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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

CTBT infrasound network performance to detect the 2013 Russian fireball event

The explosive fragmentation of the 2013 Chelyabinsk meteorite generated a large airburst with an equivalent yield of 500 kT TNT. It is the most energetic event recorded by the infrasound component of the Comprehensive Nuclear-Test-Ban Treaty-International Monitoring System (CTBT-IMS), globally detected by 20 out of 42 operational stations. This study performs a station-by-station estimation of the IMS detection capability to explain infrasound detections and nondetections from short to long distances, using the Chelyabinsk meteorite as global reference event. Investigated parameters influencing the detection capability are the directivity of the line source signal, the ducting of acoustic energy, and the individual noise conditions at each station. Findings include a clear detection preference for stations perpendicular to the meteorite trajectory, even over large distances. Only a weak influence of stratospheric ducting is observed for this low-frequency case. As a result, a strong dependence on the diurnal variability of background noise levels at each station is observed, favoring nocturnal detections.

58 GEOSCIENCES↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗
DOE OSTI2018

Automated Segmentation and Quantification of Drusen in Fundus and Optical Coherence Tomography Images for Detection of ARMD

Age-related macular degeneration (ARMD) is one of the most common retinal syndromes that occurs in elderly people. Different eye testing techniques such as fundus photography and optical coherence tomography (OCT) are used to clinically examine the ARMD-affected patients. Many researchers have worked on detecting ARMD from fundus images, few of them also worked on detecting ARMD from OCT images. However, there are only few systems that establish the correspondence between fundus and OCT images to give an accurate prediction of ARMD pathology. In this paper, we present fully automated decision support system that can automatically detect ARMD by establishing correspondence between OCT and fundus imagery. The proposed system also distinguishes between early, suspect and confirmed ARMD by correlating OCT B-scans with respective region of the fundus image. In first phase, proposed system uses different B-scan based features along with support vector machine (SVM) to detect the presence of drusens and classify it as ARMD or normal case. In case input OCT scan is classified as ARMD, region of interest from corresponding fundus image is considered for further evaluation. The analysis of fundus image is performed using contrast enhancement and adaptive thresholding to detect possible drusens from fundus image and proposed system finally classified it as early stage ARMD or advance stage ARMD. The proposed system is tested on local data set of 100 patients with100 fundus images and 6800 OCT B-scans. Proposed system detects ARMD with the accuracy, sensitivity, and specificity ratings of 98.0, 100, and 97.14%, respectively.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data

Underground hydrogen storage (UHS) is considered as a scalable approach for massive storage and seasonal extraction of hydrogen (H 2 ). Although conventional leakage detection and characterization methods based on time-lapse seismic imaging and inversion generally apply to H 2 leakage detection problem, a high-fidelity yet cost effective geophysics approach is still missing to reliably inform leakage location and properties based on very sparse data. In response, we develop a novel supervised machine learning method to detect and characterize H 2 leakage from UHS. The input to our neural network are sparse time-lapse seismic waveforms, while the output from the neural network includes the spatial location and physical properties of a H 2 leakage. Here, we generate high-quality time-lapse waveforms using the elastic-wave equations to train the neural network. We train and validate our machine learning model and find that it attains high accuracy in using extremely sparse time-lapse seismic data to detect and characterize H 2 leakage. Our investigation is the first systematic study that focuses on applying machine learning to subsurface H 2 leakage detection and characterization and could potentially serve as a cost-effective geophysical tool for underground hydrogen leakage detection and characterization with high fidelity.

08 HYDROGEN↗
DOE OSTI2022

Detecting anomalous sequences in electronic health records using higher-order tensor networks

Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

A stilbene - CdZnTe based radioxenon detection system

Atmospheric monitoring of radioxenon is one of the most widely used methods by the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) to detect elevated levels of 131m Xe, 133/133m Xe, and 135 Xe. The ratios of these radionuclides help discriminate between peaceful use of nuclear technology and nuclear weapon explosions. Radioxenon detection systems often use plastic scintillators in the capacity of an electron detector and a gas cell, plastic gas cells are responsible for introducing high memory effect in these systems. This work presents the design of a new detection system for radioxenon monitoring that utilizes silicon photomultipliers, a stilbene gas cell, and a CdZnTe detector. This detector was evaluated using xenon radioisotope samples produced in the TRIGA reactor at Oregon State University. Here, a 48-h background was collected and calculations of the Minimum Detectable Concentration (MDC) were carried out using the Region of Interest (ROI) approach. An MDC of less than 1 mBq/m 3 was obtained for 131m Xe, 133 Xe, and 133m Xe in accordance with the sensitivity limits set by the CTBTO and performs respectably when compared to state-of-the-art radioxenon detection systems. Using 131m Xe, this study indicates that the stilbene gas cell exhibits a memory effect of 0.045 ±0.017%, this is almost a two-order magnitude improvement compared to plastic scintillators. The main purpose of this work is to explore the use of new stilbene detection media for radioxenon application and addressing the problem of memory effect.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2016

Reactive impinging-flow technique for polymer-electrolyte-fuel-cell electrode-defect detection

Reactive impinging flow (RIF) is a novel quality-control method for defect detection (i.e., reduction in Pt catalyst loading) in gas-diffusion electrodes (GDEs) on weblines. The technique uses infrared thermography to detect temperature of a nonflammable (<4% H 2 ) reactive mixture of H 2 /O 2 in N 2 impinging and reacting on a Pt catalytic surface. In this article, different GDE size defects (with catalyst-loading reductions of 25, 50, and 100%) are detected at various webline speeds (3.048 and 9.144 m min -1 ) and gas flowrates (32.5 or 50 standard L min -1 ). Furthermore, a model is developed and validated for the technique, and it is subsequently used to optimize operating conditions and explore the applicability of the technique to a range of defects. The model suggests that increased detection can be achieved by recting more of the impinging H 2 , which can be accomplished by placing blocking substrates on the top, bottom, or both of the GDE; placing a substrate on both results in a factor of four increase in the temperature differential, which is needed for smaller defect detection. Lastly, overall, the RIF technique is shown to be a promising route for in-line, high-speed, large-area detection of GDE defects on moving weblines.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2015

Arsenic detection in water: YPO{sub 4}:Eu{sup 3+} nanoparticles

This work reports on the novel technique of detection of arsenic in aqueous solution utilising the luminescence properties of lanthanide doped nanomaterials. Eu{sup 3+} (5%) doped YPO{sub 4}nanorodswere utilised for the said experiment. Co-precipitation method was used for the synthesis of the materials and characterised them with different instrumental techniques like X-ray diffraction (XRD), Infra-red (IR), UV-absorption, scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS) and photoluminescence studies. This nanoparticle can adsorb both arsenic and arsenious acids. We studied the effect of arsenic adsorption on the luminescence behaviour of the nanoparticles. Arsenic acid enhanced the luminescence intensity whereas arsenious acid quenched the luminescence. This luminescence enhancement or quenching is related with arsenic concentration. This relation of luminescence property with concentration of arsenic can be used to detect arsenic in industrial waste. - Graphical abstract: Novel technique of detection of Arsenic ion in aqueous solution utilising the luminescence properties of lanthanide doped nanomaterials. Potential application for detection of arsenic in drinking and industrial waste water. - Highlights: • Novel technique of detection of Arsenic in aqueous solution by YPO{sub 4}:Eu{sup 3+} nanomaterials. • The effect of arsenic adsorption on the luminescence behaviour of the nanoparticles was studied. • Arsenic acid enhance whereas arsenious acid quenches the luminescence intensity. • This technique can be used to detect arsenic in industrial waste.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

A Zn based anionic metal-organic framework for trace Hg{sup 2+} ion detection

Highlights: • The anionic MOF Zn-TPTC was prepared by solvothermal method. • Zn-TPTC was characterized by single crystal diffraction, PXRD, EA, TGA, etc. • The Hg{sup 2+} detection LOD was 3.67 nM, much lower than the U.S. EPA standard. • Zn-TPTC could exclude the interference of other ions. • Pore, anionic framework, multiple N sites are pivotal for effective Hg{sup 2+} detection. - Abstract: As one of the most toxic heavy metal ions, Hg{sup 2+} exists widely in the living surroundings of human beings and excessive Hg{sup 2+} possesses a great threat to human health. The maximum allowable concentration of Hg{sup 2+} in drinking water is only 10 nM (2 ppb) by U.S. EPA standard. Ionic metal-organic framework (iMOF) is considered in view of high sensitive, anti-interferential material for Hg{sup 2+} detection still remain a challenge. Herein we designed and synthesized a novel Zn based anionic MOF Zn-TPTC, which was characterized by single crystal diffraction and other method. Its performance on Hg{sup 2+} (10{sup -6}–10{sup -4} M) detection were studied and the detection limit (LOD) is calculated as low as 3.67 nM. Interference experiments showed that Zn-TPTC could exclude the interference of other ions, which could be attributed to the effective pore size, anionic framework and multiple N sites in ligand, providing us new ideas for the selection and design of MOF for detection application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A stilbene–strontium iodide based radioxenon detection system for monitoring nuclear explosions

Atmospheric measurement of noble gases has been extensively used for monitoring clandestine nuclear weapon explosions for many years. The ratios of four xenon isotopes of interest ( 131 mXe, 133 mXe, 133 Xe, and 135 Xe) help in discriminating regular reactor operations from nuclear tests. A new coincidence-based detection system using stilbene and strontium iodide [SrI 2 (Eu)] for electron and photon detection respectively was developed at Oregon State University to address some of the challenges of the radioxenon systems deployed in the field such as memory effect, and poor energy resolution. Silicon photomultipliers (SiPMs) were used for sensing optical photons from all scintillation media. Real-time coincidence identification was achieved using the eight-channel digital pulse processor. The detection system was evaluated using lab check sources and Oregon State TRIGA reactor irradiated radioxenon samples. A 48-hour background coincidence spectrum was collected yielding a coincidence count rate and background rejection rate of 0.0174 ± 0.0003 counts per second (cps) and 98.9% respectively. The minimum detectable concentration (MDC) of the system was evaluated to be 0.11 ± 0.01, 0.13 ± 0.02, 0.20 ± 0.02, and 0.73 ± 0.08 for 131 mXe, 133 mXe, 133 Xe, and 135 Xe respectively. The memory effect of the detection system was found to be 0.069 ± 0.015%, which is almost a 70-fold reduction compared to traditional plastic scintillators. Here, the detection elements, custom-designed electronics, and the detector response to radioxenon are detailed in this work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

An islanding detection methodology combining decision trees and Sandia frequency shift for inverter-based distributed generations

Distributed generations (DGs) for grid-connected applications require an accurate and reliable islanding detection methodology (IDM) for secure system operation. This paper presents an IDM for grid-connected inverter-based DGs. The proposed method is a combination of passive and active islanding detection techniques for aggregation of their advantages and elimination/minimisation of the drawbacks. In the proposed IDM, the passive method utilises critical system attributes extracted from local voltage measurements at target DG locations as well as employs decision tree-based classifiers for characterisation and detection of islanding events. The active method is based on Sandia frequency shift technique and is initiated only when the passive method is unable to differentiate islanding events from other system events. Thus, the power quality degradation introduced into the system by active islanding detection techniques can be minimised. Furthermore, a combination of active and passive techniques allows detection of islanding events under low power mismatch scenarios eliminating the disadvantage associated with the use of passive techniques alone. Finally, detailed case study results demonstrate the effectiveness of the proposed method in detection of islanding events under various power mismatch scenarios, load quality factors and in the presence of single or multiple grid-connected inverter-based DG units.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Communication: Atomic force detection of single-molecule nonlinear optical vibrational spectroscopy

Atomic Force Microscopy (AFM) allows for a highly sensitive detection of spectroscopic signals. This has been first demonstrated for NMR of a single molecule and recently extended to stimulated Raman in the optical regime. We theoretically investigate the use of optical forces to detect time and frequency domain nonlinear optical signals. We show that, with proper phase matching, the AFM-detected signals closely resemble coherent heterodyne-detected signals. Applications are made to AFM-detected and heterodyne-detected vibrational resonances in Coherent Anti-Stokes Raman Spectroscopy (χ{sup (3)}) and sum or difference frequency generation (χ{sup (2)})

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Revolution in nuclear detection affairs

The detection of nuclear or radioactive materials for homeland or national security purposes is inherently difficult. This is one reason detection efforts must be seen as just one part of an overall nuclear defense strategy which includes, inter alia, material security, detection, interdiction, consequence management and recovery. Nevertheless, one could argue that there has been a revolution in detection affairs in the past several decades as the innovative application of new technology has changed the character and conduct of detection operations. This revolution will likely be most effectively reinforced in the coming decades with the networking of detectors and innovative application of anomaly detection algorithms.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2015

Magnetic particle detection in unshielded environment using orthogonal fluxgate gradiometer

A new detection system for magnetic particles, which can operate in an unshielded environment, is developed using a fundamental mode orthogonal fluxgate gradiometer. The proposed detection system offers the advantages of cost, size, and weight reduction as compared to contamination detection systems using superconducting quantum interference device sensor. The detection system can be used to detect metallic contamination in foods or lithium ion battery production lines. The system has been investigated numerically to optimize various design parameters of the system. Experimental setup has been developed to evaluate some of the numerically predicted results. Steel balls were successfully detected down to the diameter of 50 μm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Subspace-Driven Learning for Anomaly Detection in Process Transients

Nuclear power plant (NPP) monitoring and diagnostic centers are actively investigating and implementing automated anomaly detection algorithms to help plants catch anomalies sooner, thereby preventing or reducing the duration of unexpected shutdowns. Current machine learning-based anomaly detection methods are expected to be highly effective during stable, full-power operations because NPPs typically operate as baseload power generators, meaning there are extensive operating data available from plant equipment. However, it is expected that anomaly detection methods will face significant challenges during transient conditions (i.e., when power output falls below full power) because plants only occasionally operate at these lower power levels, generating sparse transient operational data, and resulting in false alarms or missed detections. Here, to address this issue, transfer learning is used, which for this problem leverages knowledge (in the form of learned features) from stable, full-power operations to improve detection accuracy during transient conditions, even with limited data. In this effort, a novel subspace approach is developed to transfer a subset of the data features from full power operation to transients. This approach is validated through experiments using synthetic data and was found to outperform two baseline transfer learning approaches in anomaly detection performance across a range of amounts of transient data used in the training process.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2014

Microlensing events by Proxima Centauri in 2014 and 2016: Opportunities for mass determination and possible planet detection

We have found that Proxima Centauri, the star closest to our Sun, will pass close to a pair of faint background stars in the next few years. Using Hubble Space Telescope (HST) images obtained in 2012 October, we determine that the passage close to a mag 20 star will occur in 2014 October (impact parameter 1.''6), and to a mag 19.5 star in 2016 February (impact parameter 0.''5). As Proxima passes in front of these stars, the relativistic deflection of light will cause shifts in the positions of the background stars of ∼0.5 and 1.5 mas, respectively, readily detectable by HST imaging, and possibly by Gaia and ground-based facilities such as the Very Large Telescope. Measurement of these astrometric shifts offers a unique and direct method to measure the mass of Proxima. Moreover, if Proxima has a planetary system, the planets may be detectable through their additional microlensing signals, although the probability of such detections is small. With astrometric accuracies of 0.03 mas (achievable with HST spatial scanning), centroid shifts caused by Jovian planets are detectable at separations of up to 2.''0 (corresponding to 2.6 AU at the distance of Proxima), and centroid shifts by Earth-mass planets are detectable within a small band of 8 mas (corresponding to 0.01 AU) around the source trajectories. Jovian planets within a band of about 28 mas (corresponding to 0.036 AU) around the source trajectories would produce a brightening of the source by >0.01 mag and could hence be detectable. Estimated timescales of the astrometric and photometric microlensing events due to a planet range from a few hours to a few days, and both methods would provide direct measurements of the planetary mass.

79 ASTRONOMY AND ASTROPHYSICS↗