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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Predicting critical heat flux using localized sensing at invisible vapor-liquid interfaces

Predicting critical heat flux (CHF) in two-phase electronics cooling systems remains a significant challenge due to the sudden onset of boiling crisis and the difficulty in directly visualizing vapor-liquid interfaces. Existing sensing methods rely on lagging temperature measurements, optically accessible systems, or spatially averaged signals that cannot pinpoint CHF initiation at localized high-heat-flux regions. Here, we report a planar capacitive sensing approach that enables real-time, localized detection of vapor-liquid interface dynamics for CHF prediction in boiling heat transfer. The capacitive sensor exploits the dielectric constant difference between liquid and vapor phases to capture bubble nucleation, growth, and departure dynamics with a temporal resolution down to 2 ms. The capacitive sensing reveals distinct signals across boiling regimes: from high-frequency fluctuations during strong nucleate boiling to low-frequency fluctuations with increased amplitudes when approaching CHF. The multi-sensor array experiments demonstrate real-time localized sensing, where each sensor responds exclusively to boiling in its immediate vicinity without crosstalk from neighboring regions. This non-intrusive sensing approach provides predictive rather than lagging sensing signals of CHF occurrence, offering predictive diagnosis of two-phase liquid cooling for the thermal management of high-power-density electronics.

CHF↗
DOE OSTI2023

Reflective Fiber Temperature Probe Based on Localized Surface Plasmon Resonance towards Low-Cost and Wireless Interrogation

Reflection fiber temperature sensors functionalized with plasmonic nanocomposite material using intensity-based modulation are demonstrated for the first time. Characteristic temperature optical response of the reflective fiber sensor is experimentally tested using Au-incorporated nanocomposite thin films deposited on the fiber tip, and theoretically validated using a thin-film-optic-based optical waveguide model. By optimizing the Au concentration in a dielectric matrix, Au nanoparticles (NP) exhibit a localized surface plasmon resonance (LSPR) absorption band in a visible wavelength that shows a temperature sensitivity ~0.025%/°C as a result of electron–electron and electron–phonon scattering of Au NP and the surrounding matrix. Detailed optical material properties of the on-fiber sensor film are characterized using scanning electron microscopy (SEM) and focused-ion beam (FIB)-assisted transmission electron microscopy (TEM). Airy’s expression of transmission and reflection using complex optical constants of layered media is used to model the reflective optical waveguide. A low-cost wireless interrogator based on a photodiode transimpedance-amplifier (TIA) circuit with a low-pass filter is designed to integrate with the sensor. The converted analog voltage is wirelessly transmitted via 2.4 GHz Serial Peripheral Interface (SPI) protocols. Feasibility is demonstrated for portable, remotely interrogated next-generation fiber optic temperature sensors with future capability for monitoring additional parameters of interest.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Sensing Electrical Networks Securely & Economically (SENSE)

The growing adoption of distributed energy resources (DERs) like battery energy storage systems and roof top solar/PV and the rapid penetration of electric vehicles (EVs), the electric grid is undergoing a major transformation with elevated stress on legacy grid assets. Despite a lot of expenditure to address these challenges, both in dollars and manpower, utilities have not been able to receive the value that was promised. The gains have been most visible at the transmission and substation level, especially where the main objective was improving operational and economic efficiency for the utility. Improving visibility and control at a few select points enhances the existing and established paradigm of centralized command and control. With changing load patterns, load types and the overall transition to an “active grid”, the centralized control and coordination paradigm gets challenged. To address the challenges, a new architecture and mechanism is needed, one that supports decentralized control and decision making, extracting value streams at the grid edge, particularly as the changes are fueled by transitions occurring in the distribution system. To address this, a communications and data processing platform, “GAMMA” was developed and demonstrated through the project. At the heart of the platform, are distributed, intelligent edge nodes with sensing and compute capabilities, that can record and analyze information locally. They are embedded in sensors and actuators specific to different distribution system applications. Phase 1 of the project focused on developing novel sensor technology that can be used for monitoring utility pole top distribution transformers. The sensors were designed with the objective of being low-cost, communicating with the GAMMA cloud using novel “delay-tolerant” networking using Bluetooth and a secure mobile application. They were non-intrusive in nature so that they can be installed quickly in the field, resulting in overall low cost of deployment and operations. Following the successful completion of Phase 1, the team manufactured 100 units for a field demonstration in Phase 2. The field demonstration was carried out on two real feeder systems with the local utility partner. In total, 100 sensors were installed and operated over a period of 6 months in the state of Georgia. The platform is operational end to end, with the cloud infrastructure deployed on a distributed, serverless environment that can serve multiple data streams, an analytics engine and a portal to securely view the data from multiple assets. The data collected through the GAMMA Mobile Phone app showcased the viability of the novel delay tolerant networking architecture, and the data processing algorithms developed through the course of the project, were successful in extracting important information about the overall network, improving the utility’s visibility and situational awareness in the distribution feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Unsupervised anomaly clustering via offset alignment in multivariate grid sensing data

Modern industries increasingly rely on multi-sensor technologies to acquire complex, high-dimensional data streams, enabling advanced monitoring and control systems. One critical application is online anomaly detection in electrical smart grids, where multivariate and multimodal sensing technologies play a vital role. However, detecting anomalies in such time-series data is challenging due to their inherent temporal dependencies and stochastic behavior. Traditional approaches based on supervised and semi-supervised learning methods depend on labeled datasets, which are often unavailable in real-world scenarios. While unsupervised methods have emerged as promising alternatives, these methods are highly susceptible to noise and outliers commonly present in sensing applications. Furthermore, deep learning-based anomaly detection methods, despite their performance, are often criticized for their black-box nature, limiting their applicability in safety-critical and online environments where interpretability and explainability are paramount. In this work, we propose an unsupervised anomaly clustering method leveraging a cyclic alignment-based offset detection algorithm for multivariate time-series signals. The proposed method is applied to multivariate data collected from vibrational, voltage, and magnetic field sensors deployed in a local grid substation. Our results demonstrate the robustness of the algorithm in accurately clustering various anomalies/events across different sensing modalities. Additionally, we compare the effectiveness of the proposed approach against a simple pattern-based anomaly detection method, which performs well for univariate data but fails to generalize to multivariate and multimodal time-series data.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2022

Microcam: A Low Power and Privacy Preserving Multi-modal Platform for Occupancy Detection (Final Report)

Heating, ventilation, and air conditioning (HVAC) consumes a significant portion of the energy used in buildings. Much of this is wasted energy, used when buildings are either not occupied at all, or occupied well under their maximum design conditions. This project has focused on residential occupancy detection to autonomously control HVAC systems and save energy. Limitations of existing occupancy sensors include one or more of the following: (i) they employ sensors or algorithms that are not able to detect stationary occupants; (ii) they cannot classify the source of the motion (such as a pet); (iii) depending on the camera resolution and employed algorithms, they do not allow for embedded or onboard computation, and require external or cloud-based processing; (iv) many algorithms developed for camera-based systems are sensitive to lighting changes, and thus prone to missed detections or false alarms; (v) Most existing systems depend on adjustment of settings for different scenarios, complicating self-commissioning; (vi) they cannot provide high enough accuracy; (vii) they are costly; (viii) they are not battery-powered, thus limiting ease of use and installation. In this project, Syracuse University and its partner SRI have developed a low-cost, high accuracy, standalone residential occupancy sensing platform, referred to as the MicroCam, to address all of the aforementioned challenges. MicroCam can operate on typical alkaline batteries without relying on the “cloud” or external computing resources, and consists of low-power, Artificial Intelligence (AI)-based, IoT platforms. Each platform has multi-modal sensors and can process motion, audio and video data, and send binary occupancy result to a lead platform. All sensor data is processed locally on platforms, and the only transmitted data is the binary occupancy state. In addition, preliminary work has been done on images wherein occupants are not discernable. Thus, MicroCam is a standalone solution preserving privacy of the occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Journey to Time-Variable Moment Tensors through Inversion of Acoustic and Seismoacoustic Data

We explore the capability of acoustic and seismoacoustic datasets to directly resolve a complex, time-variable source consisting of a buried mechanism, represented as a moment tensor, and a spall mechanism, represented as a vertical force at the surface. Traditionally, each component of a resolved moment tensor assumes one underlying source time function, which likely fails to capture the full evolution of a dynamic source, such as an explosion followed by slip on near-source joints or development of spallation. Specifically, we expand previous work to resolve a time-variable moment tensor using single-modality and joint-modality inversion frameworks through analysis of infrasound and seismoacoustic data recorded as part of the Source Physics Experiment Phase II: Dry Alluvium Geology (DAG). We investigate the impact of including signals from seismic-to-air coupling that are local to each infrasound sensor in comparison to mainly atmosphere-propagating acoustic signals, which occur from coupling of the wavefield from the subsurface to the atmosphere directly above the source. Additionally, we assess the ability of our inversion algorithm to fit observed infrasound data using a variety of time-variable source mechanisms. First, we consider the buried moment tensor source alone, which assumes that the determined Green’s functions incorporate effects from spallation or that the impact from spallation is minimal. Second, we examine the estimated buried moment tensor and vertical surface spallation as terms that must both be resolved in the inversion. Third, we assess the ability for an estimated vertical surface spallation source to fit the acoustic data on its own. Finally, we compare results from the joint inversion of both seismic geophone and infrasound acoustic data for the buried-only source compared to buried and spallation sources. Our results are a preliminary investigation into the applications of the inversion technique to recorded datasets and show the technique has limited capabilities using acoustic data alone. Instead, this method shows promise for seismic and seismoacoustic datasets to resolve the time-variable mechanisms of a buried source.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Integrating Hybrid-Clustering and Localized Regression for Time Synchronization of a Hierarchical Underwater Acoustic Sensor Array

Time synchronization is a critical requirement for the application of underwater acoustic sensor network (UWSN). Although a number of time synchronization protocols have been proposed for UWSN, none of them can be directly applied to autonomous acoustic receivers due to lack of hardware platform to communicate with each other. In this paper, we propose a machine learning-based time synchronization framework for autonomous receiver arrays, using the Juvenile Salmon Acoustic Telemetry System as a case study. The proposed framework consists of array partition and time synchronization. Using detections of receiver attached beacons as input, this framework synchronizes all receiver clocks to a root receiver clock. The framework has been successfully used in a field study at Trevallyn Dam forebay in Tasmania, Australia.

Fu, Tao↗
DOE OSTI2024

Optimal function estimation with photonic quantum sensor networks

The problem of optimally measuring an analytic function of unknown local parameters each linearly coupled to a qubit sensor is well understood, with applications ranging from field interpolation to noise characterization. Here we resolve a number of open questions that arise when extending this framework to Mach-Zehnder interferometers and quadrature displacement sensing. In particular, we derive lower bounds on the achievable mean square error in estimating a linear function of either local phase shifts or quadrature displacements. In the case of local phase shifts, these results prove, and somewhat generalize, a conjecture by Proctor []. For quadrature displacements, we extend proofs of lower bounds to the case of arbitrary linear functions. We provide optimal protocols achieving these bounds up to small (multiplicative) constants and describe an algebraic approach to deriving new optimal protocols, possibly subject to additional constraints. Using this approach, we prove necessary conditions for the amount of entanglement needed for any optimal protocol for both local phase and displacement sensing. Published by the American Physical Society 2024

Bringewatt, Jacob↗
DOE OSTI2025

Measuring Local Turbulence Along the Optical Path: Multi-Beam Optical Seeing Sensor

Deflection of light along the optical path is a major source of image degradation for ground-based telescopes. Methods have been developed to measure upper atmospheric seeing based on models of the turbulence in the atmosphere, but due to boundary conditions, transmission within telescope enclosures is more complex. The Multi-beam Optical Seeing Sensor (MOSS) directly measures the component of the image quality degradation from inhomogeneity of the index of refraction within the telescope dome. MOSS outputs four near-parallel beams of light that travel along the optical path and are imaged by the telescope’s detector, landing like starlight on the telescope’s focal plane. By using a strobed light source, we can ‘freeze’ the instantaneous index variations transverse to the optical path. This system captures both ‘dome’ and ‘mirror’ seeing. Through plotting the standard deviation of differential motion between pairs of beams, MOSS enables characterization of the length scale of turbulence within the dome. The temporal coherence of temperature gradients can be probed with different pulse lengths, and the spatial coherence by comparing pairs at different separations across the aperture of the telescope. Optical path turbulence measurements, alongside other telemetry metrics, will guide thermal and airflow management to optimize image quality. A MOSS prototype was installed in the 1.2[Formula: see text]m Auxiliary Telescope (AuxTel) at the Vera C. Rubin Observatory in Chile, and preliminary data constrain the optical path turbulence with a lower bound of 1.4 arcsec. The optical path turbulence varied throughout the night of observing.

Astronomical seeing↗
DOE OSTI2022

Measurements of time and spatial resolution of AC-LGADs with different designs

AC-LGAD sensors are prime candidates for fast and precise measurement of charged particles in a variety of applications. In a fine-pitched pixel or strip AC-LGAD sensor, the signal generated by the passage of a particle is not localized in a limited volume in the sensor, but is shared among multiple electrodes. This signal sharing characteristic allows us to improve the spatial resolution of AC-LGAD sensors beyond the capabilities of common silicon trackers. Since the magnitude of the shared signal depends on the distance between the pixels or strips and the point of passage of the particle, signal sharing is strongly influenced by the geometry of the sensor. The electrode pitch and gap size as well as the shape can be fine tuned to maximize the space resolution, while keeping the detector granularity and the number of channels to be read out under control. Prototypes of AC-LGAD sensors produced at the Brookhaven National Laboratory covering a variety of possible geometries have been studied via Transient Current Technique using an infrared laser, and the signal sharing, spatial resolution, and time resolution of the different topologies have been measured. These results have been compared with measurements performed in test-beams with 120 GeV protons at the Fermilab Test Beam Facility. A time resolution of ~30 ps can be achieved using AC-LGAD sensors with a thickness of 50 μm, compatible to that of standard DC-coupled LGADs, while presenting a far superior spatial resolution. Finally, this combination of high precision, fast timing capabilities, and low material budget makes AC-LGADs an ideal choice for a truly 4D detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Active Region Extent Assessment with X-rays (AREA-X)

The development of semiconductor sensors for new particle tracking detectors places increasing limits on sensor characteristics such as uniformity, size and shape of inefficient areas and size of active compared to inactive sensor areas. Accurately assessing these relatively subtle effects requires either measurements in particle beams or the modification of samples to be used in dedicated laser test setups. Active Region Extent Assessment with X-rays (AREA-X) has been developed as an alternative method for the fast, efficient and precise study of the active area of a semiconductor sensor. It uses a monochromatic, micro-focused X-ray beam with a 10–20 keV energy range as provided by several synchrotron beam lines and uses the photo current induced in the sensor to measure the depth of the responsive sensor volume. It can be used to study local inhomogeneities or inefficiencies, the overall extent of the active sensor volume and its shape and its localised application, which makes the need to gather statistics over a large area unnecessary, allowing for fast readout, which enables studies of the sensor behaviour at a range of external parameters, e.g. temperature or applied bias voltage. This paper presents the measurement concept and technical setup of the measurement, results from initial measurements as well as capabilities and limitations of the method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

In-Sodium Testing of a Prototype Thermoacoustic Power Sensor for Sodium-Cooled Fast Reactors

The ultimate goal of this project is to develop and demonstrate a thermoacoustic power sensor (TAPS) for Sodium-Cooled Fast Reactors (SFRs), with potential application also envisioned to other nuclear technologies such as Lead-Cooled Fast Reactors (LFRs), Molten Salt Reactors (MSRs), in addition to Light Water Reactors (LWRs). The project was led by Westinghouse Electric Company, LLC (Westinghouse) and carried out in collaboration with Argonne National Laboratory (ANL) and the University of Pittsburgh. A TAPS is a passive (self-powered), non-invasive (wireless) sensor envisioned for measuring key parameters, such as local temperature and neutron flux, in a nuclear reactor core. The sensor generates pressure waves (i.e., sound waves) with a frequency and amplitude dependent upon nuclear operating conditions (coolant temperature or power changes). The acoustic waves are able to travel through the core and associated structures, and reach to the sensor network placed outside and/or inside of the reactor vessel. These sensors require a very small amount of power which, during loss of power events, can be provided, for example, by harvesting gamma radiation energy, thus resulting in a monitoring system that can function both during normal operation and during loss of power events. Westinghouse and the University of Pittsburgh designed and fabricated TAPS prototypes for Argonne National Laboratory (ANL) to carry out in-sodium testing to evaluate the effects of sodium on the TAPS and the performance of the TAPS technique in sodium. Argonne received a TAPS prototype from Westinghouse, and the prototype was modified such that it can be installed into a test vessel and function in sodium at elevated temperature without potentially leaking. A water mockup test apparatus was constructed to validate proper working of the prototype. An instrumentation and control (I&C) system, running on the National Instruments (NI) LabView platform, was developed to: 1) operate both the water mockup test and the in-sodium test facility; and 2) process and analyze the received acoustic signals from an array of accelerometers and the Argonne sodium-submersible high-temperature acoustic sensor. The prototype was successfully tested in a water bath at different temperatures. Water mockup tests demonstrated that the TAPS prototype is working properly and its resonance frequency changes linearly with the coolant (water) temperature. A TAPS test apparatus was constructed and integrated with the upgraded Under-Sodium Viewing (USV) sodium test facility. The integrated USV-TAPS sodium test facility has been operational. The TAPS prototype and a high-temperature sodium-submersible acoustic sensor (SSAS) developed by Argonne were both installed inside the TAPS test vessel. Being operated within argon cover gas under ambient conditions, the TAPS prototype demonstrated that it was functioning properly with a resonance frequency at 1407.2 Hz, which was successfully detected by the accelerometers mounted on the external wall of the vessel and the high-temperature SSAS installed inside the vessel. After successfully transferring sodium into the vessel, in-sodium tests of the prototype were conducted. Tests of the TAPS prototype demonstrated that the resonance frequency of the TAPS changes linearly with respect to the temperature difference between the interior of the TAPS and bulk sodium. The early tests showed that the TAPS prototype could not establish a continuous and consistent resonance in sodium. The resonance diminished before the prototype reached its operating temperature. A signal postprocessor was added to the DAQ module latterly to isolate interferences, enhance signal conditioning, improve peak detection, and generate resonance frequency versus temperature plots. After testing in molten sodium and immersion at higher temperature for several weeks, the TAPS prototype was able to establish a continuous resonance. Performance evaluation of the TAPS prototype was then conducted in sodium. The tests included the investigation of 1) the effects of the temperature difference between the TAPS and bulk sodium, 2) the effects of sodium flowrate; and 3) the performance of the different sensor-receiver systems positioned inside or outside the vessel. Results of a test demonstrated that, with limited sodium circulation, a continuous and consistent resonance of the TAPS prototype was established occasionally. The tests also demonstrated that, because of the nature of detection principles and mounting methods, the high temperature SSAS is more affected by acoustic noise, while accelerometers are more affected by vibrations, in the test environment. It is unknown why the TAPS prototype only occasionally established a continuous and consistent resonance when the TAPS temperature reached its operating temperature in molten sodium, and why it ultimately failed to resonate at all. A failure modes assessment was conducted and a few potential causes of failure were identified. Different post in-sodium tests were conducted to obtain additional information potentially relevant to the cause of the failure. Nondestructive evaluation techniques are suggested to examine the internal integrity as well as the gas mixture of the prototype. If they prove inconclusive, the prototype should be cut open to conduct a thorough inspection of its internal integrity and determine the state of the gas mixture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

HRPPD photosensors for RICH detectors with a high resolution timing capability

Recently, a new version of DC-coupled High Rate Picosecond Photodetectors (DC-HRPPDs) substantially re-designed for use at the Electron-Ion Collider (EIC) has been developed. A first batch of seven “EIC HRPPDs” was manufactured in early 2024. These HRPPDs are DC-coupled photosensors based on Micro-Channel Plates (MCPs) that have an active area of 104 mm by 104 mm, 32 × 32 direct readout pixel array at a pitch of 3.25 mm, peak quantum efficiency in excess of 30%, exceptionally low dark count rates and timing resolution of 15–20 ps for a single photon detection. As such, these photosensors are very well suited for Ring Imaging CHerenkov (RICH) detectors that can additionally provide high resolution timing capability, especially in a configuration where a detected charged particle passes through the sensor window, which produces a localized flash containing a few dozens of Cherenkov photons in it.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

‪A Novel Methodology for Longitudinal Studies of Home Thermal Comfort Perception and Behavior

Human-building interactions significantly influence building energy consumption and affect peak energy demand. For example, heating and cooling contribute 46% of daily peak residential energy demand. Grid-interactive efficient buildings (GEBs) can potentially increase energy-demand flexibility and accelerate the adoption of renewables. However, traditional demand response (DR) programs focused on shedding peak loads disregard the human-building interactions leading to occupant thermal frustration. Specifically, they do not model occupants’ ability to override thermostat controls, nor how indoor environmental conditions and sociocultural factors affect the timing and magnitude of overrides. Studies have found 20% of occupants override thermostat setpoints during DR events longer than 6 hours yet lack detail about the motivation that might guide more successful efforts. Balancing energy-demand flexibility with occupant thermal comfort requires understanding dynamic occupant behavior, the underlying psychophysiological drivers, in the context of homes. This paper presents methods for scalable longitudinal studies of residential occupant behavior dynamics to inform the development of psychophysiological occupant-centric building models. Smart sensors were installed to measure local environmental conditions in 20 homes in two regions of the United States. Just-in-time ecological momentary assessments (EMAs) provided qualitative data on occupant thermal comfort and local environmental conditions not captured in Internet-of-Things (IoT) based studies or existing datasets. Participant interviews provided insight into environmental attitudes, mental models, and the role of economics in comfort and behavior, which in turn may affect thermostat interactions. Based on these data, interventions in the next phase of the study will collect data and monitor occupant behavior during simulated DR events.

Kane, Michael↗
DOE OSTI2021

Estimation of the Ambient Wind Field From Wind Turbine Measurements Using Gaussian Process Regression

In the search for a lower levelized cost of wind energy, one approach is to increase the accuracy of wind turbine measurements such as wind speed and wind direction. The sensors available on wind turbines are susceptible to local turbulence and measurement bias, which can result in suboptimal turbine performance. As an alternative, recent research has considered using the sensor measurements in a coordinated manner. With such a cooperative approach, the local wind conditions can be estimated more accurately and reliably without the need for additional measurement equipment. In this paper, a novel wind field estimation approach is presented that estimates the local wind conditions based on turbine measurements using Gaussian processes. We show that the estimation framework is able to improve the accuracy of the wind direction estimate both in an offline and online manner, as well as identify possible biases in the sensors and reduce unnecessary wind turbine yaw activity.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2023

Quantitative Characterization of Hyper-Local Atmospheric Greenhouse Gas Sources

Atmospheric greenhouse gas (GHG) emissions are often characterized using stationary, tower-based sensors. Ground based sensors reside in the turbulent boundary layer and are subject to intense concentration impulses from hyper-local (<100m) point sources of emissions. These high frequency spikes are often filtered out in broader emission flux studies, losing valuable information about how hyper-local sources influence receptors. In this study, we investigated how empirical atmospheric data can be used to locate and quantify a concurrently measured hyper-local point source in a dense urban setting. An eddy covariance style tower and a low-cost sensor tower were deployed in various locations around an urban, hyper-local CO2/CH4 emissions source (a continuously measured restaurant exhaust vent). A model using different processing and statistical techniques was built to examine the most effective procedures for source isolation, directional location, and emission quantification. Using excess concentrations above a minimum baseline, we identify the source using bivariate polar plots and quantify the relationship between source size, receptor distance, and statistical proxies. Furthermore, we find that varying statistical thresholds allows for identification of less influential sources which are drowned out by larger or closer sources. Finally, we show that large sources can be effectively characterized using low-cost sensors, a valuable outcome informing how networks for monitoring larger areas could be implemented. This work may provide a basis for source identification and monitoring protocols for networks that feature sensors influenced by hyper-local point sources, subject to site-specific assumptions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Toward local core outlet temperature monitoring in gas-cooled nuclear reactors using distributed fiber-optic temperature sensors

Gas-cooled nuclear reactors operate at temperatures up to 950 °C with significant spatial variations in power generation and coolant flow through hundreds of parallel flow channels, resulting in complex mixing at the core outlet. The high temperatures and complex mixing can damage downstream components, challenge reactor calorimetry for power determination, and result in significant conservatism in calculated peak fuel temperatures, which ultimately limits the total power output. Directly measuring each gas stream individually is unrealistic using single point thermocouples, requiring more robust sensing techniques. Fiber-optic sensors are resilient to high temperatures (up to 1,000 °C) and radiation damage. Moreover, distributed measurements can be made along the length of one fiber, making them potential candidates for monitoring local core outlet temperatures to improve core calorimetry and identify hot channels. Here, to the authors’ knowledge, this manuscript is the first to report spatially distributed fiber optic temperature measurements to quantify mixing at the outlet of a relevant orifice plate under prototypic temperature and flow regimes to assess the feasibility of using fiber-optic sensors for distributed measurements of coolant temperature in gas-cooled reactors. A single optical fiber captured dynamic changes in local temperatures, whereas the mixed outlet temperature exhibited a muted response and could not identify which channels were responsible for the change in the mixed outlet temperature. The discussion focuses on potential challenges for deploying distributed optical fibers in gas-cooled reactors, including the effects of vibrations, radiation-induced signal attenuation and drift, and routing of the fiber while minimizing flow obstructions.

42 ENGINEERING↗