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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 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Inter-digital capacitive sensor for evaluating cable jacket and insulation aging

An inter-digital capacitive (TDC) sensor has previously been used to measure dielectric properties of cable insulation polymer material when placed in direct contact with the insulation. Often cable insulation is covered by a polymer jacket. The dielectric properties of many cable jacket and insulation polymers are known to change due to thermal and radiation exposure-related damage. These dielectric properties frequently track with other measures of cable aging, such as tensile elongation-at-break and indenter modulus that have been broadly established as cable insulation polymer assessment methods. The external jacket of a cable is likely to have a different permittivity from the underlying insulation, and frequently the jacket material exhibits more severe damage than the insulation material due to environmental exposure. Because the jacket serves primarily to guard the cable during installation, as long as the underlying insulation condition is acceptable, the jacket condition is relatively unimportant in service. As part of a continuing program to develop and evaluate nondestructive examination methods that may be applied to cable condition assessment, a set of tools has been developed including (1) a parallel-plate sensor to directly measure the permittivity spectrum of flat sheet material and (2) an TDC and fixture to measure the effect of cable polymer dielectric property change on the sensor response. The TDC consists of two fork-like electrodes facing each other with the fork tines interspersed and separated by a small gap. The electrodes are printed on one side of a flexible substrate that can be conformed to the surface of a cylindrical cable, with tines parallel to the cable axis. The electrodes are connected to a broad-frequency-spectral impedance meter that senses the capacitance between the narrowly gapped electrode tines. This capacitance is known to vary as a function of the permittivity of any material in close proximity to the electrodes. By finite element modeling (FEM) and experimentation, this study investigates the effect of tine spacing and other design parameters associated with the TDC on the voltage (potential) distribution and electric field depth of penetration. The TDC measurement of an unshielded ethylene-propylene rubber (EPR)-insulated cable is shown to track with the degree of aging and quantities obtained by established methods. For jacketed cable systems, the TDC response is dominated by the jacket but, by analyzing measurements from TDC sensors with different depths-of-field penetration into the cable under test, the influence of the chlorinated polyethylene (CPE) cable jacket material degradation can be separated from an assessment of the cable insulation thereby enabling assessment of the insulation beneath/through the jacket.

Cable NDE, Interdigital Capacitance, Cable test↗
DOE OSTI2019

Integrated Hydrogen Sensor and Separator for Parabolic Trough Expansion Tanks

The National Renewable Energy Laboratory (NREL) and Acciona Energy USA Global are developing and implementing a process that addresses the issue of hydrogen buildup in parabolic trough power plants. Our method selectively removes hydrogen from the expansion tanks of the power plant to control hydrogen levels in the circulating heat-transfer fluid (HTF). As part of this effort, we developed a sensor that measures hydrogen partial pressure in the expansion-tank headspace gas. During previous work, we demonstrated that our sensor measures hydrogen levels over a wide range of partial pressure from 1.33 mbar down to 0.003 mbar. In this paper, we report our most recent work, in which we conceived and developed an integrated process module that performs both hydrogen sensing and separating functions. The sensor/separator measures hydrogen partial pressure in the headspace gas in the same way as our original sensor design. Additionally, the integrated module separates hydrogen from the headspace gas to reduce hydrogen to the level needed to maintain the performance of receivers in the collector field. Sensor data from the integrated module also provide an estimate of the rate at which hydrogen is being extracted from the headspace gas. We designed and fabricated a laboratory-scale integrated module and determined its performance in our laboratory at NREL. Testing showed that the module performed as expected in both sensing and separating modes. The accuracy of the sensing function was +/-10%. The hydrogen extraction rate for the separating mode was consistent with our modeling predictions. The primary benefit of this module is its simple design, both in terms of function and incorporation into the HTF subsystem of the power plant. The integrated module can be installed close to the HTF expansion tanks, where it draws headspace gas from the tanks, removes hydrogen, and returns the treated gas back to the tanks. We are currently installing a test-scale process at the Nevada Solar One (NSO) power plant to demonstrate this process and determine its operational performance. Results from the test-scale process will be used to specify and design the full-scale process that will be installed at NSO.

14 SOLAR ENERGY↗
DOE OSTI2026

Sensor Reduction for Diversion Detection in a Realistic Heat Pipe Microreactor Using Supervised Machine Learning

Microreactors are designed as a smaller, cheaper, and safer alternative to traditional nuclear power plants. Their non-traditional characteristics and prospect of mass production and deployment will likely require new approaches to nuclear safeguards. The primary proliferation concern with microreactors is the diversion of fuel material. Such diversion may produce measurable defects in key physical attributes like neutron flux, which may in turn be detectable using machine learning models. Preliminary work has demonstrated this ability for modeled nominal and diversion scenarios using large quantities of energy integrated neutron flux data. In practice, the number of available sensors for such measurements will be limited and energy integrated flux information will not be available. This work explores the ability of tree-based gradient boosted ensemble models to classify a given microreactor core is nominal or diversion, and determine the number of fuel pins diverted in the case of diversion with reduced numbers of sensors and more realistic detector responses. Classification accuracy of greater than 98% and regression errors as low as 5% of the total number of fuel pins were achieved with as few as 15 sensors, compared to 99% and 4.1% with a maximum of 240 sensors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Restoring the discontinuous heat equation source using sparse boundary data and dynamic sensors

Abstract This study focuses on addressing the inverse source problem associated with the parabolic equation. We rely on sparse boundary flux data as our measurements, which are acquired from a restricted section of the boundary. While it has been established that utilizing sparse boundary flux data can enable source recovery, the presence of a limited number of observation sensors poses a challenge for accurately tracing the inverse quantity of interest. To overcome this limitation, we introduce a sampling algorithm grounded in Langevin dynamics that incorporates dynamic sensors to capture the flux information. Furthermore, we propose and discuss two distinct dynamic sensor migration strategies. Remarkably, our findings demonstrate that even with only two observation sensors at our disposal, it remains feasible to successfully reconstruct the high-dimensional unknown parameters.

Mathematics↗
DOE OSTI2020

Enhancing ZnO nanowire gas sensors using Au/Fe 2 O 3 hybrid nanoparticle decoration

Heterojunctions are an important strategy for designing high performance electrical sensor materials and related devices. Herein, a new type of metal-semiconductor hybrid nanoparticle has been successfully used to remarkably sensitize the surface of ZnO nanowires for detecting NO 2 with high responses over a broad temperature window ranging from room temperature to 600 °C. These hybrid nanoparticles are comprised of iron oxide nanowires with well dispersed single crystalline Au nanoparticles. The hybrid nanoparticle decorated ZnO nanowires have achieved a giant response, as high as 74 500 toward NO 2 gas, about 42 times that of Au decorated ZnO nanowire sensors. This dramatic enhancement may be attributed to the efficient charge transfer across the Au–Fe 2 O 3 Schottky and Fe 2 O 3 –ZnO semiconductor heterojunction interfaces. Due to the incorporation of thermally-stable Fe 2 O 3 nanoparticles as the support of Au nanoparticles, the working temperature of nanowire sensors was successfully extended to higher temperatures, with an increase of 200 °C, from 400 °C to 600 °C. Such a combination of semiconductor heterojunction and semiconductor-metal Schottky contact presents a new strategy for designing high performance electrical sensors with high sensitivity, stability, selectivity, and wide operation temperature window, which are potentially suitable for advanced energy systems such as automotive engines and power plants.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2016

Trapping in irradiated p + -n-n - silicon sensors at fluences anticipated at the HL-LHC outer tracker

The degradation of signal in silicon sensors is studied under conditions expected at the CERN High-Luminosity LHC. 200μm thick n-type silicon sensors are irradiated with protons of different energies to fluences of up to 3 x 10 15 neq/cm 2 . Pulsed red laser light with a wavelength of 672 nm is used to generate electron-hole pairs in the sensors. The induced signals are used to determine the charge collection efficiencies separately for electrons and holes drifting through the sensor. The effective trapping rates are extracted by comparing the results to simulation. The electric field is simulated using Synopsys device simulation assuming two effective defects. The generation and drift of charge carriers are simulated in an independent simulation based on PixelAV. The effective trapping rates are determined from the measured charge collection efficiencies and the simulated and measured time-resolved current pulses are compared. Furthermore, the effective trapping rates determined for both electrons and holes are about 50% smaller than those obtained using standard extrapolations of studies at low fluences and suggests an improved tracker performance over initial expectations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Properties of tree rings in LSST sensors

Images of uniformly illuminated sensors for the Large Synoptic Survey Telescope have circular periodic patterns with an appearance similar to tree rings. Furthermore, these patterns are caused by circularly symmetric variations of the dopant concentration in the monocrystal silicon boule induced by the manufacturing process. Non-uniform charge density results in the parasitic electric field inside the silicon sensor, which may distort shapes of astronomical sources. Here, we analyzed data from fifteen LSST sensors produced by ITL to determine the main parameters of the tree rings: amplitude and period, and also variability across the sensors tested at Brookhaven National Laboratory. Tree ring pattern has a weak dependence on the wavelength. But the ring amplitude gets smaller as wavelength gets longer, since longer wavelengths penetrate deeper into the silicon. Tree ring amplitude gets larger as it gets closer to the outer part of the wafer, from 0.1 to 1.0%, indicating that the resistivity variation is larger for larger radii.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Test beam measurement of ams H35 HV-CMOS capacitively coupled pixel sensor prototypes with high-resistivity substrate

In the context of the studies of the ATLAS High Luminosity LHC programme, radiation tolerant pixel detectors in CMOS technologies are investigated. To evaluate the effects of substrate resistivity on CMOS sensor performance, the H35DEMO demonstrator, containing different diode and amplifier designs, was produced in ams H35 HV-CMOS technology using four different substrate resistivities spanning from 80 cm to 1000 cm. A glueing process using a high-precision flip-chip machine was developed in order to capacitively couple the sensors to FE-I4 Readout ASIC using a thin layer of epoxy glue with good uniformity over a large surface. The resulting assemblies were measured in beam test at the Fermilab Test Beam Facilities with 120 GeV protons and CERN SPS H8 beamline using 180 GeV pions. Furthermore, the in-time efficiency and tracking properties measured for the different sensor types are shown to be compatible with the ATLAS ITk requirements for its pixel sensors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Experimental study of different silicon sensor options for the upgrade of the CMS Outer Tracker

During the high-luminosity phase of the LHC (HL-LHC), planned to start in 2027, the accelerator is expected to deliver an instantaneous peak luminosity of up to 7.5×10 34 cm –2 s –1 . A total integrated luminosity of 3000 or even 4000 fb –1 is foreseen to be delivered to the general purpose detectors ATLAS and CMS over a decade, thereby increasing the discovery potential of the LHC experiments significantly. The CMS detector will undergo a major upgrade for the HL-LHC,with entirely new tracking detectors consisting of an Outer Tracker and Inner Tracker. However, the new tracking system will be exposed to a significantly higher radiation than the current tracker, requiring new radiation-hard sensors. CMS initiated an extensive irradiation and measurement campaign starting in 2009 to systematically compare the properties of different silicon materials and design choices for the Outer Tracker sensors. Several test structures and sensors were designed and implemented on 18 different combinations of wafer materials, thicknesses, and production technologies. The devices were electrically characterized before and after irradiation with neutrons,and with protons of different energies, with fluences corresponding to those expected at different radii of the CMS Outer Tracker after 3000 fb –1 . The tests performed include studies with β sources, lasers, and beam scans. This paper compares the performance of different options for the HL-LHC silicon sensors with a focus on silicon bulk material and thickness.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Performance of a Front End prototype ASIC for picosecond precision time measurements with LGAD sensors

For the High-Luminosity phase of LHC, the ATLAS experiment is proposing the addition of a High Granularity Timing Detector (HGTD) in the forward region, to mitigate the effects of the increased pile-up. The chosen detection technology is Low Gain Avalanche Detector (LGAD) silicon sensors that can provide an excellent timing resolution below 50 ps. The front-end read-out ASIC must exploit the large signal derivative and small noise provided by the sensor, while keeping low power consumption. This paper presents the results on the first prototype of a front-end ASIC, named ALTIROC0, which contains the analog stages (preamplifier and discriminator) of the read-out chip. The ASIC was characterised both alone and as part of a module with a 2×2 LGAD array of 1.1×1.1 mm 2 pads bump-bonded to it. The various contributions of the electronics to the time resolution were investigated in test-bench measurements with a calibration setup. Both when the ASIC is alone or with a bump-bonded sensor, the jitter of the ASIC is better than 20 ps for an injected charge of 10 fC. The time walk effect, which arises from the different preamplifier response for various injected charges, can be corrected up to 10 ps using a Time Over Threshold measurement. The combined performance of the ASIC and the LGAD sensor, which was measured during a beam test campaign in October 2018 with pions of 120 GeV energy at the CERN SPS, is around 40 ps for all measured modules. All tested modules show good efficiency and time resolution uniformity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

200 mm sensor development using bonded wafers

Sensors fabricated from high resistivity, float zone, silicon material have been the basis of vertex detectors and trackers for the last 30 years. The areas of these devices have increased from a few square cm to > 200m 2 for the existing CMS tracker. High Luminosity Large Hadron Collider (HL-LHC), CMS and ATLAS tracker upgrades will each require more than 200 m 2 of silicon and the CMS High Granularity Calorimeter (HGCAL) will require more than 600 m 2 . The cost and complexity of assembly of these devices is related to the area of each module, which in turn is set by the size of the silicon sensors. In addition to large area, the devices must be radiation hard, which requires the use of sensors thinned to 200 microns or less. The combination of wafer thinning and large wafer diameter is a significant technical challenge, and is the subject of this work. In this paper we describe work on development of thin sensors on 200 mm wafers using wafer bonding technology. Results of development runs with float zone, Silicon-on-Insulator and Silicon-Silicon bonded wafer technologies are reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Neutron irradiation and electrical characterisation of the first 8” silicon pad sensor prototypes for the CMS calorimeter endcap upgrade

As part of its HL-LHC upgrade program, the CMS collaboration is replacing its existing endcap calorimeters with a high-granularity calorimeter (CE). The new calorimeter is a sampling calorimeter with unprecedented transverse and longitudinal readout for both electromagnetic and hadronic compartments. Due to its compactness, intrinsic time resolution, and radiation hardness, silicon has been chosen as active material for the regions exposed to higher radiation levels. The silicon sensors are fabricated as 20 cm (8”) wide hexagonal wafers and are segmented into several hundred pads which are read out individually. As part of the sensor qualification strategy, 8” sensor irradiation with neutrons has been conducted at the Rhode Island Nuclear Science Center (RINSC) and followed by their electrical characterisation in 2020-21. The completion of this important milestone in the CE's R&D program is documented in this paper and it provides detailed account of the associated infrastructure and procedures.The results on the electrical properties of the irradiated CE silicon sensors are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Superconducting quasiparticle-amplifying transmon: A qubit-based sensor for meV-scale phonons and single terahertz photons

With great interest from the quantum computing community, an immense amount of R&D effort has been invested into improving superconducting qubits. The technologies developed for the design and fabrication of these qubits can be directly applied to applications for ultralow-threshold particle detectors, e.g., low-mass dark matter and far-infrared photon sensing. We propose a novel energy-resolving sensor based on the transmon qubit architecture combined with a signal-enhancing superconducting quasiparticle amplification stage. We refer to these sensors as SQUATs: superconducting quasiparticle-amplifying transmons. We detail the operating principle and design of this new sensor and predict that, with minimal R&D effort, solid-state-based detectors patterned with these sensors can achieve sensitivity to single terahertz photons, and sensitivity to 1 meV phonons in the detector absorber substrate on the microsecond timescale. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Multiparameter Estimation in Networked Quantum Sensors

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Kensor: Coordinated Intelligence from Co-Located Sensors

Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically colocated in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.

Kotevska, Olivera↗
DOE OSTI2016

Parts Per Million CH4 Chemoresistor Sensors Based on multi wall Carbon Nanotubes/Metal-oxide Nanoparticles

This paper presents a novel atomic layer deposition (ALD) based ZnO functionalization of surface pretreated multiwalled carbon nanotubes (MWCNTs) for highly sensitive methane chemoresistive sensors. The temperature optimization of the ALD process leads to enhanced ZnO nanoparticle functionalization and improvement in their crystalline quality as shown by transmission electron microscopy. Here we propose a facile and effective top-down method for the high-yield fabrication of chemoresistor sensors making use of MWCNTs/ZnO composite. The behavior of MWCNTs/ZnO sensors in presence of methane concentrations down to 10 ppm level has been investigated. The sensor is currently being tested under a range of conditions that include potentially interfering gases and changes to relative humidity.

chemoresistor, carbon nanotubes, metal-oxide- nano↗
DOE OSTI2023

The Distributed Grid Sensing Services (DGSS) platform: A scalable and decentralized sensor dissemination network

In this paper, a scalable, decentralized sensor-oriented, communication blueprint will be introduced. The proposed architecture has been specifically designed to support the mass deployment of sensors while enabling data consumers to access the underlying data streams using a distributed systems approach. With the proposed approach, sensors and data clients can be decoupled from the physical communication infrastructure and migrated into a modern, software-defined infrastructure ecosystem that can be configured to suit the end application’s demands. In its current iteration, the blueprint defines the interactions, processes, and expected outcomes that each individual component within the architecture must fulfill in order to support the overall ecosystem’s needs. Within this work, such assemble of services is referred the databus, and represents the heart of the Distributed Grid Sensing Services (DGSS) architecture, a multi-year project that aims to design, implement and test a dedicated sensor dissemination network that can support the needs of the extended grid state. It is our expectation, that the proposed blueprint presented in this paper will serve as a future guide to our implementation efforts

Sebastian Cardenas, David J.↗