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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Diversity of Abundance Patterns of Light Neutron-capture Elements in Very-metal-poor Stars

We determine the abundances of neutron-capture elements from Sr to Eu for five very-metal-poor stars (−3 < [Fe/H] < −2) in the Milky Way halo to reveal the origin of light neutron-capture elements. Previous spectroscopic studies have shown evidence of at least two components in the r-process; one referred to as the “main r-process” and the other as the “weak r-process,” which is mainly responsible for producing heavy and light neutron-capture elements, respectively. Observational studies of metal-poor stars suggest that there is a universal pattern in the main r-process, similar to the abundance pattern of the r-process component of solar-system material. Still, it is uncertain whether the abundance pattern of the weak r-process shows universality or diversity, due to the sparseness of measured light neutron-capture elements. We have detected the key elements, Mo, Ru, and Pd, in five target stars to give an answer to this question. The abundance patterns of light neutron-capture elements from Sr to Pd suggest a diversity in the weak r-process. In particular, scatter in the abundance ratio between Ru and Pd is significant when the abundance patterns are normalized at Zr. Our results are compared with the elemental abundances predicted by nucleosynthesis models of supernovae with parameters such as electron fraction or proto-neutron-star mass, to investigate sources of such diversity in the abundance patterns of light neutron-capture elements. This paper presents that the variation in the abundances of observed stars can be explained with a small range of parameters, which can serve as constraints on future modeling of supernova models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system

Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.

Hou, Zhangshuan↗
DOE OSTI2017

Multi-resolution anisotropy studies of ultrahigh-energy cosmic rays detected at the Pierre Auger Observatory

Here, we report a multi-resolution search for anisotropies in the arrival directions of cosmic rays detected at the Pierre Auger Observatory with local zenith angles up to 80(o) and energies in excess of 4 EeV (4 × 10 18 eV). This search is conducted by measuring the angular power spectrum and performing a needlet wavelet analysis in two independent energy ranges. Both analyses are complementary since the angular power spectrum achieves a better performance in identifying large-scale patterns while the needlet wavelet analysis, considering the parameters used in this work, presents a higher efficiency in detecting smaller-scale anisotropies, potentially providing directional information on any observed anisotropies. No deviation from isotropy is observed on any angular scale in the energy range between 4 and 8 EeV. Above 8 EeV, an indication for a dipole moment is captured, while no other deviation from isotropy is observed for moments beyond the dipole one. The corresponding p-values obtained after accounting for searches blindly performed at several angular scales, are 1.3 × 10 -5 in the case of the angular power spectrum, and 2.5 × 10 -3 in the case of the needlet analysis. While these results are consistent with previous reports making use of the same data set, they provide extensions of the previous works through the thorough scans of the angular scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Using Temporal Information from Human Mobility Data to Detect Anchor Points

Spatiotemporal mobility data are available in massive quantities, but large quantities of data typically include fewer variables or data fields. Often, the only available fields are User ID, Longitude, Latitude, Timestamp (ULLT). This raises an important question: how much can we infer about human mobility patterns using only these four fields? With ULLT data, we do not know individuals' socioeconomic status information or when they are visiting their anchor points (AP) or locations (such as homes, places of employment, or schools), and it is a modern challenge to use this data to infer these characteristics. When detecting anchor locations with limited input information, verification and validation (VV) are significant challenges. This paper addresses the problem of identifying individuals' anchor locations using only temporal information from spatiotemporal datasets with limited attributes. Our approach does not explicitly use latitude and longitude during analysis. Locationbased information is only employed in the preprocessing stage to identify periods of movement (trips) and stops (dwelling). Beyond this step, all analysis is based on temporal patterns. In theory, if stops and dwell times could be detected through alternative means, our method could function entirely without location-based input. We demonstrate this methodology on the 2017 National Household Travel Survey (NHTS) data, because it includes a carefully designed and collected time use survey with representative sampling and labeled ground truth. The high-quality survey data allows us to test the accuracy of our methods because NHTS contains intended place labels and agent/user characteristics. We have also applied our validated AP identification algorithm on very large-scale GPS based trajectory data for Patterns-of-Life (PoL) assessment and other applications, but due to space limit that could not be presented here.

McBride, Liz [ORNL] (ORCID:0000000286925869)↗
DOE OSTI2014

External-field-free magnetic biosensor

In this paper, we report a magnetic nanoparticle (MNP) detection scheme without the presence of any external magnetic field. The proposed magnetic sensor uses a patterned groove structure within the sensor so that no external magnetic field is needed to magnetize the MNPs. An example is given based on a giant magnetoresistance (GMR) sensing device with a spin valve structure. For this structure, the detection of MNPs located inside the groove and near the free layer is demonstrated under no external magnetic field. Micromagnetic simulations are performed to calculate the signal to noise level of this detection scheme. A maximum signal to noise ratio (SNR) of 18.6 dB from one iron oxide magnetic nanoparticle with 8 nm radius is achieved. As proof of concept, this external-field-free GMR sensor with groove structure of 200 nm × 200 nm is fabricated using a photo and an electron beam integrated lithography process. Using this sensor, the feasibility demonstration of the detection SNR of 9.3 dB is achieved for 30 μl magnetic nanoparticles suspension (30 nm iron oxide particles, 1 mg/ml). This proposed external-field-free sensor structure is not limited to GMR devices and could be applicable to other magnetic biosensing devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Topological Engineering of Interfacial Optical Tamm States for Highly Sensitive Near-Singular-Phase Optical Detection

Here, we developed planar multilayered photonic-plasmonic structures, which support topologically protected optical states on the interface between metal and dielectric materials, known as optical Tamm states. Coupling of incident light to the Tamm states can result in perfect absorption within one of several narrow frequency bands, which is accompanied by a singular behavior of the phase of electromagnetic field. In the case of near-perfect absorptance, very fast local variation of the phase can still be engineered. In this research, we theoretically and experimentally demonstrate how these drastic phase changes can improve sensitivity of optical sensors. A planar Tamm absorber was fabricated and used to demonstrate remote near-singular-phase temperature sensing with an over an order of magnitude improvement in sensor sensitivity and over two orders of magnitude improvement in the figure of merit over the standard approach of measuring shifts of resonant features in the reflectance spectra of the same absorber. Our experimentally demonstrated phase-to-amplitude detection sensitivity improvement nearly doubles that of state-of-the-art nano-patterned plasmonic singular-phase detectors, with further improvements possible via more precise fabrication. Tamm perfect absorbers form the basis for robust planar sensing platforms with tunable spectral characteristics, which do not rely on low-throughput nano-patterning techniques.

42 ENGINEERING↗
DOE OSTI2015

Detection of nanoscale embedded layers using laboratory specular X-ray diffraction

Unusual specular X-ray diffraction patterns have been observed from certain thin film intergrowths of metal monochalcogenide (MX) and transition metal dichalcogenide (TX{sub 2}) structures. These patterns exhibit selective “splitting” or broadening of selected (00l) diffraction peaks, while other (00l) reflections remain relatively unaffected [Atkins et al., Chem. Mater. 24, 4594 (2012)]. Using a simplified optical model in the kinematic approximation, we illustrate that these peculiar and somewhat counterintuitive diffraction features can be understood in terms of additional layers of one of the intergrowth components, MX or TX{sub 2}, interleaved between otherwise “ideal” regions of MX-TX{sub 2} intergrowth. The interpretation is in agreement with scanning transmission electron microscope imaging, which reveals the presence of such stacking “defects” in films prepared from non-ideal precursors. In principle, the effect can be employed as a simple, non-destructive laboratory probe to detect and characterize ultrathin layers of one material, e.g., 2-dimensional crystals, embedded between two slabs of a second material, effectively using the two slabs as a highly sensitive interferometer of their separation distance.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Statistical Techniques For Real-time Anomaly Detection Using Spark Over Multi-source VMware Performance Data

Anomaly detection refers to the identi cation of an irregular or unusual pat- tern which deviates from what is standard, normal, or expected. Such deviated patterns typically correspond to samples of interest and are assigned different labels in different domains, such as outliers, anomalies, exceptions, or malware. Detecting anomalies in fast, voluminous streams of data is a formidable chal- lenge. This paper presents a novel, generic, real-time distributed anomaly detection framework for heterogeneous streaming data where anomalies appear as a group. We have developed a distributed statistical approach to build a model and later use it to detect anomaly. As a case study, we investigate group anomaly de- tection for a VMware-based cloud data center, which maintains a large number of virtual machines (VMs). We have built our framework using Apache Spark to get higher throughput and lower data processing time on streaming data. We have developed a window-based statistical anomaly detection technique to detect anomalies that appear sporadically. We then relaxed this constraint with higher accuracy by implementing a cluster-based technique to detect sporadic and continuous anomalies. We conclude that our cluster-based technique out- performs other statistical techniques with higher accuracy and lower processing time.

Apache Spark↗
DOE OSTI2023

Confident detection of doubly ionized thorium in the extreme Ap star CPD-62° 2717

Despite the Universe containing primordial thorium (Th) of sufficient abundance to appear in stellar spectra, detection of Th has to date been tentative and based on just a few weak and blended lines. Here, we present convincing evidence not only for the first Th detection in a magnetic chemically peculiar Ap star but also for the first detection of Th iii in a stellar spectrum. CPD-62° 2717 was initially recognized as a highly magnetized Ap star due to resolved magnetically split lines captured in H-band spectra from the SDSS/APOGEE survey. The star was subsequently pinpointed as extraordinarily peculiar when careful inspection of the H-band line content revealed the presence of five lines of Th iii, none of which are detected in the other ∼1500 APOGEE-observed Ap stars. Follow-up with the VLT + UVES confirmed a similarly peculiar optical spectrum featuring dozens of Th iii lines, among other peculiarities. Unlike past claims of Th detection, and owing to high-resolution observations of the strong (∼8–12 kG) magnetic field of CPD-62° 2717, the detection of Th iii can in this case be supported by matches between the observed and theoretical magnetic splitting patterns. Comparison of CPD-62° 2717 to stars for which Th overabundances have been previously reported (e.g. Przybylski’s Star) indicates that only for CPD-62° 2717 is the Th detection certain. Along with the focus on Th iii, we use time series measurements of the magnetic field modulus to constrain the rotation period of CPD-62° 2717 to ∼4.8 yr, thus establishing it as a new example of a superslowly rotating Ap star.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Real time monitoring of superparamagnetic nanoparticle self-assembly on surfaces of magnetic recording media

Nanoparticle self-assembly dynamics are monitored in real-time by detecting optical diffraction from an all-nanoparticle grating as it self-assembles on a grating pattern recorded on a magnetic medium. The diffraction efficiency strongly depends on concentration, pH, and colloidal stability of nanoparticle suspensions, demonstrating the nanoparticle self-assembly process is highly tunable. This metrology could provide an alternative for detecting nanoparticle properties such as colloidal stability.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantitative stable isotope probing (qSIP) and cross-domain networks reveal bacterial-fungal interactions in the hyphosphere

Interactions between fungi and bacteria have the potential to substantially influence soil carbon dynamics in soil, but we have yet to fully identify these interactions and partners in their natural environment. In this study, we stacked two powerful methods, 13 C quantitative stable isotope probing (qSIP) and cross-domain co-occurrence network, to identify interacting fungi and bacteria in a California grassland soil. We used in-field whole plant 13 CO 2 labeling along with sand-filled ingrowth bags (that trap fungi and hyphae-associated bacteria) to amplify the signal of fungal-bacterial interactions, separate from the bulk soil background. We found a total of 54 bacterial ASVs and 9 fungal OTUs that were significantly 13 C-enriched. These were saprotrophic and biotrophic fungi, and motile, sometimes predatory bacteria. Among these, 70% of all 13 C-enriched bacteria identified were motile. Notably, we detected fungal-bacterial network links between a fungal OTU of the genus Alternaria and several bacterial ASVs of the genera Bacteriovorax, Mucilaginibacter, and Flavobacterium, providing empirical evidence of their direct interactions through C exchange. We observed a strong positive co-occurrence pattern between predatory bacteria of the phylum Bdellovibrionota and fungal OTUs, suggesting the transfer of C across the soil food web. To date, our ability to associate microbial co-occurrence network patterns with biological interactions is limited, but the incorporation of qSIP allowed us to more precisely detect interacting partners by narrowing in on the taxa that were actively incorporating plant-fixed, fungal-transported labeled substrates. Together, these approaches can help build a mechanistic understanding of the complex nature of fungal-bacterial interactions in soil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Localized excitation of magnetostatic surface spin waves in yttrium iron garnet by shorted coaxial probe detected via spin pumping and rectification effect

We demonstrate the localized excitation and dc electrical detection of magnetostatic surface spin waves (MSSWs) in yttrium iron garnet (YIG) by a shorted coaxial probe. Thin films of NiFe and Pt are patterned at different regions onto a common bulk YIG substrate. A shorted coaxial probe is used to excite spin precession locally near various patterned regions. The dc voltages across the corresponding regions are recorded. For excitation of the Pt regions, the dc voltage spectra are dominated by the spin pumping of MSSWs from YIG, where various modes can be clearly distinguished. For the NiFe region, it is also found that spin pumping from MSSWs generated in YIG dominated the spectra, indicating that the spin pumped currents are dissipated into charge currents via the inverse Spin Hall effect (ISHE) in NiFe. For all regions, dc signals from YIG MSSWs are observed to be much stronger than the ferromagnetic resonance (FMR) uniform mode, likely due to the nature of the microwave excitation. The results indicate the potential of this probe for microwave imaging via dc detection of spin dynamics in continuous and patterned films.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Failure Analysis on Multivariate Time-series Data given Uncertain Labels

Machine failure analysis and detection is critical to today’s industrial society. Given a number of failure events on multivariate temporal data, the ability to identify 1) the discriminative patterns prior to the events and 2) the most relevant features to the failure has practical use for early warning and root cause analysis. However, since these patterns are not necessarily adjacent to the onset of failure in time, faulty labels are often with uncertainty, which makes traditional supervised detection methods inapplicable. To address the label uncertainty and learn the complicated correlation in multivariate time series, we design Failure Analysis on Multivariate Time-series Data (MAMT) that jointly selects the most failure-relevant features and time-instances by a novel dynamic and directional label diffusion process. Extensive experiments demonstrate that MAMT is more effective while more efficient than popular baselines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Gender and collaboration patterns in a temporal scientific authorship network

One can point to a variety of historical milestones for gender equality in STEM (science, technology, engineering, and mathematics), however, practical effects are incremental and ongoing. It is important to quantify gender differences in subdomains of scientific work in order to detect potential biases and monitor progress. In this work, we study the relevance of gender in scientific collaboration patterns in the Institute for Operations Research and the Management Sciences (INFORMS), a professional society with sixteen peer-reviewed journals. Using their publication data from 1952 to 2016, we constructed a large temporal bipartite network between authors and publications, and augmented the author nodes with gender labels. We characterized differences in several basic statistics of this network over time, highlighting how they have changed with respect to relevant historical events. We find a steady increase in participation by women (e.g., fraction of authorships by women and of new women authors) starting around 1980. However, women still comprise less than 25% of the INFORMS society and an even smaller fraction of authors with many publications. Moreover, we describe a methodology for quantifying the structural role of an authorship with respect to the overall connectivity of the network, using it to measure subtle differences between authorships by women and by men. Specifically, as measures of structural importance of an authorship, we use effective resistance and contraction importance, two measures related to diffusion throughout a network. As a null model, we propose a degree-preserving temporal and geometric network model with emergent communities. Our results suggest the presence of systematic differences between the collaboration patterns of men and women that cannot be explained by only local statistics.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Radionuclide gas transport through nuclear explosion-generated fracture networks

Underground nuclear weapon testing produces radionuclide gases which may seep to the surface. Barometric pumping of gas through explosion-fractured rock is investigated using a new sequentially-coupled hydrodynamic rock damage/gas transport model. Fracture networks are produced for two rock types (granite and tuff) and three depths of burial. The fracture networks are integrated into a flow and transport numerical model driven by surface pressure signals of differing amplitude and variability. There are major differences between predictions using a realistic fracture network and prior results that used a simplified geometry. Matrix porosity and maximum fracture aperture have the greatest impact on gas breakthrough time and window of opportunity for detection, with different effects between granite and tuff simulations highlighting the importance of accurately simulating the fracture network. In particular, maximum fracture aperture has an opposite effect on tuff and granite, due to different damage patterns and their effect on the barometric pumping process. From stochastic simulations using randomly generated hydrogeologic parameters, normalized detection curves are presented to show differences in optimal sampling time for granite and tuff simulations. In conclusion, seasonal and location-based effects on breakthrough, which occur due to differences in barometric forcing, are stronger where the barometric signal is highly variable.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Transducer selection and application in magnetoacoustic tomography with magnetic induction

As an acoustic receiver, transducer plays a vital role in signal acquisition and image reconstruction for magnetoacoustic tomography with magnetic induction (MAT-MI). In order to optimize signal acquisition, the expressions of acoustic pressure detection and waveform collection are theoretically studied based on the radiation theory of acoustic dipole and the reception pattern of transducer. Pressure distributions are simulated for a cylindrical phantom model using a planar piston transducer with different radii and bandwidths. The proposed theory is also verified by the experimental measurements of acoustic waveform detection for an aluminum foil cylinder. It is proved that acoustic pressure with sharp and clear boundary peaks can be detected by the large-radius transducer with wide bandwidth, reflecting the differential of the induced Lorentz force accurately, which is helpful for precise conductivity reconstruction. To detect acoustic pressure with acceptable pressure amplitude, peak pressure ratio, amplitude ratio, and improved signal to noise ratio, the scanning radius of 5–10 times the radius of the object should be selected to improve the accuracy of image reconstruction. This study provides a theoretical and experimental basis for transducer selection and application in MAT-MI to obtain reconstructed images with improved resolution and definition.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

A quality assurance framework for the fully automated and objective evaluation of image quality in cone-beam computed tomography

Purpose: Thousands of cone-beam computed tomography (CBCT) scanners for vascular, maxillofacial, neurological, and body imaging are in clinical use today, but there is no consensus on uniform acceptance and constancy testing for image quality (IQ) and dose yet. The authors developed a quality assurance (QA) framework for fully automated and time-efficient performance evaluation of these systems. In addition, the dependence of objective Fourier-based IQ metrics on direction and position in 3D volumes was investigated for CBCT. Methods: The authors designed a dedicated QA phantom 10 cm in length consisting of five compartments, each with a diameter of 10 cm, and an optional extension ring 16 cm in diameter. A homogeneous section of water-equivalent material allows measuring CT value accuracy, image noise and uniformity, and multidimensional global and local noise power spectra (NPS). For the quantitative determination of 3D high-contrast spatial resolution, the modulation transfer function (MTF) of centrally and peripherally positioned aluminum spheres was computed from edge profiles. Additional in-plane and axial resolution patterns were used to assess resolution qualitatively. The characterization of low-contrast detectability as well as CT value linearity and artifact behavior was tested by utilizing sections with soft-tissue-equivalent and metallic inserts. For an automated QA procedure, a phantom detection algorithm was implemented. All tests used in the dedicated QA program were initially verified in simulation studies and experimentally confirmed on a clinical dental CBCT system. Results: The automated IQ evaluation of volume data sets of the dental CBCT system was achieved with the proposed phantom requiring only one scan for the determination of all desired parameters. Typically, less than 5 min were needed for phantom set-up, scanning, and data analysis. Quantitative evaluation of system performance over time by comparison to previous examinations was also verified. The maximum percentage interscan variation of repeated measurements was less than 4% and 1.7% on average for all investigated quality criteria. The NPS-based image noise differed by less than 5% from the conventional standard deviation approach and spatially selective 10% MTF values were well comparable to subjective results obtained with 3D resolution pattern. Determining only transverse spatial resolution and global noise behavior in the central field of measurement turned out to be insufficient. Conclusions: The proposed framework transfers QA routines employed in conventional CT in an advanced version to CBCT for fully automated and time-efficient evaluation of technical equipment. With the modular phantom design, a routine as well as an expert version for assessing IQ is provided. The QA program can be used for arbitrary CT units to evaluate 3D imaging characteristics automatically.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC -heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2%, recall of 89.7%, precision of 88.5% and F1-score of 89.2%. Finally, these results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗