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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Influence of Joule Heating on the Stability of High Temperature Irradiation Resistant Thermocouples

Development of in-core instrumentation is driven by the pursuit of safer and more economic energy production from both existing nuclear reactors and Generation IV reactor designs. Idaho National Laboratory (INL) has developed high temperature irradiation resistant thermocouples (HTIR-TCs) for temperature sensing inside Generation IV nuclear reactors. These thermocouples are composed of phosphorus-doped niobium (Nb-P) and lanthana-doped molybdenum (Mo-LaO) thermoelements, an alumina (Al2O3) insulation, and a niobium sheath. HTIR-TCs require an initial heat treatment exceeding the maximum service temperature to stabilize the generated electromotive force (EMF) signal; however, the mechanism behind this stabilization is not well understood. This work evaluates the impact of Joule heating on the thermoelements' microstructures, chemical stability, and mechanical properties to determine the mechanisms by which the EMF signal stabilization occurs. Accordingly, during the Joule heat treatment, a secondary Nb3P phase coarsened along the length of the Nb-P thermoelement, along with the formation of an interaction region at the Al2O3/niobium interface. The interaction between the alumina insulation and the Nb-P thermoelement was also observed within the Mo-LaO thermoelement. Joule heating induced stability within the generated EMF signal of HTIR-TCs through the formation of secondary phases within the Nb-P and the interaction of the alumina insulation with the thermoelements.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Fast Data Processing for Hyperspectral Sensors on Small Platforms

Hyperspectral imaging is a very promising technology for nuclear proliferation detection. However, due to size and weight restrictions, small hyperspectral platforms such as satellites and small drones lack the on-board computing resources for accurate, real-time analysis of the enormous flow of data that a continuously operating hyperspectral sensor generates. This severely limits satellite systems, which can collect far more data than what they can telemeter, and hinders the ability of all platforms to adapt their missions on the fly in response to observations. This program addresses the hyperspectral data processing challenge through development of new, fast and accurate algorithms that produce data products in real time. The algorithms circumvent the major computational bottlenecks in existing processing streams, and would be incorporated in lightweight, power-efficient single-board computer systems. The toolkit of fast algorithms will be immediately useful in current and future hyperspectral systems being built by the Government and by private industry, including drone-based systems and satellite constellations that acquire timely global imagery.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Design and evaluation of large area strip sensor prototypes for the ATLAS Inner Tracker detector

The ATLAS community is facing the last stages prior to the production of the upgraded silicon strip Inner Tracker for the High-Luminosity Large Hadron Collider. An extensive Market Survey was carried out in order to evaluate the capability of different foundries to fabricate large area silicon strip sensors, satisfying the ATLAS specifications. The semiconductor manufacturing company, Infineon Technologies AG, was one of the two foundries, along with Hamamatsu Photonics K.K., that reached the last stage of the evaluation for the production of the new devices. The full prototype wafer layout for the participation of Infineon, called ATLAS17LS-IFX, was designed using a newly developed Python-based Automatic Layout Generation Tool, able to rapidly design sensors with different characteristics and dimensions based on a few geometrical and technological input parameters. Here we present the layout design process and the results obtained from the evaluation of the new Infineon large area sensors before and after proton and neutron irradiations, up to fluences expected in the inner layers of the future ATLAS detector.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Compact instantaneous phase-shifting Sagnac interferometer for nanoscale tilt measurement

There is a large inter disciplinary need for high precision angular sensors in state-of-the-art technologies. This particularly applies to optical metrology in the field of Extreme Ultraviolet (EUV) or X-ray where the optics are commonly characterized in terms of slope errors at a nanoradian scale level. Here, in this paper, we present the in-lab development of an angular sensor based on a single reflection compact Sagnac interferometer using instantaneous phase shifting. Our automated prototype and first results show less than 15 nrad RMS repeatability, at least 200 nrad sensitivity and 280 nrad reproducibility over 100 μrad angular range.

36 MATERIALS SCIENCE↗
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 OSTI2020

Overview of CMOS Sensors for Future Tracking Detectors

Depleted Complementary Metal-Oxide-Semiconductor (CMOS) sensors are emerging as one of the main candidate technologies for future tracking detectors in high luminosity colliders. Their capability of integrating the sensing diode into the CMOS wafer hosting the front-end electronics allows for reduced noise and higher signal sensitivity, due to the direct collection of the sensor signal by the readout electronics. They are suitable for high radiation environments due to the possibility of applying high depletion voltage and the availability of relatively high resistivity substrates. The use of a CMOS commercial fabrication process leads to their cost reduction and allows faster construction of large area detectors. In this contribution, a general perspective of the state of the art of CMOS detectors for High Energy Physics experiments is given. The main developments carried out with regard to these devices in the framework of the CERN RD50 collaboration are summarized.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Assembly and Functional Test of NRAD Heated Instrumentation Rig

This is a technical report (TEV-4296) which documents the configuration and materials of construction for a heated sensor test conducted in the NRAD reactor. It is being reviewed for external release so that operators of university reactors can evaluate whether this level of documentation will be acceptable for similar tests conducted in their reactors. The Objective section of this TEV is reproduced below. The purpose of this TEV is to document assembly and functional testing of a device which is designed to allow advanced sensors to be temporarily placed at the periphery of the NRAD reactor. While in this location, the operational characteristics of the sensors can be evaluated while they are exposed to a neutron flux. This device (or “rig”) consists of a cartridge heater surrounded by six guide tubes, which are in turn surrounded by insulation and an aluminum dry tube. The heater allows for the sensors to be tested while at elevated temperature.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Relation Inference among Sensor Time Series in Smart Buildings with Metric Learning

Smart Building Technologies hold promise for better livability for residents and lower energy footprints. Yet, the rollout of these technologies, from demand response controls to fault detection and diagnosis, significantly lags behind and is impeded by the current practice of manual identification of sensing point relationships, e.g., how equipment is connected or which sensors are co-located in the same space. This manual process is still error-prone, albeit costly and laborious.We study relation inference among sensor time series. Our key insight is that, as equipment is connected or sensors co-locate in the same physical environment, they are affected by the same real-world events, e.g., a fan turning on or a person entering the room, thus exhibiting correlated changes in their time series data. To this end, we develop a deep metric learning solution that first converts the primitive sensor time series to the frequency domain, and then optimizes a representation of sensors that encodes their relations. Built upon the learned representation, our solution pinpoints the relationships among sensors via solving a combinatorial optimization problem. Extensive experiments on real-world buildings demonstrate the effectiveness of our solution.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Facility Cybersecurity Framework Best Practices

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought. The U.S. Department of Energy’s (DOE) Federal Energy Management Program (FEMP) funded the Pacific Northwest National Laboratory (PNNL) to develop various cybersecurity tools, trainings, and reports to aid federal facility managers – and other building owners and operators – in better applying frameworks and lessons learned from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), risk management framework (RMF), DOE’s cybersecurity capability maturity model (C2M2), and a wide variety of industry best practices and guidance documents (i.e., NIST 800 series, Department of Defense United Facilities Criteria). This set of tools, collectively known as the FEMP Facility-Related Control System Cyber Toolkit (FRCS Cyber Toolkit)2, is focused on cybersecurity concerns from facility-related control systems and other operational technology (OT), such as industrial control systems (ICS). The FRCS Cyber Toolkit can be applied across six of the sixteen critical infrastructure sectors designated by the Department of Homeland Security, including government facilities, healthcare and public health, commercial facilities (e.g., public assembly, offices, lodging), financial services (e.g., banking and insurance), emergency services (e.g., fire and police stations), and information technology. With increasingly converged IT and OT systems, it is crucial to address OT cybersecurity considerations and assess how the seam of these two systems could impact the overall cybersecurity posture of a facility. The objective of this report is to provide an overview of the best possible method to use FRCS Cyber Toolkit (section 2.0) and distilled cybersecurity best practices for the federal facilities to address growing non-linear cyber threats (section 3.0). Recommendations in this document are aggregated from several NIST and other documents (see Appendix A for additional details).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Facility Cybersecurity Framework Best Practices Version 2.0

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Miniaturized Magnetoelastic Sensor System

This article describes the design, assembly, and implementation of a hand-held, magnetic-field-based sensor system that can be adapted for a variety of sensing applications. The miniaturized system is based on Chemical Identification by Magneto-Elastic Sensing (ChIMES) technology, which uses three concentric solenoid coils to wirelessly interrogate a sensor body comprised of a response material coupled to a magnetoelastic wire. The response material expands when it encounters a target, imposing mechanical stress on the wire and altering its magnetic permeability. The sensor bodies are passive, requiring no external power source, and they are small, measuring about 15 mm in length and 3.0 mm in diameter. Up to four sensor bodies can be configured as an evenly-spaced linear array. The sensor system operates by applying a low-frequency, current-stabilized, filtered triangle wave to a uniform-density excitation coil to switch the magnetic domains within the wire. Further, the responses from the sensors are picked up by a detection coil as stress-induced changes in the Faraday voltage, and the strong magnetic field induced by the excitation coil in the detection coil is nullified by a cancellation coil reverse-wound in series with the detection coil. The responses of the sensors in an array are separated in time by a linear gradient dc biasing coil. The sensors can be interrogated through metallic and nonmetallic barriers. The signals from the detection coil and the excitation coil are digitized by a pair of bipolar analog-to-digital converters (ADCs). A Raspberry Pi single-board computer (SBC) and associated software perform data acquisition and control all aspects of the sensor system hardware. The program allows the user to select the number of sensors in the array, the type of signal that is being collected, and the number of samples to take. The program also allows for signal processing of the sensor data, such as baseline correction. The program can differentiate sensor peaks from each other and calculate the magnitude of each sensor response with less than 1% error. The data are then displayed along with a graph of the signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Secure Data Logging and Processing with Blockchain and Machine Learning (Final Report)

Secure Data Logging and Processing with Blockchain and Machine Learning (ML) research is focused on the development of a platform to securely log and process sensor data in fossil power plants. The platform integrates two emerging technologies, blockchain and ML, and incorporates several innovative mechanisms to ensure the integrity, reliability, and resiliency of power systems. The goal is to protect the power plant from various cyberattacks such as false data injection and denial of service attacks using these technologies. The research goal was enabled by the following Research Project Objectives: 1) Secure authentication and identity verification of sensor nodes, actuators, and other equipment within a network. 2) Development of mechanisms that ensure only data sent by legitimate sensors are accepted and stored in the data repository. 3) Development of data aggregation methodologies using ML / Deep Learning (DL) algorithms to minimize noise / faulty data. 4) Implementation of the blockchain technologies to provide data security using secured IOTA framework & nodes.

20 FOSSIL-FUELED POWER PLANTS↗