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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Machine learning applied to wind turbine blades impact detection

The significant development of wind power generation worldwide brings, together with environmental benefits, wildlife concerns, especially for volant species vulnerability to interactions with wind energy facilities. For surveying such events, an automatic system for continuous monitoring of blade collisions is critical. An onboard multi-senor system capable of providing real-time collision detection using integrated vibration sensors is developed and successfully tested. However, to detect low signal-to-noise ratio impact can be challenging; hence, an advanced impact detection method has been developed and presented in this article. A robust automated detection algorithm based on support vector machine is proposed. After a preliminary signal pre-processing, geometric features specifically selected for their sensitivity to impact signals are extracted from raw vibration signal and energy distribution graph. The predictive model is formulated by training conventional support vector machine using extracted features for impact identification. Finally, the performance of the predictive model is evaluated by accuracy, precision, and recall. Results indicate a linear regression relationship between signal-to-noise ratio and model overall performance. Here, the proposed method is much reliable on higher signal-to-noise ratio (SNR≥6), but it shows to be ineffective at lower signal-to-noise ratio (SNR<2).

17 WIND ENERGY↗
DOE OSTI2024

Metal Oxide vs Organic Semiconductor Charge Extraction Layers for Halide Perovskite Indoor Photovoltaics

Halide perovskite indoor photovoltaics (PVs) are highly promising to autonomously power the billions of microelectronic sensors in the emerging and disruptive technology of the Internet of Things (IoT). However, how the wide range of different types of hole extraction layers (HELs) impacts the indoor light harvesting of perovskite solar cells is still elusive, which hinders the material selection and industrial–scale fabrication of indoor perovskite photovoltaics. In the present study, new insights are provided regarding the judicial selection of HELs at the buried interface of halide perovskite indoor photovoltaics. This study unravels the detrimental and severe light–soaking effect of metal oxide transport layer–based PV devices under the indoor lighting effect for the first time, which then necessitates the interface passivation/engineering for their reliant performance. This is not a stringent criterion under 1 sun illumination. By systematically investigating the charge carrier dynamics and sequence of measurements from dark, light–soaked, interlayer–passivated device, the bulk and interface defects are decoupled and reveal the gradual defect passivation from shallow to deep level traps. Thus, the present study puts forward a useful design strategy to overcome the deleterious effect of metal oxide HELs and employ them in halide perovskite indoor PVs.

14 SOLAR ENERGY↗
DOE OSTI2021

Development of a new control rod drive mechanism design for the ISU AGN-201M reactor

The Aerojet General Nucleonics (AGN) model 201-Modified, known as the AGN-201M reactor, plays an essential role in the educational and research activities at Idaho State University (ISU). The ISU AGN-201M's original Control Rod Drive Mechanism (CRDM) has been in operation for more than fifty years with no large-scale redesigns. The CRDM is required to eject the fuel rods within one second during a SCRAM event (also known as a 'reactor trip') and adjust the control rods' insertion speed, and keeps the rod insertion sequence correct. The existing control rod drive mechanisms meet these criteria but experience a few concerns due to aging. Concerns include complex maintenance and costly repairs for old electromechanical components, rod position feedback errors, and the impediment of the plate during a SCRAM due to binding of the lead screws of the existing mechanism. During a binding event, the drive mechanism becomes locked, preventing the control rod's magnetic plate from moving in or out under the reactor's normal and emergency operating conditions. Although, the binding has no effect on the ability of the rod to exit the core during the SCRAM. To counteract the concerns and issues with the current CRDM, a new design has been proposed using newer components and a simplified design. The new design utilizes more advanced electric and mechanical components that are commercially available. The new CRDM system is divided into four main aspects: (1) control rod movement design (motor, lead screw, guide rods), (2) control rod ejection (springs, electromagnet), (3) control rod position and feedback (position transducer, microswitches), and (4) material selection and structural analysis. The new design aims to reduce the overall complexity and probability of failure to improve the reactor's overall reliability. With proper material selection and improved structural design, the new drives are lighter with little to no change in structural integrity. The new control rod drive mechanism eliminates binding scenarios by using a single lead screw and implementing additional guide rods. An advanced linear position sensor and microswitches replace the existing and aging synchro system for accurate rod position feedback resulting in better reactivity control. The new design meets the reactor's operational limits by having an average reactivity insertion of 0.065% Δk/k per second, which corresponds to a total control rod insertion time of 19.23 s, while the control rod's ejection time remains less than one second during a SCRAM event. The new design ensures the reactor's long-term viability for educational and research activities by increasing the reliability and safety of operation for years to come.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Sap Velocity Data for East River Watershed Sites (2023-2025)

This dataset includes sap velocity measurements for aspen (populus tremuloides), fir (abies lasiocarpa), spruce (Engelmann spruce) and lodgepole pine (pinus contorta) trees at nine sites in the East River Watershed near Gothic, CO. This dataset was generated following a similar method as a previous dataset (Dataset. doi:10.15485/1647654) but was conducted at different sites in the area and now includes lodgepole pine. Site selection was done to explicitly improve understanding of topographic controls of tree water use. The data collection began in June 2023 and we provide data until December 2025 - though data collection is ongoing. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. We also provide a normalized sap velocity estimate by subtracting each measurement from that trees' annual minimum and dividing by that trees' annual maximum. This provides data for each tree and year on a 0-1 scale. This data entry contains one CSV file that includes all available sap flow data. The timestamps are provided in local time as year, day of year and hour and each measurement contains an associated latitude, longitude and site number which can be used to identify trees in a given stand. Each species is given a numeric value as: 1= populus tremuloides, 2=Engelmann spruce, 3=abies lasiocarpa and 4=pinus contorta.

abies↗
DOE OSTI2016

Advanced Sensor Arrays and Packaging

Novel sensor packaging elements were designed, fabricated, and tested in order to facilitate the transition of electrochemical mixed-potential sensors toward commercialization. Of the two designs completed, the first is currently undergoing field trials, taking direct measurements within vehicle exhaust streams, while the second is undergoing preliminary laboratory testing. The sensors’ optimal operating conditions, sensitivity to hydrogen, and long-­term baseline stability were also investigated. The sensing capabilities of lanthanum chromite (La 0.8 Sr 0.2 CrO 3 ) and indium-­doped tin oxide (ITO) working electrodes were compared, and the ITO devices were selected for pre-­commercial field trials testing at a hydrogen fuel cell vehicle fueling station in California. Previous data from that fueling station were also analyzed, and the causes of anomalous baseline drift were identified.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Integrated System for Methane Emissions Monitoring, Mapping, and Quantification

This report presents the work completed under the DOE iM4 project for the development of a methane emission monitoring system for detection, location, and quantification of methane in oil and gas industries. The task was divided into four main areas including: 1) Sensors and Input, 2) Centralized Cloud Information Center, 3) Algorithms, and 4) Testing and Validation. Task 1 focused on researching and developing an understanding of the current, or soon to be, available methane sensing technologies. Task 2 consisted of developing the architecture, selecting hardware, software and elements for the methane monitoring system. Task 3 focused on the algorithms used for the complex inverse model of going from measured methane signatures to the detection, localization, and quantification of sources that are desired. Finally, Task 4 focused on the methods of testing and validating the operation of the system. Attention was also given to the development method and cost breakdown of the system.

03 NATURAL GAS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, AD model validation through real-world sensor data is important for applications in nuclear facilities. In this paper, we propose an Autoencoder (AE)—a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD)—as another AD scheme for identifying irregularities withinthe same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Detection and Quantification of Biologically Active Botulinum Neurotoxin Serotypes A and B Using a Förster Resonance Energy Transfer-Based Quantum Dot Nanobiosensor

Botulinum neurotoxin (BoNT) is the most potent toxin known. The ingestion of food contaminated with biologically active BoNT causes foodborne botulism, which can lead to respiratory paralysis, coma, and death after ingestion of as little as 70 mu g for a 70 kg human. Because of its lethality and challenges associated with current detection methods, there is an urgent need for highly sensitive rapid screening techniques capable of detecting biologically active BoNT. Here, we describe a Forster resonance energy transfer-based nanobiosensor that uses quantum dots (QDs) and two specific quencher-labeled peptide probes to detect and differentiate two biologically active forms of BoNT, serotypes A and B, which were responsible for 80% of human foodborne botulism cases in the U.S. from 2012 to 2015. Each peptide probe contains an enzymatic cleavage site specific to only one serotype. QDs were selected based on the spectral overlap with the quenchers. In the presence of the target BoNT serotype, the peptide probe is cleaved and the quenching of QD photoluminescence (PL) is reduced, giving a signal that is easily detected by a PL spectrophotometer. This sensor performance was evaluated with light chains of BoNT/A and BoNT/B (LcA and LcB), catalytic domains of the respective serotypes. LcA and LcB were detected in 3 h with limits of detection of 0.2 and 2 ng/mL, respectively. The specificity of the sensor was evaluated, and no cross-reactivity from nontarget serotypes was observed with 2 h of incubation. Because each serotype-specific peptide is conjugated to a QD with a unique emission wavelength, multiple biologically active BoNT serotypes could be detected in one PL spectrum. The sensor was also shown to be responsive to BoNT/A and BoNT/B holotoxins. Good performance of this sensor implies its potential application as a rapid screening method for biologically active BoNT/A and BoNT/B in the laboratory and in the field.

botulinum neurotoxin↗
DOE OSTI2015

Thermal tuning of infrared resonant absorbers based on hybrid gold-VO{sub 2} nanostructures

Resonant absorbers based on plasmonic materials, metamaterials, and thin films enable spectrally selective absorption filters, where absorption is maximized at the resonance wavelength. By controlling the geometrical parameters of nano/microstructures and materials' refractive indices, resonant absorbers are designed to operate at wide range of wavelengths for applications including absorption filters, thermal emitters, thermophotovoltaic devices, and sensors. However, once resonant absorbers are fabricated, it is rather challenging to control and tune the spectral absorption response. Here, we propose and demonstrate thermally tunable infrared resonant absorbers using hybrid gold-vanadium dioxide (VO{sub 2}) nanostructure arrays. Absorption intensity is tuned from 90% to 20% and 96% to 32% using hybrid gold-VO{sub 2} nanowire and nanodisc arrays, respectively, by heating up the absorbers above the phase transition temperature of VO{sub 2} (68 °C). Phase change materials such as VO{sub 2} deliver useful means of altering optical properties as a function of temperature. Absorbers with tunable spectral response can find applications in sensor and detector applications, in which external stimulus such as heat, electrical signal, or light results in a change in the absorption spectrum and intensity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗
DOE OSTI2019

Sequential Learning with Active Partial Labeling for Building Metadata

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts.We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A Threshold Helium Leakage Detection Switch with Ultra Low Power Operation

Detecting helium leakage is important in many applications, such as in dry cask nuclear waste storage systems. This work develops a helium detection system based on the relative permittivity (dielectric constant) difference between air and helium. This difference changes the status of an electrostatic microelectromechanical system (MEMS) switch. The switch is a capacitive-based device and requires a very negligible amount of power. Exciting the switch’s electrical resonance enhances the MEMS switch sensitivity to detect low helium concentration. This work simulates two different MEMS switch configurations: a cantilever-based MEMS modeled as a single-degree-freedom model and a clamped-clamped beam MEMS molded using the COMSOL Multiphysics finite-element software. While both configurations demonstrate the switch’s simple operation concept, the clamped-clamped beam was selected for detailed parametric characterization due to its comprehensive modeling approach. The beam detects at least 5% helium concentration levels when excited at 3.8 MHz, near electrical resonance. The switch performance decreases at lower excitation frequencies or increases the circuit resistance. The MEMS sensor detection level was relatively immune to beam thickness and parasitic capacitance changes. However, higher parasitic capacitance increases the switch’s susceptibility to errors, fluctuations, and uncertainties.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Testing of the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is being designed to provide a secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. ACTS is an active device that uses an almost-universal core platform that can be appropriately configured to provide the necessary data acquisition, data logging, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. The core design supports active monitoring of containment, movement, and location and provides secure data transmission via selectable communication methods that can be used throughout the world. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, thus providing an integration path for current and new technologies. ACTS contains a built-in set of sensors but also supports an expansion bus for up to six additional communications, tracking, or sensing modules that may enhance monitoring and tracking of particular containers for specific applications. ACTS stores events in memory, communicates activity to Argonne National Laboratory’s ARG-US TransPort tracking and information server, and provides options for data encryption and authentication. We are currently beginning a testing phase that will field five devices outdoors in a controlled environment to allow evaluation of the functionality of the systems and the hardness of the devices to outdoor conditions. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory, and commercialization partner The Aquila Group.

Britton Jr, Chuck↗
DOE OSTI2019

Characterization of Cameras for the COSMO K-coronagraph

Digital image sensors are ubiquitous in astronomical instrumentation and it is well known that they suffer from issues that must be corrected for data to be scientifically useful. I present discussion on errors resulting from digitization and characterization of nonlinearity and ADC errors of the PhotonFocus MV-D1024E cameras selected for the K-coronagraph of the Coronal Solar Magnetism Observatory. I derive an analytic expression for quantization errors. The MV-D1024E camera has adequate bit depth for which quantization error is not an issue. I show that this is not the case for all cameras, particularly those with deep wells and low read noise. The impact of nonlinearity and ADC errors on science observations of the K-coronagraph is analyzed using a simplified telescope model. Errors caused by the camera ADCs result in systematic errors in the measurement of the polarimetric signal of several times 10{sup −9} B {sub ⊙}, which is about an order of magnitude above the desired sensitivity. I demonstrate a method for post-facto data correction using a lookup table and derive parameters from camera characterization measurements that were made with a lab setup. Nonlinearity is traditionally addressed with a global correction. I show through analysis of calibration data that for the MV-D1024E this correction leaves residual systematic errors after dark and gain correction of up to 1% of the signal. I demonstrate that a pixel-wise correction of nonlinearity reduces the errors to below 0.1%. These corrections are necessary for the K-coronagraph data products to meet the science requirements. They have been implemented in the instrument data acquisition system and data reduction pipeline. While no other instruments besides the K-coronagraph or cameras besides the MV-D1024E are discussed here, the results are illustrative for all instruments and cameras.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Statistical and Neural Network for Real Sensor-Data-Driven Anomaly Detection in Nuclear Applications

Anomaly detection (AD) in sensor data is critical to ensure uninterrupted functionality of nuclear power plants (NPPs). Consequently, validation of AD models through real-world sensor data is important for their application in nuclear facilities. In this paper, we propose an Autoencoder (AE)— a multi-layered neural network, for AD in sensor data from an operational NPP testbed. Since the dataset lacks labels for irregularities, we introduce random noise and label them to effectively train our model. The proposed AE model assigns a higher reconstruction error to the abnormal samples that deviate from those encountered during the training phase and uses the reconstruction loss to detect anomalies in a representative imbalanced dataset. We also introduce an analytical solution—seasonal trend decomposition (STD) — as another AD scheme for identifying irregularities within the same time-series dataset. In contrast to the AE model which relies on reconstruction loss, the STD scheme decomposes the entire dataset into its trend, seasonality, and residual components to pinpoint irregularities. Our findings indicate that the proposed AE and STD models individually achieve recall scores of 97% and 92%, respectively. We also validate the performance of the two models on both balanced and imbalanced data. We further solidify the results by picking the combined selected anomalies of the two solutions with an "AND" operator for more reliable predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Electrically-cooled HPGe detector for advanced x-ray spectroscopy and imaging

High Purity Germanium (HPGe) detectors are used for high-resolution x- and gamma-ray spectroscopy. For their operation, the necessary cryogenic cooling is performed with liquid nitrogen or with electromechanical coolers. Although mature and industrialized solutions, most of HPGe detectors integrating electrical coolers present a limited spectroscopic performance due to the generated mechanical vibration and electromagnetic interference. This paper describes a novel HPGe detector, specifically designed to address the challenges of ultimate x-ray spectroscopy and imaging applications. Due to the stringent demands associated with nano-scale imaging in synchrotron applications, a custom-designed cryostat was built around a Canberra CP5-Plus electrical cooler featuring extremely low vibration levels and high cooling power. The heat generated by the cryo-cooler itself, as well as the electronics, is evacuated via an original liquid cooling circuit. This architecture can also be used to address high ambient temperature, which does not allow conventional cryo-coolers to work properly. The multichannel detector head can consist of a segmented monolithic HPGe sensor, or several closely packed sensors. Each sensor channel is read out by state-of-the-art pulse-reset preamplifiers in order to achieve excellent energy resolution for count rates in excess of 1 Mcps. The sensitive electronics are located in EMI-proof housings to avoid any interference from other devices on a beam-line. The front-end of the detector is built using selected high-purity materials and alloys to avoid any fluorescence effects. We present a detailed description of the detector design and we report on its performance. A discussion is also given on the use of electrically cooled HPGe detectors for applications requiring ultimate energy resolution, such as synchrotron, medicine or nuclear industry. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A SiPM-based optical readout system for the EIC dual-radiator RICH

Silicon photomultipliers (SiPM) are candidates selected as the potential photodetector technology for the dual-radiator Ring-Imaging Cherenkov (dRICH) detector at the future Electron-Ion Collider (EIC). SiPM are cheap devices, highly efficient and insensitive to the high magnetic field at the expected location of the sensors in the experiment. Here, on the other hand, SiPM are not radiation tolerant. Despite the integrated radiation level is expected to be moderate it should be tested whether single photon-counting capabilities and the increase in dark count rate can be kept under control to maintain the optimal dRICH detector performance across the years. The current status of the research for the EIC dRICH application and the first results on studies performed on a sample of commercial and prototype SiPM sensors are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗