SEARCH · Engineering Papers

Results for “Sensor performance”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Performance of a phonon-mediated kinetic inductance detector at the NEXUS cryogenic facility

Microcalorimeters that leverage microwave kinetic inductance detectors to read out phonon signals in the particle-absorbing target, referred to as kinetic inductance phonon-mediated (KIPM) detectors, offer an attractive detector architecture to probe dark matter (DM) down to the fermionic thermal relic mass limit. A prototype KIPM detector featuring a single aluminum resonator patterned onto a 1-gram silicon substrate was operated in the Northwestern EXerimental Underground Site (NEXUS) low-background facility at Fermilab for characterization and evaluation of this detector architecture’s efficacy for a DM search. An energy calibration was performed by exposing the bare substrate to a pulsed source of 470-nm photons, resulting in a baseline resolution on the energy absorbed by the phonon sensor of 2.1 ± 0.2 eV, a factor of two better than the current state of the art, enabled by quasiparticle lifetimes extending up to 6.5 ms. However, due to the subpercent phonon collection efficiency, the resolution on energy deposited in the substrate is limited to σ E = 318 ± 29 eV. We further model both the signal pulse shape as a function of device temperature to extract quasiparticle lifetimes, and the observed noise spectra.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Abstract for CRADA between NETL and Hope Gas, Inc.

The National Energy Technology Laboratory (NETL) and Hope Gas (PARTICIPANT) will collaborate in field demonstrations of NETL-developed methane quantification and mitigation technologies, including pipeline sensors and pipeline protection materials to mitigate methane emissions. Hope Gas will also provide access to gathering pipelines and other facilities for an NETL team to perform methane emission field survey.

03 NATURAL GAS↗

DOE OSTI2023

Test Flights of NOAA Ozone Photometer for Uncrewed Aerial Systems (UAS)/Balloon-Based Studies (OPUS) during TRACER (Field Campaign Report)

We proposed to integrate and fly the National Oceanic and Atmospheric Administration (NOAA) Ozone Photometer for Uncrewed Aerial Systems (UAS)/Balloon-Based Studies (OPUS) on the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s tethered balloon system (TBS) during the second phase (August 1-15) of the 2022 Tracking Aerosol Convection Interactions Experiment (TRACER) in southwest Houston, Texas. The two main goals were: 1) Test fly the sensor to discover and address any potential problems such as interface and communication with other instruments, interferences with other instruments and radio, and 2) If OPUS performs well, then inter-compare OPUS with the EN-SCI ozonesondes that will also be flown on the TBS. The collaborating institutions are DOE Sandia National Laboratories TBS team, the University of Houston, and St. Edward's University.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Resilient Autonomous Wind Farms

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm level, taking into account individual wind turbine information to achieve wind-farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have experienced significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms that can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as from local meteorological stations, nearby radars, sodars, and lidars, and supervisory control and data acquisition (SCADA) data. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and ensuring more efficient overall control of farmwide optimizations through mechanisms such as wake steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach that distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks that seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft-Byzantine-Fault-Tolerant algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will...

fault tolerance↗

DOE OSTI2025

Vehicular Re-Identification from Uncontrolled Multiple Views

Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.

convolutional neural networks↗

DOE OSTI2021

Additive Manufacturing of Miniaturized Peak Temperature Monitors for In-Pile Applications

Passive monitoring techniques have been used for peak temperature measurements during irradiation tests by exploiting the melting point of well-characterized materials. Recent efforts to expand the capabilities of such peak temperature detection instrumentation include the development and testing of additively manufactured (AM) melt wires. In an effort to demonstrate and benchmark the performance and reliability of AM melt wires, we conducted a study to compare prototypical standard melt wires to an AM melt wire capsule, composed of printed aluminum, zinc, and tin melt wires. The lowest melting-point material used was Sn, with a melting point of approximately 230 °C, Zn melts at approximately 420 °C, and the high melting-point material was aluminum, with an approximate melting point of 660 °C. Through differential scanning calorimetry and furnace testing we show that the performance of our AM melt wire capsule was consistent with that of the standard melt-wire capsule, highlighting a path towards miniaturized peak-temperature sensors for in-pile sensor applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗

DOE OSTI2021

Optimization Of In-field Alpha Spectrometry For Uranium Enrichment Determination In Uranium Hexafluoride

In response to needs identified by the International Atomic Energy Agency (IAEA) research is underway to develop In-Field Alpha Spectrometry (IFAS) as a method to allow IAEA safeguards inspectors to collect samples of uranium hexafluoride (UF6) at processing facilities to assess and verify uranium enrichment. For sample collection, the IFAS method uses Single-Use Destructive Assay (SUDA) samplers, which contain thin zeolite coatings that trap UF6 gas and convert it to the safer, more stable form uranyl fluoride (as a dihydrate, UO2F2·2H2O). For alpha spectrometry, the IFAS instrument employs a large area silicon semiconductor transducer to detect and record alpha particle energy-deposition events. Over the past year optimization work has significantly increased the diameter of useful SUDA samples (from 12.7 mm to 48 mm), improved the manufacturability and reproducibility of SUDA samples, increased the area of the IFAS alpha spectrometer sensor from 1.2 cm to 3.1 cm, and improved source positioning within the IFAS. This paper will report on this optimization work, its impacts on IFAS performance, and future plans for IFAS miniaturization, improvements, and testing.

Chichester, David↗

DOE OSTI2021

Optimization of In-Field Alpha Spectrometry for Uranium Enrichment Determination in Uranium Hexafluoride

In response to needs identified by the International Atomic Energy Agency (IAEA) research is underway to develop In-Field Alpha Spectrometry (IFAS) as a method to allow IAEA safeguards inspectors to collect samples of uranium hexafluoride (UF6) at processing facilities to assess and verify uranium enrichment. For sample collection, the IFAS method uses Single-Use Destructive Assay (SUDA) samplers, which contain thin zeolite coatings that trap UF6 gas and convert it to the safer, more stable form uranyl fluoride (UO2F2). For alpha spectrometry, the IFAS instrument employs a large area silicon semiconductor transducer to detect and record alpha particle energy-deposition events. Over the past year optimization work has significantly increased the diameter of useful SUDA samples (from 12.7 mm to 48 mm), improved the manufacturability and reproducibility of SUDA samples, increased the area of the IFAS alpha spectrometer sensor from 1.2 cm to 3.1 cm, and improved source positioning within the IFAS. This paper will report on this optimization work, its impacts on IFAS performance, and future plans for IFAS miniaturization, improvements, and testing.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2020

Alignment of the ATLAS Inner Detector in Run 2

The performance of the ATLAS Inner Detector alignment has been studied using pp collision data at s√=13TeV collected by the ATLAS experiment during Run 2 (2015–2018) of the Large Hadron Collider (LHC). The goal of the detector alignment is to determine the detector geometry as accurately as possible and correct for time-dependent movements. The Inner Detector alignment is based on the minimization of track-hit residuals in a sequence of hierarchical levels, from global mechanical assembly structures to local sensors. Subsequent levels have increasing numbers of degrees of freedom; in total there are almost 750,000. The alignment determines detector geometry on both short and long timescales, where short timescales describe movements within an LHC fill. The performance and possible track parameter biases originating from systematic detector deformations are evaluated. Momentum biases are studied using resonances decaying to muons or to electrons. The residual sagitta bias and momentum scale bias after alignment are reduced to less than ~0.1 TeV -1 and 0.9×10 -3 , respectively. Impact parameter biases are also evaluated using tracks within jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Attack Surface Analysis of the Digital Twins interface with Advanced Sensor and Instrumentation Interfaces: Cyber Threat Assessment and Attack Demonstration for Digital Twins in Advanced Reactor Architectures

A digital twin is a virtual representation of a physical system or object using real-time data that can predict and analyze how the system or object performs. This relatively new technology can be applied to the field of nuclear power generation, to aid in the design and development of new nuclear power plants and reduce operation costs using predictive maintenance and other data analytical methods. While there are already companies utilizing simulation software to train operators and technicians in the nuclear industry, some are now transitioning to utilizing their existing technology, software, and methods to develop digital twin solutions for the next generation of nuclear power plants, offering their services to utilities and government organizations around the world.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Thermocouple Testing in Support of the AGR-5/6/7 Experiment

This report documents thermocouple testing performed in IRC Lab C-15 over a period of seven years. This testing supported selection and characterization of the thermocouple set used in the AGR-5/6/7 experiment. The following summary was taken directly from the report. Temperature measurement is a challenging aspect of very high temperature irradiation experiments because commonly used high-temperature commercial thermocouples such as platinum-rhodium (Types S, R, and B) and tungsten-rhenium (Type C), suffer dramatic drift because of neutron-induced transmutation. As a result, these types of thermocouples, which are used routinely for industrial temperature measurements outside of reactors, are used only in very special circumstances for reactor experiments. Conversely, because of their low neutron cross-sections, Type N thermocouples are affected to only a limited extent by neutron irradiation. However, the use of these nickel-based thermocouples is limited when the temperature exceeds 1050°C due to drift arising from minor alloying elements migrating from the thermocouple's metal sheath to the thermoelements. This change in the composition of the thermo-elements results in significant decalibration of the signal. The issues described above were recognized during the early planning stages of the final AGR experiment (designated AGR-5/6/7), and a thermocouple furnace testing program was performed over a seven-year period (2014-2019, 2021) to first select and then characterize the best thermocouple set for the high temperature regions of the AGR-5/6/7 irradiation experiment. The calculated temperature range of the AGR-5/6/7 experiment was 600–1500°C. For temperatures below 1000°C standard Type N thermocouples were deemed adequate. The furnace testing campaign identified two thermocouple types suitable for measuring temperatures above 1000°C, a Mo/Nb thermocouple developed at INL called HTIR-TC, and a Type N thermocouple developed by Cambridge University (called herein Cambridge Type N), which featured a custom high nickel alloy sheath. One of the original goals of the furnace testing program was to identify a thermocouple capable of low drift operation near the peak temperature expected in AGR-5/6/7, i.e., about 1400°C. The HTIR-TC design appeared promising in this regard, however a manufacturing difficulty proved to be a barrier and instead the furnace testing focused on drift performance at 1250°C. The manufacturing difficulty was that the Nb sheaths of the HTIR-TCs experienced extreme embrittlement when heat treated at 1600°C or greater. Heat treatment is needed to stabilize the emf output of this TC type, and the higher the heat treatment temperature the higher the peak temperature of stable operation. Because of the sheath embrittlement the heat treatment temperature had to be lowered to 1450°C resulting in a stable operating temperature of about 1250°C. One of the successes of the furnace testing program was identification of a shortcoming in the heat treatment procedure that had been traditionally used in the production of HTIR-TCs. The shortcoming was that the entire heated length of the HTIR-TC sensor was not being heat treated, but rather only the part of the sensor expected to experience temperatures above 1000°C. The problem manifested itself when the thermocouples were removed from the heat treat furnace and placed in another furnace with a different geometry, their indicated temperatures would be widely scattered, but mostly in the negative direction. The solution was to heat treat the entire heated length of the sensor. Since the deepest immersion depth in the AGR-5/6/7 experiment was about 40 inches, a heat treatment length of 48 inches was used. After this change was implemented, thermocouples which were moved into a new environment with a different temperature profile (i.e., a different furnace), produced accurate temperature measurements. Although assembly of the AGR-5/6/7 experiment was completed in September of 2017 (and irradiation begun in 2018), furnace testing of thermocouples continued in 2018 and 2019. The main purpose of this testing was to establish very long-term drift characteristics of the HTIR and Cambridge Type N thermocouples installed in the experiment. Representative thermocouples from the same lots as those installed in the AGR-5/6/7 experiment were used. Additionally, thermocouples of different designs, (particularly variations on the HTIR-TC design) were "piggy-backed" on this testing program to provide insights for instrumenting future very high temperature irradiation experiments. This two-year testing program demonstrated that HTIR-TCs and Cambridge Type N TCs could operate at 1250°C for up to 10,000 hrs (and in some cases longer) while experiencing negative drifts on the order of 2-4°C/1000 hrs. This performance was considered acceptable given the extreme operating environment the sensors faced.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗

DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning

High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Ultrafast Annealing Improves SNR and Long-Term Stability of a Highly Multiplexed Line-by-Line FBG Array Inscribed by Femtosecond Laser in a Coreless Fiber for Extreme-Temperature Applications

This study reports the fabrication of a fourth-order line-by-line fiber Bragg grating (FBG) array using femtosecond laser inscription within a highly multimode coreless optical fiber, with a particular focus on achieving substantial multiplexing capabilities. An ultrafast annealing procedure is employed, resulting in an impressive enhancement of the FBG sensor’s fringe visibility by approximately 13 dB, signifying a notable improvement of approximately ~4 dB. This substantial enhancement contributes to the long-term stability and performance of the multiplexed FBG array in extreme-temperature conditions. The systematic fabrication approach employed for the multiplexed FBG array guarantees a high signal-to-noise ratio (SNR) for each individual FBG within the array. This FBG array is intended for extreme-temperature applications, addressing limitations associated with traditional FBGs based on doped optical fibers, including SNR degradation and temperature-induced fringe drift. Testing at temperatures up to 1120 °C demonstrates the FBG array’s stability without fluctuations in readings. Furthermore, it endures seven heat cycles, spanning from 500 °C to 1120 °C, over 60 h, exhibiting outstanding thermal stability. Furthermore, this highly multiplexed FBG array with an ultrafast annealing approach holds promise for extreme-temperature environments, such as steelmaking, where precise and reliable distributed temperature monitoring is imperative.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

New Mexico Small Business Assistance Program: Management Sciences Inc. (Final Report)

Sandia National Laboratories will, as time and budget allow, perform the following tasks as part of a New Mexico Small Business Assistance (NMSBA) Program project for Management Sciences, Inc. (MSI): 1. Set up a thermal radar in Sandia's Technical Area (TA) III test bed. 2. Collect alarm data from the thermal radar and a radio frequency (RF) radar during simulated intrusion tests to estimate detection performance, based on the Department of Energy (DOE) threat definition. 3. Collect nuisance alarm data caused by weather and non-intruder-related stimuli, to estimate nuisance alarm rate performance. 4. Provide data to the requester, allowing them to process the data. 5. Provide the requester, as a stretch goal, with live radar and thermal radar sensor feeds, allowing real-time processing of the RF radar and the thermal radar. A key technical issue that will influence the success of this activity is the range accuracy of the thermal radar. If issues are encountered, Sandia will work with the requester to correct the range issues.

42 ENGINEERING↗