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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterization of Embedded Sensors in Stainless Steel Test Articles and Design/Planning for MAGNET Testing

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes for heat rejection, which results in significant thermal stresses in the monolithic structures. This work describes the initial characterization and test plan for evaluating stainless steel test articles fabricated with embedded sensors for measuring heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing. The electrically heated testing will be performed in the non-nuclear Microreactor Agile Non-Nuclear Testbed and Single Primary Heat Extraction and Removal Emulator facilities located at Idaho National Laboratory. The goals of these tests are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. More generally, the ability to monitor component and system health during microreactor operation is attractive for providing a high sensor density to inform a limited number of microreactor operators to ultimately reduce operation and maintenance costs and move toward semi-autonomous operation. This report discusses the characterization of embedded thermocouples and fiber optic sensors in relevant test articles, including cylindrical pipes and hexagonal monolithic test articles for heat pipe-based reactors. The sensors were embedded by placing them in machined channels and then building up additional material by using ultrasonic additive manufacturing (UAM). UAM is a solid-state welding process that uses downward pressure and a lateral scrubbing motion to bond thin metal foils to a base material layer by layer. The ultrasonic welding process relies on the plastic deformation of the metal—as opposed to typical melting and solidification—to break oxide scales and bond the metal layers. The characterization of these embedded sensors included evaluating fiber optic signal attenuation, observing residual strain in the fibers, investigating microstructural and mechanical aspects, and demonstrating the sensors under various thermal loads and acoustic vibrations. Post-embedding characterization showed a fine grain structure (<1 μm) near the interfaces of the bonded foils as a result of severe deformation from the welding process. A large increase in hardness was observed at the foil interfaces and the fiber/matrix interface compared with the bulk matrix. Even when compared with the SS304 interfaces, the higher hardness observed around the embedded fiber suggests a higher degree of deformation due to the soft metal coating around the silica fiber core. The distributed fiber-optic temperature sensors and embedded thermocouples reliably measured temperature distributions during steady-state and transient thermal testing. The embedded fiber-optic sensors reliably measured strain during both transient and steady-state testing and properly identified resonant frequencies during acoustic testing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Analysis of WIRE-21 SPND and Optical Fiber Sensor Measurements

The Wireless Instrumented RB Experiment 2021 (WIRE-21) was a highly instrumented experiment designed to test prototype wireless temperature and pressure sensors developed by Westinghouse Electric Corporation (WEC) in the High Flux Isotope Reactor (HFIR). In addition to these sensors, a suite of other sensors was also integrated into the experiment capsule, including various types of distributed optical fiber sensors and self-powered neutron detectors (SPNDs). This report presents an analysis of the data generated by the SPNDs and optical fiber sensors that were tested under the highest reported neutron flux. The distributed optical fiber sensor results demonstrate that F-doped optical fibers, particularly those inscribed with fs fiber Bragg gratings (FBGs), are capable of surviving fast neutron fluences on the order of 1021 n fast /cm 2 at temperatures relevant to light-water reactors (between 200 and 400°C). However, a significant blue-shift in the optical spectra of these sensors was observed over the course of irradiation which cannot be explained based on the current understanding of radiation-induced compaction in fused silica glass. A mechanistic understanding of this drift has not yet been developed and is proposed as future scope under the Advanced Sensors and Instrumentation program. Of the four SPNDs, only one appeared to operate normally during three cycles of irradiation. A radiation transport model of the experiment in HFIR was used to determine time-dependent neutron flux in this SPND and to calculate the neutron sensitivity of the device. Results showed a cycle averaged sensitivity of 1.5 ×10 -22 and 1.4 ×10 -22 A/nν for the first and third cycles of irradiation, respectively. Additional details pertaining to the optical fibers and SPNDs are included herein.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Sensor Placement Optimization Study for the Built Environment: Operational Use Cases

Systems of fixed-position radiation sensors can provide information that assists emergency responders following nuclear and radiological incidents. State, local, tribal, and territorial (SLTT) government agencies that implement systems of fixed-position sensors are faced with numerous decisions regarding sensor selection, quantity, and placement. To develop guidance on implementation of radiation detection systems, we simulated the release of radioactive material in an urban environment using a combination of three models: the Weather Research Forecasting (WRF), Quick Urban and Industrial Complex (QUIC), and Monte Carlo N-Particle (MCNP) models. We then evaluated the performance of several hypothetical sensor systems. The small number of simulations we conducted are not sufficient to generate definitive design guidance for radiation sensor systems, but we did identify trends that would be of interest to emergency planners. For a scenario that releases 1000 curies of Cs-137, radiation detectors were needed at 500-meter intervals to have a high likelihood of event detection and to estimate source location and plume detection. We also noted that optimal detector altitude varied with distance to the source. We recommend additional research in this area be conducted to support developing sensor placement guidelines that expand on a range of locations, isotopes, activity levels, and different weather conditions. Original simulation strategies included a range of environments, additional radioisotopes (Am241 and AmBe), and a larger selection of sensor types. These types of expansions would support SLTT guidance on sensor system recommendations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Interpreting Mobile and Handheld Air Sensor Readings in Relation to Air Quality Standards and Health Effect Reference Values: Tackling the Challenges

The US Environmental Protection Agency (EPA) and other federal agencies face a number of challenges in interpreting and reconciling short-duration (seconds to minutes) readings from mobile and handheld air sensors with the longer duration averages (hours to days) associated with the National Ambient Air Quality Standards (NAAQS) for the criteria pollutants-particulate matter (PM), ozone, carbon monoxide, lead, nitrogen oxides, and sulfur oxides. Similar issues are equally relevant to the hazardous air pollutants (HAPs) where chemical-specific health effect reference values are the best indicators of exposure limits; values which are often based on a lifetime of continuous exposure. A multi-agency, staff-level Air Sensors Health Group (ASHG) was convened in 2013. ASHG represents a multi-institutional collaboration of Federal agencies devoted to discovery and discussion of sensor technologies, interpretation of sensor data, defining the state of sensor-related science across each institution, and provides consultation on how sensors might effectively be used to meet a wide range of research and decision support needs. ASHG focuses on several fronts: improving the understanding of what hand-held sensor technologies may be able to deliver; communicating what hand-held sensor readings can provide to a number of audiences; the challenges of how to integrate data generated by multiple entities using new and unproven technologies; and defining best practices in communicating health-related messages to various audiences. This review summarizes the challenges, successes, and promising tools of those initial ASHG efforts and Federal agency progress on crafting similar products for use with other NAAQS pollutants and the HAPs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2012

Exploitation of Unique Properties of Zeolites in the Development of Gas Sensors

The unique properties of microporous zeolites, including ion-exchange properties, adsorption, molecular sieving, catalysis, conductivity have been exploited in improving the performance of gas sensors. Zeolites have been employed as physical and chemical filters to improve the sensitivity and selectivity of gas sensors. In addition, direct interaction of gas molecules with the extraframework cations in the nanoconfined space of zeolites has been explored as a basis for developing new impedance-type gas/vapor sensors. In this review, we summarize how these properties of zeolites have been used to develop new sensing paradigms. There is a considerable breadth of transduction processes that have been used for zeolite incorporated sensors, including frequency measurements, optical and the entire gamut of electrochemical measurements. It is clear from the published literature that zeolites provide a route to enhance sensor performance, and it is expected that commercial manifestation of some of the approaches discussed here will take place. The future of zeolite-based sensors will continue to exploit its unique properties and use of other microporous frameworks, including metal organic frameworks. Zeolite composites with electronic materials, including metals will lead to new paradigms in sensing. Use of nano-sized zeolite crystals and zeolite membranes will enhance sensor properties and make possible new routes of miniaturized sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Embedded Piezoresistive Microcantilever Sensors Functionalized for the Detection of Methyl Salicylate

Sensors designed to detect the presence of methyl salicylate (MeS) have been tested. These sensors use a sensor platform based on the embedded piezoresistive microcantilever (EPM) design. Sensing materials tested in this study included the polymer poly (ethylene vinyl acetate), or PEVA as well as a composite sensing material consisting of the enzyme SA-binding protein 2, or SABP-2. The SABP-2 was immobilized within a biocompatible Hypol gel matrix. The PEVA-based sensors exhibited slower but reversible responses to MeS vapors, recovering fully to their initial state after the analyte was removed. SABP-2 sensors exhibited faster overall response to the introduction of MeS, responding nearly instantly. These sensors, however, do not recover after exposures have ended. Sensors using the SABP-2 sensing materials act instead as integrating sensors, measuring irreversibly the total MeS dose obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Gas sensor protection device and method

A gas sensor includes a sensor housing and a sensing element located within the sensor housing. The sensing element has a distal end and defines an axis. The gas sensor also includes a sensor protection device coupled to the sensor housing and at least partially surrounding the distal end of the sensing element. The sensor protection device includes a first member coupled to the housing, the first member having a generally rectangular cross-sectional shape in a plane perpendicular to the axis. The first member includes a gas inlet and a gas outlet. The sensor protection device also includes a second member coupled to the housing.

42 ENGINEERING↗
DOE OSTI2017

Wireless sensor platform

A wireless sensor platform and methods of manufacture are provided. The platform involves providing a plurality of wireless sensors, where each of the sensors is fabricated on flexible substrates using printing techniques and low temperature curing. Each of the sensors can include planar sensor elements and planar antennas defined using the printing and curing. Further, each of the sensors can include a communications system configured to encode the data from the sensors into a spread spectrum code sequence that is transmitted to a central computer(s) for use in monitoring an area associated with the sensors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Two-level Clustering-based Target Detection Through Sensor Deployment and Data Fusion

Target detection is one fundamental problem in many sensor network-based applications, which is typically tackled in two separate stages for sensor deployment and data fusion. We propose an integrated solution, referred to as 2C-SSEM,which combines 2-level clustering-based sensor deployment and source strength estimate map-based data fusion for the detection of a single static or moving target. 2C-SSEM conducts the first level of clustering to determine a sensor deployment scheme and the second level of clustering to divide the deployed sensors into several subsets. For each sensor, the source strength is estimated at each grid point of the entire region based on a signal attenuation model, and for each subset of sensors, the target location is estimated using a distribution map-based statistical analysis method. A final detection decision is made by thresholding the clustering degree of the target location estimates computed by all subsets of sensors. Compared with traditional grid-based target detection methods, 2C-SSEM significantly reduces the computation complexity and improves the detection performance through an integrated optimization strategy. Extensive simulation results show the performance superiority of the proposed solution over several well-known methods for target detection.

Rao, Nageswara↗
DOE OSTI2018

A Portable 16-channel Magnetic Sensor for Human Magnetocardiographic Experiments

Here, we constructed a portable, low-cost 16-channel magnetic sensor for magnetocardiography (MCG) applications. The sensor is based on a high-sensitivity optically pumped magnetometer (OPM) with a novel configuration of nearly parallel pump and probe laser beams. The OPM-based 16-channel magnetic sensing is realized in a single module by using a fairly large rubidium vapor cell, broad pump/probe laser beams, and a 16-channel photodiode array, leading projected 10-fold reduction of the cost of sensors. The portability of the sensor is achieved by coupling the pump and probe lasers to OPM optics through multi-mode optical fibers. The 16-channel magnetic sensor is surrounded by a non-magnetic enclosure for safe operation. For MCG experiments with the sensor, its main components except lasers are located inside a magnetically shielded room (MSR), which reduces the ambient magnetic noise. The location of lasers and supporting electronics outside the MSR improves the sensitivity of the sensor. We report that the 16-channel magnetic sensor has a magnetic field sensitivity sufficient to directly detect the human cardiac magnetic signals.

16-channel magnetic sensing↗
DOE OSTI2022

Selective sensor data transmission

A system controls a transmission of data. A sensor datum measured by a sensor is received. Whether to send the received sensor datum to a multiplexer is determined based on a predefined real time download rate. When the determination indicates to send the received sensor datum to the multiplexer, the received sensor datum is sent to the multiplexer. When the determination does not indicate to send the received sensor datum to the multiplexer, the received sensor datum is written to a data file. The written sensor datum is sent from the data file to the multiplexer when there is an indicator of excess available bandwidth.

Liaghati, Amir Leon↗
DOE OSTI2022

Partial discharge sensor

An approach to detecting partial discharge events involves retrofitting an electrical system to include a capacitive sensor configured to capacitively sense partial discharge events of a component of the electrical system. The capacitive sensor has a first electrical conductor that forms a first terminal of the capacitive sensor, a second electrical conductor that forms a second terminal of the capacitive sensor, and a dielectric that separates the first electrical conductor and the second electrical conductor. The capacitive sensor generates an electrical sensor signal at an output of the capacitive sensor in response to the partial discharge event. The electrical sensor signal is converted to an optical signal and the optical signal is processed to detect an occurrence of the partial discharge event.

Karin, Todd↗
DOE OSTI2025

A high-temperature heat flux sensor using the transverse Seebeck effect in elemental rhenium

Heat flux sensors compatible with hot environments are critical to advance aerospace, materials, and energy generation technologies that cope with extreme thermal conditions. In this work, we report on the development and characterization of a high-temperature heat flux sensor using the transverse Seebeck effect in rhenium single crystals. The sensor leverages refractory alloys and ceramics compatible with temperatures exceeding 1000 °C. The heat flux sensor was characterized from room temperature to 500 °C using a temperature-controlled calibration facility. At constant temperature, the sensor’s voltage output is linear with respect to the absorbed heat flux. The responsivity of the sensor varies with temperature, from 1.3 μV/(W/cm 2 ) at room temperature to −3.2 μV/(W/cm 2 ) at 500 °C, increasing monotonically in magnitude after changing sign from positive to negative at approximately 300 °C. The experimental results are in good agreement with analytical predictions of the sensor’s temperature-dependent responsivity, which suggest a further increase in magnitude up to −7.4 μV/(W/cm 2 ) at 1000 °C. These results highlight the unique characteristics of rhenium as a TSE transducer. The design offers compatibility with a wide range of operating temperatures and yields a measurement sensitivity that increases as the environmental conditions become more challenging.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2017

Editors' Choice—Field Trials Testing of Mixed Potential Electrochemical Hydrogen Safety Sensors at Commercial California Hydrogen Filling Stations

Hydrogen safety sensors must meet specific performance requirements, mandated by the U.S. Department of Energy, for hydrogen fueling station monitoring. Here, we describe the long-term performance of two zirconia-based mixed potential electrochemical hydrogen gas sensors, developed specifically with a high sensitivity to hydrogen, low cross-sensitivity, and fast response time. Over a two-year period, sensors with tin-doped indium oxide and strontium doped lanthanum chromite electrodes were deployed at two stations in four field trials tests conducted in Los Angeles. The sensors documented the existence of hydrogen plumes ranging in concentration from 100 to as high as 2700 ppm in the area surrounding the dispenser, consistent with depressurization from 700 bar following vehicle refueling. As expected, the hydrogen concentration reported by the mixed potential sensors was influenced by wind direction. Baseline stability testing at a Chino, CA station showed no measureable baseline drift throughout 206 days of uninterrupted data acquisition. In conclusion, the high baseline stability, excellent correlation with logged fueling/depressurization events, and absence of false alarms suggest that the zirconia-based mixed potential sensor platform is a good candidate for protecting hydrogen infrastructure where frequent calibrations or sensor replacement to reduce the false alarm frequency have been shown to be cost prohibitive.

08 HYDROGEN↗
DOE OSTI2023

Assessment of Nuclear Sensors and Instrumentation Maturity in Advanced Nuclear Reactors

In the last decade, 97% of the worldwide commercial nuclear reactors connected to the grid were Light Water Reactors (LWRs). LWRs are expected to stay the dominant type of nuclear reactors for the next few decades. Reliable and redundant safety systems are required in nuclear reactors to ensure safe operation and shutdown in abnormal conditions. These safety systems are actuated by the signals obtained from several sensors and instrumentation in and out of the reactor core. Research and Development (R&D) in advanced sensors and instrumentation has gained extra attention, particularly following the accident at the Three Mile Island Unit-2 (TMI-2). In LWRs, these sensors and instrumentation have shown a high level of maturity with long operating experience. Ensuring the compatibility of these sensors and instrumentation with advanced nuclear reactors (Generation IV) is necessary, particularly with the expected expansion of the nuclear industry in the next few decades. Nuclear Sensor and instrumentation technologies used in the current generation of LWRs were investigated. The compatibility of these technologies with advanced reactors was assessed by comparing the advanced reactors' environments with those of the currently operating reactors. In addition to that, the needed R&D for such technologies was highlighted. In comparison with the LWRs environment, it was shown that advanced reactor environments are expected to experience elevated temperatures, a fast neutron spectrum, and a harsh corrosion environment. It was demonstrated that R&D is required mainly for fixed in-core nuclear sensors and instrumentation, while it is not a priority for ex-core nuclear sensors and instrumentation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

NREL Hydrogen Sensor Testing Laboratory

Relevance: Detection is recognized as a critical element for hydrogen facility safety design and supports risk mitigation. Detection methodologies will support validation of H2 behavior research. Hydrogen point sensors play a critical role for safety and process monitoring, but other methodologies can be developed. Approach: NREL Sensor Laboratory tests and verifies sensor performance for manufacturers, developers, end-users, regulatory agencies and SDOs/CDOs NREL deployment activity supports regulatory requirement verification, hydrogen behavior models, and method development for use by stakeholders. Accomplishments and Progress: NREL's R&D accomplishments have supported developers, industry, and SDOs by providing sensor performance and deployment expertise not otherwise available. Development of alternative detection strategies for hydrogen applications have been initiated. HyWAM and advanced detection methodologies deployments at H2@Scale Facilities are being implemented. Collaborations: Collaboration with government laboratories, universities, private organizations and regulatory agencies has leveraged the NREL Sensor Laboratory's success in advancing hydrogen safety sensors and process control. Proposed Future Work: NREL will support hydrogen deployment by the proper implementation of hydrogen sensors and advanced detection strategies. NREL will continue to support science-based codes and standards. This effort will be guided by the needs of the hydrogen community.

codes and standards↗
DOE OSTI2011

Steam distribution and energy delivery optimization using wireless sensors

The Extreme Measurement Communications Center at Oak Ridge National Laboratory (ORNL) explores the deployment of a wireless sensor system with a real-time measurement-based energy efficiency optimization framework in the ORNL campus. With particular focus on the 12-mile long steam distribution network in our campus, we propose an integrated system-level approach to optimize the energy delivery within the steam distribution system. We address the goal of achieving significant energy-saving in steam lines by monitoring and acting on leaking steam valves/traps. Our approach leverages an integrated wireless sensor and real-time monitoring capabilities. We make assessments on the real-time status of the distribution system by mounting acoustic sensors on the steam pipes/traps/valves and observe the state measurements of these sensors. Our assessments are based on analysis of the wireless sensor measurements. We describe Fourier-spectrum based algorithms that interpret acoustic vibration sensor data to characterize flows and classify the steam system status. We are able to present the sensor readings, steam flow, steam trap status and the assessed alerts as an interactive overlay within a web-based Google Earth geographic platform that enables decision makers to take remedial action. We believe our demonstration serves as an instantiation of a platform that extends implementation to include newer modalities to manage water flow, sewage and energy consumption.

54 ENVIRONMENTAL SCIENCES↗