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At least 919 records · Page 51

All sources NASA NTRS DOE OSTI

DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

A first test of CUPID prototypal light detectors with NTD-Ge sensors in a pulse-tube cryostat

CUPID is a next-generation bolometric experiment aiming at searching for neutrinoless double-beta decay with ~250 kg of isotopic mass of 100 Mo. It will operate at ~10 mK in a cryostat currently hosting a similar-scale bolometric array for the CUORE experiment at the Gran Sasso National Laboratory (Italy). CUPID will be based on large-volume scintillating bolometers consisting of 100 Mo-enriched Li 2 MoO 4 crystals, facing thin Ge-wafer-based bolometric light detectors. In the CUPID design, the detector structure is novel and needs to be validated. In particular, the CUORE cryostat presents a high level of mechanical vibrations due to the use of pulse tubes and the effect of vibrations on the detector performance must be investigated. In this paper we report the first test of the CUPID-design bolometric light detectors with NTD-Ge sensors in a dilution refrigerator equipped with a pulse tube in an above-ground lab. Light detectors are characterized in terms of sensitivity, energy resolution, pulse time constants, and noise power spectrum. Despite the challenging noisy environment due to pulse-tube-induced vibrations, we demonstrate that all the four tested light detectors comply with the CUPID goal in terms of intrinsic energy resolution of 100 eV RMS baseline noise. Indeed, we have measured 70–90 eV RMS for the four devices, which show an excellent reproducibility. We have also obtained high energy resolutions at the 356 keV line from a 133 Ba source, as good as Ge semiconductor γ detectors in this energy range.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Monolithic Spatial Heterodyne Raman Spectrometers (mSHRS): Toward highly miniature chemical sensors for deployment on moving platforms (Final Report, Phase I)

This report covers phase 1 (year 1) of the mSHRS project. Our ultimate long-term goal is to develop highly miniature Raman analyzers, known as spatial heterodyne Raman spectrometers (SHRS), that are deployable as in-situ chemical sensors on moving platforms. During Phase I (FY19/20) funding, our team focused our efforts solely on the key innovation needed toward realizing that goal: the development of the monolithic interferometer comprising the beam splitter and gratings with the appropriate design parameters and tolerances to enable Raman interferometry. We successfully developed and characterized the performance of the first monolithic SHRS devices including a monolithic version that incorporates field-widened optics. Our solid-state design approach integrated all optical components as a single monolithic unit for maintaining precise optical alignment during sensor operation, a requirement for operating in a vibration prone environment. This project has two principle activities: (1) Design and fabricate monolithic SHRS devices (i.e. monolithic device task) and (2) characterize the mSHRS devices in terms of performance (i.e. the Monolithic SHRS characterization task).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

The Uniform Methods Project: Smart Thermostat Evaluation Protocol

A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., ±2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol ; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Zinc Oxide–Graphene Nanocomposite-Based Sensor for the Electrochemical Determination of Cetirizine

A nanocomposite electrode of graphene (Gr) and zinc oxide (ZnO) nanoparticles was fabricated to study the electrochemical oxidation behavior of an anti-inflammatory drug, i.e., cetirizine (CET). The voltametric response of CET for bare CPE, Gr/CPE, ZnO/CPE, and the ZnO-Gr nanocomposite electrode was studied. The modifier materials were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray powder diffraction (XRD) to comprehend the surface morphology of the utilized modifiers. The influence of pH, scan rate, and accumulation time on the electrooxidation of CET was examined. It was found that the electrochemical oxidation of CET was diffusion-controlled, in which two protons and two electrons participated. The detection limit was found to be 2.8 × 10 -8 M in a linearity range of 0.05–4.0 µM. Study of excipients was also performed, and it was found that they had negligible interference with the peak potential of CET. The validation and utility of the fabricated nanocomposite sensor material were examined by analyzing clinical and biological samples. Stability testing of the nanocomposite electrode was conducted to assess the reproducibility, determining that the developed biosensor has good stability and high efficiency in producing reproducible results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Q4FY20 Quarterly Report. LL20-AM-sensor

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Project is 13 months into the 30 month on complete project plan. We are continuing effort on 3 main technical thrusts: algorithm development, multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In FY20 we underspent the initial budget, partly due to work restrictions from Covid-19 pandemic, but are working to make up lost time

42 ENGINEERING↗

DOE OSTI2021

Q2FY21 Quarterly Report LL20-AM-sensor

42 ENGINEERING↗

DOE OSTI2021

Q3FY21 Quarterly Report LL20-AM-sensor

36 MATERIALS SCIENCE↗

DOE OSTI2026

Quantifying Operational Drivers of Multimodal Biometric Verification in Aerial Surveillance

Multimodal biometric verification is increasingly applied across operational contexts ranging from close-range security cameras and building-mounted surveillance to long-range ground sensors and unmanned aerial system (UAS) imagery. Variations in acquisition conditions—such as image resolution, viewing geometry, and motion artifacts—pose significant challenges for cross-domain algorithmic generalization. This study evaluates two independent multimodal biometric verification systems developed under the Intelligence Advanced Research Projects Activity (IARPA) Biometric Recognition and Identification at Altitude and Range (BRIAR) program, comparing performance on close-range and aerial datasets. Close-range video served as a baseline to quantify the decline in verification performance on aerial footage. The dataset included six UAS platforms, spanning small quadcopters at 10m altitude to medium-sized fixed-wing aircraft at 360m. Mixed-effects logistic regression identified image resolution (head and body pixel counts), head height, sensor characteristics, and algorithm selection as primary determinants of verification success, whereas demographic attributes and mission gait were not significant predictors. Activity type and collection site influenced performance in close-range data but had negligible impact on UAS imagery. These results clarify modality-specific strengths and limitations and highlight opportunities to enhance cross-domain biometric verification.

Peluso, Alina [ORNL] (ORCID:0000000328950406)↗

DOE OSTI2021

The EGS Collab Project: Status and Accomplishments

The EGS Collab project, supported by the US Department of Energy, is addressing challenges in implementing enhanced geothermal systems (EGS). This includes improving understanding of the stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The project is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory. Data and observations from the field test are compared to simulations to understand processes and to build confidence in numerical modeling of the processes. In Experiment 1, we examined hydraulic fracturing an underground test bed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km. We drilled eight sub-horizontal boreholes in a well-characterized phyllite. Six of the boreholes were instrumented with many sensor types to allow careful monitoring of stimulation events and flow tests, and the other two boreholes were used for water injection and production. We performed a number of stimulations and flow tests in the testbed. Our monitoring systems allowed detailed observations and collection of numerous data sets of processes occurring during stimulation and during dynamic flow tests. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Data were rapidly analyzed, allowing adaptive control of the tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results. Many simulations were performed in near-real-time in conjunction with the field experiments, with more detailed process study simulations performed on a longer timeframe. Experiment 2 will examine hydraulic shearing in a test bed being built at the SURF at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1. Five sets of fracture orientations were considered in design, and three orientations seem to be consistently observed.

EGS Collab↗

DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

A generalized machine learning workflow to visualize mechanical discontinuity

Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.

42 ENGINEERING↗

DOE OSTI2024

Reliable operation of Cr 2 O 3 :Mg/ $β$-Ga 2 O 3 p–n heterojunction diodes at 600 °C

Beta gallium oxide (β-Ga 2 O 3 )-based semiconductor heterojunctions have recently demonstrated improved performance at high voltages and elevated temperatures and are, thus, promising for applications in power electronic devices and harsh environment sensors. However, the long-term reliability of these ultra-wideband gap (UWBG) semiconductor devices remains barely addressed and may be strongly influenced by chemical reactions at the p–n heterojunction interface. Here, we experimentally demonstrate operation and evaluate the reliability of Cr 2 O 3 :Mg/β-Ga 2 O 3 p–n heterojunction diodes during extended operation at 600 °C, as well as after 30 repeated cycles between 25 and 550 °C. The calculated pO 2 -temperature phase stability diagram of the Ga-Cr-O material system predicts that Ga 2 O 3 and Cr 2 O 3 should remain thermodynamically stable in contact with each other over a wide range of oxygen pressures and operating temperatures. The fabricated Cr 2 O 3 :Mg/β-Ga 2 O 3 p–n heterojunction diodes show room-temperature on/off ratios >104 at ±5 V and a breakdown voltage (V Br ) of -390 V. The leakage current increases with increasing temperature up to 600 °C, which is attributed to Poole–Frenkel emission with a trap barrier height of 0.19 eV. Over the course of a 140-h thermal soak at 600 °C, both the device turn-on voltage and on-state resistance increase from 1.08 V and 5.34 mΩ cm 2 to 1.59 V and 7.1 mΩ cm 2 , respectively. This increase is attributed to the accumulation of Mg and MgO at the Cr 2 O 3 /Ga 2 O 3 interface as observed from the time-of-flight secondary ion mass spectrometry analysis. These findings inform future design strategies of UWBG semiconductor devices for harsh environment operation and underscore the need for further reliability assessments for β-Ga 2 O 3 -based devices.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data from the Development Evolution of a Vehicle for Custom Control

In order to develop custom controllers intended to operate vehicles on a live highway, a series of data collection-focused tests were performed at increasing stages of complexity. Modern vehicles with features like Adaptive Cruise Control (ACC) feature a rich set of sensors and drive-by-wire mechanisms. The presented stages of data collection begins with the analysis of raw data provided by various vehicles, and eventually results in spoofing Controller Area Network (CAN) protocols for sending control commands to operate a vehicle. This paper covers the data and technical efforts needed at various stages. The raw data and tools to plot the data are also publicly available.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Analysis of a Runtime Data Sharing Architecture over LTE for a Heterogeneous CAV Fleet

This paper describes a lightweight runtime architecture for telemetry, communication, and control of cars deployed with advanced driver assistance systems where a human is in the loop with the car, via an LTE connection. The system architecture supports both local control decisions based on car sensors and safety algorithms as well as high-level input from external systems that may provide insight into traffic state ahead of sensor data. Implementation of the architecture is done in ROS and depends on open-source software packages for runtime decoding of information from the vehicle’s controller area network (CAN) and integration of GPS data from accompanying sensors. The contribution of the paper is to describe the overall architecture, the data it can communicate to other systems, performance of the system at runtime, and challenges faced when deploying the architecture across a heterogeneous fleet. Preliminary results from analysis of test data will provide insights into whether the use of high-latency communication can be effective for societal-scale intelligent transportation systems when applied in future scenarios

Richardson, Alex↗

DOE OSTI2021

Accelerometer Modeling in the State-Space

Generically, accelerometers are sensors that measure the accelerating force applied to an object. Their operation is based on a simple mechanical system and D’Alembert’s force principle along with Newton’s laws of motion. This device is typically mounted to the body of the test object under investigation that is subjected to an accelerating force. The displacement of the seismic or “proof” mass due to the acceleration is sensed by an embedded component from which the acceleration is determined. Interestingly enough, the resulting the resulting mathematical relations governing the dynamical motion is based on the underlying physics, sensor materials, geometry and fabrication. Mathematically, the various designs satisfy the underlying Newtonian equations of motion, but their type and performance differ significantly.

42 ENGINEERING↗

DOE OSTI2024

Beam test results for the Endcap Timing ReadOut Chip (ETROC2) for the CMS ETL upgrade

The Endcap Timing ReadOut Chip (ETROC) for the Compact Muon Solenoid (CMS) Minimum ionizing particle Timing Detector (MTD) is designed to process Low Gain Avalanche Diode (LGAD) signals with a time resolution of 40-50ps per hit, with 30-35 ps per charged particle using two layer detector. The ETROC2, is the first full size (16x16 pixels) and full functionally design that has been tested in hadron and electron beams at CERN and DESY (Sep 2023 - June 2024). Precision timing studies at these test beams are done using a self-referential beam telescope composed of up to 4 layers/chips. This talk describes the ETROC2 beam telescope and summarizes the performance of the ETROC2 chips bump-bonded to (16x16) Fondazione Bruno Kessler (FBK) and Hamamatsu Photonics K.K. (HPK) LGAD sensors using results from recent test beams. The experiences and lessons learned during the course of these test beams are also detailed in this talk.

Safdari, Murtaza↗