phylogenetic structure of specialization: A new approach that integrates partner availability and phylogenetic diversity to quantify biotic specialization in ecological networks

Abstract Biotic specialization holds information about the assembly, evolution, and stability of biological communities. Partner availabilities can play an important role in enabling species interactions, where uneven partner availabilities can bias estimates of biotic specialization when using phylogenetic diversity indices. It is therefore important to account for partner availability when characterizing biotic specialization using phylogenies. We developed an index, phylogenetic structure of specialization (PSS), that avoids bias from uneven partner availabilities by uncoupling the null models for interaction frequency and phylogenetic distance. We incorporate the deviation between observed and random interaction frequencies as weights into the calculation of partner phylogenetic α‐diversity. To calculate the PSS index, we then compare observed partner phylogenetic α‐diversity to a null distribution generated by randomizing phylogenetic distances among the same number of partners. PSS quantifies the phylogenetic structure (i.e., clustered, overdispersed, or random) of the partners of a focal species. We show with simulations that the PSS index is not correlated with network properties, which allows comparisons across multiple systems. We also implemented PSS on empirical networks of host–parasite, avian seed‐dispersal, lichenized fungi–cyanobacteria, and hummingbird pollination interactions. Across these systems, a large proportion of taxa interact with phylogenetically random partners according to PSS, sometimes to a larger extent than detected with an existing method that does not account for partner availability. We also found that many taxa interact with phylogenetically clustered partners, while taxa with overdispersed partners were rare. We argue that species with phylogenetically overdispersed partners have often been misinterpreted as generalists when they should be considered specialists. Our results highlight the important role of randomness in shaping interaction networks, even in highly intimate symbioses, and provide a much‐needed quantitative framework to assess the role that evolutionary history and symbiotic specialization play in shaping patterns of biodiversity. PSS is available as an R package at https://github.com/cjpardodelahoz/pss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Analysis of the S{sub 2}←S{sub 0} vibronic spectrum of the ortho-cyanophenol dimer using a multimode vibronic coupling approach

The S{sub 2}←S{sub 0} vibronic spectrum of the ortho-cyanophenol dimer (oCP){sub 2} is analyzed in a joint experimental and theoretical investigation. Vibronic excitation energies up to 750 cm{sup −1} are covered, which extends our previous analysis of the quenching of the excitonic splitting in this and related species [Kopec et al., J. Chem. Phys. 137, 184312 (2012)]. As we demonstrate, this necessitates an extension of the coupling model. Accordingly, we compute the potential energy surfaces of the ortho-cyanophenol dimer (oCP){sub 2} along all relevant normal modes using the approximate second-order coupled cluster method RI-CC2 and extract the corresponding coupling constants using the linear and quadratic vibronic coupling scheme. These serve as the basis to calculate the vibronic spectrum. The theoretical results are found to be in good agreement with the experimental highly resolved resonant two-photon ionization spectrum. This allows to interpret key features of the excitonic and vibronic interactions in terms of nodal patterns of the underlying vibronic wave functions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Forming a database to study reversed magnetic shear from the National Spherical Torus eXperiment using machine learning

Achieving a long-lived reversed magnetic shear (RMS) target plasma in the National Spherical Torus eXperiment Upgrade will require developing various sustainment scenarios. To help with the ongoing plasma control efforts, the development of a new analysis for the motional Stark effect (MSE) diagnostic using a machine learning algorithm, namely, MSE-ML, is described. MSE-ML will be used to identify patterns during RMS discharges, some of which suffer magnetohydrodynamic (MHD) events resulting in current redistribution and monotonic q-profiles. A database consisting of q and magnetic shear profiles is being constructed primarily based on the existing National Spherical Torus eXperiment data with equilibrium reconstructions constrained by the magnetic field pitch angle profile measured using the multi-channel MSE diagnostic. An unsupervised k-means clustering of the data is developed to study the RMS formation as a function of time. The initial clustering from the q-profiles shows significant differences in both amplitude and the duration of the RMS period. As a goal, the clustering results that detect and distinguish shots with substantial and sustained RMS are to be used as a preprocessing step in a supervised algorithm to identify the underlying conditions that lead to long-lasting improved confinement with RMS. Another aim of the MSE-ML study is to identify precursors of RMS-destroying MHD events in either derived data such as the q-profile or directly measured data such as the magnetic field pitch angle profile.

Uzun-Kaymak, I. U. (ORCID:0000000276251493)↗