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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Perspective: Tipping the scales: Search for drifting constants from molecular spectra

Transitions in atoms and molecules provide an ideal test ground for constraining or detecting a possible variation of the fundamental constants of nature. In this perspective, we review molecular species that are of specific interest in the search for a drifting proton-to-electron mass ratio μ. In particular, we outline the procedures that are used to calculate the sensitivity coefficients for transitions in these molecules and discuss current searches. These methods have led to a rate of change in μ bounded to 6 × 10{sup −14}/yr from a laboratory experiment performed in the present epoch. On a cosmological time scale, the variation is limited to |Δμ/μ| < 10{sup −5} for look-back times of 10–12× 10{sup 9} years and to |Δμ/μ| < 10{sup −7} for look-back times of 7× 10{sup 9} years. The last result, obtained from high-redshift observation of methanol, translates into μ{sup .}/μ=(1.4±1.4)×10{sup −17}/yr if a linear rate of change is assumed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Murchison widefield array observations of anomalous variability: a serendipitous night-time detection of interplanetary scintillation

We present observations of high-amplitude rapid (2 s) variability toward two bright, compact extragalactic radio sources out of several hundred of the brightest radio sources in one of the 30{sup ∘}×30{sup ∘} Murchison Widefield Array (MWA) Epoch of Reionization fields using the MWA at 155 MHz. After rejecting intrinsic, instrumental, and ionospheric origins we consider the most likely explanation for this variability to be interplanetary scintillation (IPS), likely the result of a large coronal mass ejection propagating from the Sun. This is confirmed by roughly contemporaneous observations with the Ooty Radio Telescope. We see evidence for structure on spatial scales ranging from <1000 to >10{sup 6} km. The serendipitous night-time nature of these detections illustrates the new regime that the MWA has opened for IPS studies with sensitive night-time, wide-field, low-frequency observations. This regime complements traditional dedicated strategies for observing IPS and can be utilized in real-time to facilitate dedicated follow-up observations. At the same time, it allows large-scale surveys for compact (arcsec) structures in low-frequency radio sources despite the 2{sup ′} resolution of the array.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software

A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Deep Learning for Unsupervised Insider Threat Detection in Structured Cyber Security Data Streams

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

Deep Learning, Insider Threat↗
DOE OSTI2017

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams

Analysis of an organization’s computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach’s potential to greatly reduce analyst workloads.

Tuor, Aaron R.↗
DOE OSTI2019

Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks

Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one cyberattack on load forecasting data. Here, a widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Crack detection in crystalline silicon solar cells using dark-field imaging

The high capital expenditure (capex) necessary to manufacture crystalline silicon PV modules negatively affects the levelized cost of electricity (¢/kWh) and critically impacts the rate at which the PV industry can scale up. Wafer, cell, and module fabrication with thin free-standing silicon wafers is one key to reduce capex. Thin wafers reduce capex associated with silicon refining and wafer fabrication, which together sum to 58% of the total capex of silicon module manufacturing. In addition, thin wafers directly and significantly reduce variable costs. However, introducing 50 μm thin free-standing wafers into today’s manufacturing lines result in cracking, creating a yield-based disincentive. Due to the brittle nature of silicon, wafer breakage is the major concern due to the high stress that is induced during processes in manufacturing lines. In this paper, we describe an improved method for edge micro-crack detection that can help enable low-capex, thin free-standing Si wafers. We present a method of detecting and measuring cracks along wafer edges by using a dark-field IR scattering imaging technique which enables detection of edge cracks at the micron scale.

14 SOLAR ENERGY↗
DOE OSTI2024

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1

Abstract Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

Wang, Jiaxin (ORCID:0000000348085085)↗
DOE OSTI2020

ContourNet: Salient Local Contour Identification for Blob Detection in Fusion Plasma Simulation Data

We present ContourNet, a deep learning approach to identify salient local isocontours as blobs in large-scale 5D gyrokinetic tokamak simulation data. Blobs-regions of high turbulence that run along the edge wall down toward the diverter and can damage the tokamak-are non-well-defined features but have been empirically localized by isocontours in 2D normalized fluctuating density fields. The key of our study is to train ContourNet to follow the empirical rules to detect blobs over the time-varying simulation data. The architecture of ContourNet is a convolutional neural segmentation network: the inputs are the density field and a rasterized isocontour; the output is a set of isocontour encircling blobs. At the training stage, we feed the network with manually identified isocontours and propagated labels. At the inference stage, we extract isocontours from the segmented blob regions. Results show that our approach can achieve both high accuracy and performance, which enables scientists to understand the blob dynamics influencing the confinement of the plasma.

Blob detection↗
DOE OSTI2024

3D‐Printed Carbon Nanoneedle Electrodes for Dopamine Detection in Drosophila

In vivo electrochemistry in small brain regions or synapses requires nanoelectrodes with long straight tips for submicron scale measurements. Nanoelectrodes can be fabricated using a Nanoscribe two-photon printer, but annealed tips curl if they are long and thin. We propose a new pulling-force strategy to fabricate a straight carbon nanoneedle structure. A micron-width bridge is printed between two blocks. The annealed structure shrinks during pyrolysis, and the blocks create a pulling force to form a long, thin, and straight carbon bridge. Parameterization study and COMSOL modeling indicate changes in the block size, bridge size and length affect the pulling force and bridge shrinkage. Electrodes were printed on niobium wires, insulated with aluminum oxide, and the bridge cut with focused ion beam (FIB) to expose the nanoneedle tip. Annealed needle diameters ranged from 400 nm to 5.25 μm and length varied from 50.5 μm to 146 μm. The electrochemical properties are similar to glassy carbon, with good performance for dopamine detection with fast-scan cyclic voltammetry. Nanoelectrodes enable biological applications, such as dopamine detection in a specific Drosophila brain region. Long and thin nanoneedles are generally useful for other applications such as cellular sensing, drug delivery, or gas sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

Constraining primordial magnetic fields with future cosmic shear surveys

The origin of astrophysical magnetic fields observed in galaxies and clusters of galaxies is still unclear. One possibility is that primordial magnetic fields generated in the early Universe provide seeds that grow through compression and turbulence during structure formation. A cosmological magnetic field present prior to recombination would produce substantial matter clustering at intermediate/small scales, on top of the standard inflationary power spectrum. In this work we study the effect of this alteration on one particular cosmological observable, cosmic shear. We adopt the semi-analytic halo model in order to describe the non-linear clustering of matter, and feed it with the altered mass variance induced by primordial magnetic fields. We find that the convergence power spectrum is, as expected, substantially enhanced at intermediate/small angular scales, with the exact amplitude of the enhancement depending on the magnitude and power-law index of the magnetic field power spectrum. Specifically, for a fixed amplitude, the effect of magnetic fields is larger for larger spectral indices. We use the predicted statistical errors for a future wide-field cosmic shear survey, on the model of the ESA Cosmic Vision mission Euclid, in order to forecast constraints on the amplitude of primordial magnetic fields as a function of the spectral index. We find that the amplitude will be constrained at the level of ∼ 0.1 nG for n{sub B} ∼ −3, and at the level of ∼ 10{sup −7} nG for n{sub B} ∼ 3. The latter is at the same level of lower bounds coming from the secondary emission of gamma-ray sources, implying that for high spectral indices Euclid will certainly be able to detect primordial magnetic fields, if they exist. The present study shows how large-scale structure surveys can be used for both understanding the origins of astrophysical magnetic fields and shedding new light on the physics of the pre-recombination Universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Intrinsic alignments of galaxies around cosmic voids

ABSTRACT The intrinsic alignments of galaxies, i.e. the correlation between galaxy shapes and their environment, are a major source of contamination for weak gravitational lensing surveys. Most studies of intrinsic alignments have so far focused on measuring and modelling the correlations of luminous red galaxies with galaxy positions or the filaments of the cosmic web. In this work, we investigate alignments around cosmic voids. We measure the intrinsic alignments of luminous red galaxies detected by the Sloan Digital Sky Survey around a sample of voids constructed from those same tracers and with radii in the ranges: [20–30; 30–40; 40–50] h−1 Mpc and in the redshift range z = 0.4−0.8. We present fits to the measurements based on a linear model at large scales, and on a new model based on the void density profile inside the void and in its neighbourhood. We constrain the free scaling amplitude of our model at small scales, finding no significant alignment at 1σ for either sample. We observe a deviation from the null hypothesis, at large scales, of 2σ for voids with radii between 20 and 30 h−1 Mpc, and 1.5σ for voids with radii between 30 and 40 h−1 Mpc and constrain the amplitude of the model on these scales. We find no significant deviation at 1σ for larger voids. Our work is a first attempt at detecting intrinsic alignments of galaxy shapes around voids and provides a useful framework for their mitigation in future void lensing studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Superconducting Qubits for Dark Matter Detection

Developments over the last decade have pushed the search for particle dark matter (DM) to new frontiers, including the keV-scale lower mass limit for thermally-produced DM. Galactic DM at this mass is kinematically matched with the energy needed to break a Cooper pair in common superconductors (~meV). Quantum sensors such as superconducting qubits are sensitive to these broken Cooper pairs, and can potentially be exploited as low-threshold detectors for particle-like DM scattering. The Quantum Science Center group at Fermilab exploring the use of qubit-based detectors for particle detection in the LOUD surface dilution fridge facility. This talk will discuss recent R&D efforts to understand qubit response to energy depositions through a combination of measurements in LOUD and low-energy G4CMP simulations of our superconducting qubit chips.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Radiolytic formation of the carbon dioxide radical anion in acetonitrile revealed by transient IR spectroscopy

In this study, the solvated electron in CH 3 CN is scavenged by CO 2 with a rate constant of 3.2 × 10 10 M –1 s –1 to produce the carbon dioxide radical anion (CO 2 •– ), a strong and versatile reductant. Using pulse radiolysis with time-resolved IR detection, this radical is unambiguously identified by its absorption band at 1650 cm –1 corresponding to the antisymmetric CO 2 •– stretch. This assignment is confirmed by 13 C isotopic labelling experiments and DFT calculations. In neat CH 3 CN, CO 2 •– decays on a ~10 μs time scale via recombination with solvent-derived radicals (R•) and solvated protons. Upon addition of formate (HCO 2 – ), the radiation yield of CO 2 •– is substantially increased due to H-atom abstraction by R• from HCO 2 – (R• + HCO 2 – → RH + CO 2 •– ), which occurs in two kinetically separated steps. The rapid step involves the stronger H-abstracting CN•, CH 3 •, and possibly, H• primary radicals, while the slower step is due to the less reactive, but more abundant radical, CH 2 CN•. The removal of solvent radicals by HCO 2 – also results in over a hundredfold increase in the CO 2 •– lifetime. CO 2 •– scavenging experiments suggest that at 50 mM HCO 2 – , about 60% of the solvent-derived radicals are engaged in CO 2 •– generation. Finally, even under CO 2 saturation, no formation of the radical adduct, (CO 2 ) 2 •– , could be detected on the microsecond time scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Determination of the Spatial Distribution of Indigenous Lipid Biomarkers in an Immature Jurassic Sediment Using Time-of-Flight–Secondary Ion Mass Spectrometry

The ability to detect and map lipids, including potential lipid biomarkers, within a sedimentary matrix using mass spectrometry (MS) imaging may be critical to determine whether potential lipids detected in samples returned from Mars are indigenous to Mars or are contaminants. Here, we use gas chromatography–mass spectrometry (GC-MS) and time-of-flight–secondary ion mass spectrometry (ToF-SIMS) datasets collected from an organic-rich, thermally immature Jurassic geologic sample to constrain MS imaging analysis of indigenous lipid biomarkers in geologic samples. GC-MS data show that the extractable fractions are dominated by C 27 –C 30 steranes and sterenes as well as isorenieratene derivatives. ToF-SIMS spectra from organic matter-rich laminae contain a strong, spatially restricted signal for ions m/z 370.3, m/z 372.3, and m/z 386.3, which we assign to C 27 sterenes, cholestane (C 27 ), and 4- or 24-methyl steranes (C 28 ), respectively, as well as characteristic fragment ions of isorenieratene derivatives, including m/z 133.1, m/z 171.1, and m/z 237.1. We observed individual steroid spatial heterogeneity at the scale of 10's to 100's of microns. The fine-scale heterogeneity observed implies that indigenous lipid biomarkers concentrated within specific regions may be detectable via ToF-SIMS in samples with even low amounts of organic carbon, including in samples returned from Mars.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Test of the CLAS12 RICH large-scale prototype in the direct proximity focusing configuration

A large-area ring-imaging Cherenkov detector has been designed to provide clean hadron identi- fication capability in the momentum range from 3 GeV/c up to 8 GeV/c for the CLAS12 experiment at the upgraded 12 GeV continuous electron beam accelerator facility of Jefferson Laboratory. The adopted solution foresees a novel hybrid optics design based on aerogel radiator, composite mirrors and highly packed and highly segmented photon detectors. Cherenkov light will either be imaged directly (forward tracks) or after two mirror reflections (large-angle tracks). We report here the results of the tests of a large-scale prototype of the RICH detector performed with the hadron beam of the CERN T9 experimental hall for the direct detection configuration. The tests demonstrated that the proposed design provides the required pion-to-kaon rejection factor of 1 : 500 in the whole momentum range.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Studying baryon acoustic oscillations using photometric redshifts from the DESI Legacy Imaging survey DR9

Context. The Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Survey DR9 (DR9 hereafter), with its extensive dataset of galaxy locations and photometric redshifts, presents an opportunity to study baryon acoustic oscillations (BAOs) in the region covered by the ongoing spectroscopic survey with DESI. Aims. We aim to investigate differences between different parts of the DR9 footprint. Furthermore, we want to measure the BAO scale for luminous red galaxies within them. Our selected redshift range of 0.6–0.8 corresponds to the bin in which a tension between DESI Y1 and eBOSS was found. Methods. We calculated the anisotropic two-point correlation function in a modified binning scheme to detect the BAOs in DR9 data. We then used template fits based on simulations to measure the BAO scale in the imaging data. Results. Our analysis reveals the expected correlation function shape in most of the footprint areas, showing a BAO scale consistent with Planck’s observations. Aside from identified mask-related data issues in the southern region of the South Galactic Cap, we find a notable variance between the different footprints. Conclusions. We find that this variance is consistent with the difference between the DESI Y1 and eBOSS data, and it supports the argument that that tension is caused by sample variance. Additionally, we also uncovered systematic biases not previously accounted for in photometric BAO studies. We emphasize the necessity of adjusting for the systematic shift in the BAO scale associated with typical photometric redshift uncertainties to ensure accurate measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2015

Modeling and Analysis of Realistic Fire Scenarios in Spacecraft

An accidental fire inside a spacecraft is an unlikely, but very real emergency situation that can easily have dire consequences. While much has been learned over the past 25+ years of dedicated research on flame behavior in microgravity, a quantitative understanding of the initiation, spread, detection and extinguishment of a realistic fire aboard a spacecraft is lacking. Virtually all combustion experiments in microgravity have been small-scale, by necessity (hardware limitations in ground-based facilities and safety concerns in space-based facilities). Large-scale, realistic fire experiments are unlikely for the foreseeable future (unlike in terrestrial situations). Therefore, NASA will have to rely on scale modeling, extrapolation of small-scale experiments and detailed numerical modeling to provide the data necessary for vehicle and safety system design. This paper presents the results of parallel efforts to better model the initiation, spread, detection and extinguishment of fires aboard spacecraft. The first is a detailed numerical model using the freely available Fire Dynamics Simulator (FDS). FDS is a CFD code that numerically solves a large eddy simulation form of the Navier-Stokes equations. FDS provides a detailed treatment of the smoke and energy transport from a fire. The simulations provide a wealth of information, but are computationally intensive and not suitable for parametric studies where the detailed treatment of the mass and energy transport are unnecessary. The second path extends a model previously documented at ICES meetings that attempted to predict maximum survivable fires aboard space-craft. This one-dimensional model implies the heat and mass transfer as well as toxic species production from a fire. These simplifications result in a code that is faster and more suitable for parametric studies (having already been used to help in the hatch design of the Multi-Purpose Crew Vehicle, MPCV).

life support↗