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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Examining the potential for detecting simultaneous noble gas and aerosol samples in the international monitoring system radionuclide network

The purpose of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) is to establish a legally binding ban on nuclear weapon test explosions or any other nuclear explosions. The Preparatory Commission for the CTBT Organization (CTBTO PrepCom) is developing the International Monitoring System (IMS) that includes a global network of 80 stations to monitor for airborne radionuclides upon entry into force of the CTBT. All 80 radionuclide stations will monitor for particulate radionuclides and at least half of the stations will monitor for radioxenon. The airborne radionuclide monitoring is an important verification technology both for the detection of a radionuclide release and in the determination of whether the release event originates from a nuclear explosion as opposed to an industrial use of nuclear materials. Nuclear power plants and many medical isotope production facilities release radioxenon into the atmosphere. Low levels of a few particulate isotopes, such as iodine, may also be released. Detections of multiple isotopes are useful for screening the radionuclide samples for relevance to the Treaty. This paper examines the anticipated joint detections in the IMS of noble gas and particulate isotopes from underground nuclear explosions where breaches in the underground containment vents from low levels to up to 1% of the radionuclide inventory of the resulting fission products to the atmosphere. Detection probabilities are based on 844 simulated release events spaced out at 17 release locations and one year in time. Six different release (venting) scenarios, including two fractionated scenarios, were analyzed. When ranked by detection probability, 11 particulate isotopes and one noble gas isotope ( 133 Xe) appear in the top 20 isotopes for all six release scenarios. Using the 11 particulate isotopes and the one noble gas isotope, the IMS has nearly the same detection probability as when 45 particulate and 4 noble gas isotopes are used. Thus, a limited list of relevant radionuclides may be sufficient for treaty verification purposes. The probability that at least one particulate and at least one radioxenon isotope would be detected in the IMS from the release events ranged from 0.15 to 0.86 depending on the release scenario.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2022

High-fidelity photoneutron detection via neutron activation analysis

To interdict illicit radioactive sources and prevent nuclear terror, governments have deployed portal monitor systems at ports of entry. Such portal monitors rely upon the detection of spontaneously-emitted radiation signatures, however, detection scenarios become more challenging for highly enriched uranium, which emits relatively few spontaneous radiation signatures. Photon active interrogation techniques can augment current cargo inspection scenarios and improve detection capabilities for illicit nuclear material. A high-energy photon interrogation source can induce photofission in nuclear material, producing strong radiation signatures for detection. Prompt fission signals are of specific interest because prompt neutrons are greater than 99% of the fission signal. When using detectors capable of spectroscopy, e.g., pulse shape discrimination-capable organic scintillators, the intense interrogating source often will cause pulse pile-up, degrading particle classification. In this work, we demonstrate photoneutron detection via neutron activation of aluminum and iron. The experiments rely upon nuclear reactions that can only be induced by neutrons, and are robust against pile-up, thereby providing high-confidence neutron rates. In this work, we apply neutron activation analysis for the detection of high-energy neutrons produced by high-energy photon irradiation; the depleted uranium sample was irradiated with a 9-MeV bremsstrahlung beam. We find that the irradiated depleted uranium produced an aluminum activation flux of 1.77 × 10 5 n cm –2 s –1 and an iron-56 activation flux of 1.05 × 10 5 n cm –2 s –1 . Furthermore, the ability to detect radioactivity induced by neutrons created in (, n) and photofission reactions demonstrates a new approach for photon active interrogation to identify nuclear materials.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Determination of X-ray detection limit and applications in perovskite X-ray detectors

X-ray detection limit and sensitivity are important figure of merits for perovskite X-ray detectors, but literatures lack a valid mathematic expression for determining the lower limit of detection for a perovskite X-ray detector. In this work, we present a thorough analysis and new method for X-ray detection limit determination based on a statistical model that correlates the dark current and the X-ray induced photocurrent with the detection limit. The detection limit can be calculated through the measurement of dark current and sensitivity with an easy-to-follow practice. Alternatively, the detection limit may also be obtained by the measurement of dark current and photocurrent when repeatedly lowering the X-ray dose rate. While the material quality is critical, we show that the device architecture and working mode also have a significant influence on the sensitivity and the detection limit. Our work establishes a fair comparison metrics for material and detector development.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Light-powered end-to-end neutron detection and imaging with an edge-deployed optical AI chip

Neutron detection is widely used in many applications including nuclear physics, nuclear energy, nuclear technologies and nuclear safeguards. Developing an end-to-end neutron detection and imaging workflow paves way towards fully automated processes for many applications. We implemented an automated workflow for neutron detection experiments which use a solid state image sensor to capture neutron hits as a digital image. We deploy the workflow to an edge-based optical neural network (ONN) to increase the radiation-hardness and lifetime of neutron detection instruments. We present a two-stage neural network framework for detection of neutrons at sub-pixel resolution. The first stage uses a region proposal network to efficiently detect and extract neutron hits from the input camera image. The second stage feeds the extracted hits into a fully connected neural network to predict the sub-pixel hit position. The performance of the two-stage framework is evaluated using the edge-based ONN. The results show that we can achieve above 96% neutron detection accuracy as well as sub-pixel and sub-micron position resolution, while enjoying the advantages of the ONN hardware including radiation-hardness, low energy consumption and high computing speed for integrated edge camera and hardware deployment, when compared with electronic counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Boron nitride neutron detector with the ability for detecting both thermal and fast neutrons

The detection of fast neutrons is regarded technically challenging because the interaction probability of fast neutron with matter is extremely low. Based on our recent development of hexagonal boron nitride (BN) semiconductor thermal neutron detectors with a record high efficiency of 59%, we report here the feasibility studies of BN detectors for detecting fast neutrons. A BN detector with a detection area of 2.1 cm 2 was fabricated from a 90 $μ$m thick BN epilayer. In the presence of a bare Cf-252 source emitting fast neutrons ranging from 1 to 9 MeV, the detection efficiency was estimated to be about 0.1%. Furthermore, the measured mean free path of fast neutron in BN is about 7.6 cm. Together with the capability of BN for thermal neutron detection, the present results indicate that by incorporating BN with a large thickness, BN neutron detectors are expected to possess the unique capability of directly detecting thermal to fast neutrons as well as outstanding features resulting from the ultrawide bandgap of BN. The identification of a single material that is sensitive to both thermal and fast neutrons is valuable for the development of novel neutron detection technologies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Systematic Source Determination and X-Ray Radiography Detection of Nonuniformities on High-Density Carbon Ablators

Recent deuterium-tritium (D-T)–layered implosion experiments at the National Ignition Facility have achieved a burning plasma and >1-MJ neutron yield. A series of repeat experiments have shown that the degree of performance is very likely dependent on capsule quality, including the quantity of what are collectively termed “high- Z particles.” These particles are detected on a custom-built radiography system, known as the Sagometer, during the final target qualification process. The term particles is misleading, as the source of these nonuniformities in the capsule images is uncertain; the term detection will be used instead. An increased number of D-T targets have been rejected at the final stages of production due to Sagometer detections. Late detections are deleterious in terms of loss of production parts, effort, and overall operating efficiency. In response, we undertook an effort to determine the origin of these detections and to ultimately mitigate target losses caused by them. Through careful testing and analysis, we have determined neither insufficient production cleanliness nor hohlraum shedding is responsible for the detections on the capsule. Here, we determined that the detections are inherent to the capsule and have made efforts to use the Zeiss Xradia to identify them earlier in the production process. While testing revealed the Xradia is not currently sufficient for identifying such particles using radiography images, we continue to look to other forms of metrology to down select the capsules early in the process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

CORC $^{\circledR}$ cable terminations with integrated Hall arrays for quench detection

ReBCO superconducting cables have the potential to enable compact thermonuclear fusion reactors that operate at magnetic fields exceeding 20 T and allow operation at temperatures far exceeding the boiling point of liquid helium, potentially allowing for demountable magnets. Normal zone detection remains a challenge, and while novel quench detection techniques are an active area of research, few are non-invasive, provide real-time quench detection, and have been demonstrated with current ramp rates relevant for fusion reactors. To address this problem, a CORC$^{\circledR}$ cable termination is developed with integrated Hall sensors to monitor current redistribution as a proxy for quench detection. The methodology exploits the current sharing and layered topology in CORC$^{\circledR}$ cables, and allows quench detection using a localized sensor instead of co-wound voltage wires or optical fibers. Furthermore, experiments are presented where current redistribution is measured from induced quenches, and in a 0.2 meter CORC$^{\circledR}$ sample it is found that the Hall sensors detect normal zone transitions with a similar magnitude and temporal resolution as voltage measurements. To emulate the conditions of dynamic poloidal and central solenoidal fields, experiments are repeated with ramp rates up to 10 kA s -1 that demonstrate the potential to detect normal zone development over a range of experimental parameters.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Quench detection using Hall sensors in high-temperature superconducting CORC ® -based cable-in-conduit-conductors for fusion applications

Advanced magnet systems for fusion applications would greatly benefit from the use of high-temperature superconductors (HTS). These materials allow fusion magnets to operate at higher magnetic fields, allowing for more compact fusion machines, and allow for operation at elevated temperatures, enabling demountable coils that provide access for maintenance of the fusion reactor. Quench detection remains a major challenge in the protection of HTS magnets that are vulnerable to localized conductor burnout due to their low quench propagation velocities. One of the methods explored is the use of Hall sensors that are incorporated in or near the magnet terminations that can detect local field variations that occur as a result of current redistribution within the conductor to bypass a hotspot within the magnet winding. This method is potentially well suited for Cable in Conduit Conductors, such as those made from Conductor on Round Core (CORC) cables, in which sub-cables containing HTS tapes are connected to the terminations at a low resistance. Here, to demonstrate the technique, a CORC ® triplet consisting of three sub-cables, rated for 4 kA operation at 77 K, was manufactured and Hall sensors were used to measure local field variations next to the terminations due to current redistribution between the cables. The Hall response was compared to voltages that developed over the cables and terminations as a local hotspot was applied to different cables in the triplet. It was found that the Hall sensors were faster and more sensitive than voltage contact measurements and were able to reliably detect current redistribution of only a few amperes caused by a hotspot, well before the triplet exceeded its critical current. The method also allowed the detection of heater-induced hotspots during high ramp rates of 2 kA s<:sup>–1 relevant for fusion applications. Hall sensors have a distinct benefit of being less sensitive to inductive pickup of AC interference compared to voltage contact measurements that make quench detection through voltage measurements in magnets especially challenging. The method can also be used for diagnostic measurements of current redistribution caused by other sources such as inhomogeneous current injection from faulty joints, or localized conductor damage. The Hall sensors are likely capable of detecting the onset of a quench that may occur a far distance away from the sensor location, presenting a breakthrough in HTS quench detection that potentially removes one of the remaining barriers to reliable operation of large HTS magnet systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
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 OSTI2020

Adaptive Anomaly Detection for Dynamic Clinical Event Sequences

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

Niu, Haoran↗
DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Vehicle Tracking with Crop-based Detection

End-to-end production of vehicle tracking data from video in real-time and with high accuracy remains a challenging problem due to the computational cost of object detection on each frame. In this work we present Tracking with Crop-based Detection, a method for speeding object tracking in constrained contexts (with stable cameras and relatively-predictable object motion) such as vehicle traffic monitoring. We leverage this context to provide a strong prior for object locations, which we use to 1.) boost detection speed by detecting objects only in regions corresponding to object priors on most frames and 2.) inform the selection of the detector output for each object. We evaluate Crop-based Detection as an extension to the KIOU object tracker (Crop-KIOU) on the UA-DETRAC dataset. The proposed tracker outperforms all other reported algorithms in terms of PR-MOTA, PR-MOTP, and mostly tracked objects on the UA-DETRAC benchmark, establishing a new state-of-the-art. Relative to tracking by detection with KIOU, Crop-KIOU achieves a 26% higher frame-rate and increases accuracy. Furthermore, Tracking with Crop-based Detection can be combined with frame skipping; we show a 149% increase in framerate relative to KIOU with no decrease in accuracy using this combination of methods.

Gloudemans, Derek↗
DOE OSTI2020

Detection and Localization of Damaged Photovoltaic Cells and Modules Using Spread Spectrum Time Domain Reflectometry

The operating efficiency of photovoltaic (PV) plants can be improved if damaged or degraded modules can be detected and identified. Currently, string-level power electronics can detect problems with modules or cabling but not locate them, which would facilitate addressing these issues. Here, we investigate the ability of spread spectrum time domain reflectometry (SSTDR) to both detect and locate/identify damaged cells and modules within a series-connected PV string. We tested the ability of SSTDR to detect and locate single-cell mini-modules and full-sized PV modules, which were intentionally damaged by impacts with a hammer (breaking the glass and damaging the silicon below) or by cutting through some or all busbars. Damage to the glass and silicon of cells was detected and located within a small string of minimodules. Busbar damage was detectable only if an open was created by cutting through all intercell busbars. Furthermore, physical impact damage to the glass and silicon of a full-sized PV module could be detected, but further development of signal processing is needed to achieve localization of such damaged modules within a string.

14 SOLAR ENERGY↗
DOE OSTI2023

Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.

Erdagi, Ismet Goksad↗
DOE OSTI2022

Real-time Event Detection Using Rank Signatures of Real-world PMU Data

Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture the event signatures. Specifically, we track the change in rank signatures of PMU data to accurately localize the events. To optimize the detection process, we incorporate an offline Bayesian optimization algorithm to tune the parameters by efficiently searching for the best values. The experiments using the real-world PMU dataset from a U.S. interconnection show that the proposed event detection approach can efficiently detect the events from PMU data streams with high accuracy.

Ghasemkhani, Amir↗
DOE OSTI2020

Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%–90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. Finally, the mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

58 GEOSCIENCES↗
DOE OSTI2022

Out of Distribution Detection with Neural Network Anchoring

This is code to reproduce and build on OOD detection from the paper "Out of Distribution Detection with Neural Network Anchoring". Our goal here is to exploit heteroscedastic temperature scaling as a calibration strategy for out of distribution (OOD) detection. Heteroscedasticity here refers to the fact that the optimal temperature parameter for each sample can be different, as opposed to conventional approaches that use the same value for the entire distribution. To enable this, we propose a new training strategy called anchoring that can estimate appropriate temperature values for each sample, leading to state-of-the-art OOD detection performance across several benchmarks. Using NTK theory, we show that this temperature function estimate is closely linked to the epistemic uncertainty of the classifier, which explains its behavior. In contrast to some of the best-performing OOD detection approaches, our method does not require exposure to additional outlier datasets, custom calibration objectives, or model ensembling. Through empirical studies with different OOD detection settings - far OOD, near OOD, and semantically coherent OOD - we establish a highly effective OOD detection approach.

Thiagarajan, Jayaraman↗
DOE OSTI2021

A Frequency-Domain-Based Algorithm for Detecting Microseismicity Using Dense Surface Seismic Arrays

We propose a new frequency-domain-based algorithm for detecting small-magnitude seismic events using dense surface seismic arrays. Our proposed method takes advantage of the high energy carried by S waves, and approximate known source locations, which are used to rotate the horizontal components to obtain the maximum amplitude. By surrounding the known source area with surface geophones, we achieve a favorable geometry for locating the detected seismic events with the backprojection method. To test our new detection method, we used a dense circular array, consisting of 151 5 Hz three-component geophones, over a 5 km aperture that was in operation at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah. We apply the new detection method during a small-scale test injection phase at FORGE, and during an aftershock sequence of an Mw 4.1 earthquake located ~30 km north of the geophone array, within the Black Rock volcanic field. We are able to detect and locate microseismic events (Mw<0) during injections, despite the high level of anthropogenic activity, and several aftershocks that are missing from the regional catalog. By comparing our method with known algorithms that operate both in the time and frequency domain, we show that our proposed method performs better in the case of the FORGE injection monitoring, and equally well for the off-array aftershock sequence. Our new method has the potential to improve microseismic event detections even in extremely noisy environments, and the proposed location scheme serves as a direct discriminant between true and false detections.

Geochemistry & Geophysics↗