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At least 145 records · Page 8

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DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗

DOE OSTI2025

Casing Annulus Monitoring of CO 2 Injection Using Wireless Autonomous Distributed Sensor Networks

Effective and secure carbon subsurface storage, involving the deep underground injection of CO 2 into geological formations where it is permanently trapped, is paramount to mitigating CO 2 emissions (Figure I). Ensuring the integrity of these storage sites and detecting potential leakage through the casing annulus necessitates robust monitoring. This work provides the first integrated demonstration of a wireless casing-annulus monitoring architecture that can operate in highly attenuating cement-brine environments relevant to CO 2 storage. This project focused on developing and validating a novel sensor system for integration with autonomous monitoring near the cement reservoir interface. The goal was a fully integrated Technology Readiness Level (TRL) 4/5 field validation of a distributed wireless intelligent sensor system providing real-time, direct subsurface formation measurements to enhance fluid movement monitoring in the cemented casing annulus. Achieving this objective required the development and integration of 1) wireless autonomous microsensor technology by California Institute of Technology (Caltech); 2) sensor packaging and emplacement technology by Research Triangle Institute (RTI); and 3) smart well completions using wireless active casing collars and NOV pipe by the Sandia National Lab (SNL). The collaboration with the Caltech team in this project aimed to develop millimeter-scale radio frequency identification (RFID) sensors capable of detecting CO 2 , pH, and/or methane levels. These sensors are engineered to be impervious to fluids, allowing them to be mixed with cement and installed within the casing annulus. They operate using RFID protocols at frequencies of 902–928 MHz for both power and communication. A Sandia National Laboratories’ team engaged their expertise in the development of a Smart Collar system designed for the wireless data collection from these RFID sensors embedded in the cement annulus and transmission of this information to the ground surface via IntelliPipe/IntelliServ NOV drill pipe. This is accomplished through inductive coupling at the collar, which facilitates data transfer through each segment of the pipe. Because the system cannot transmit a direct current signal to power the Smart Collar, both power and communication were implemented using alternating current and electromagnetic signals at varying frequencies. Furthermore, the developed microsensor technology had to be demonstrated and validated in comparison with reference transducer measurements in a field test site at The University of Texas at Austin (UT-Austin). Although the full sensor suite did not reach field-deployment readiness, the system-level integration achieved in this project establishes a validated pathway for future incorporation of advanced microsensors.

47 OTHER INSTRUMENTATION↗

DOE OSTI2018

Investigation into the Development and Testing of a Simplex Capacitance Sensor for Rotor Tip Clearance Measurement in Turbo Machinery

The rotor tip clearance is a vital parameter to characterize turbomachinery performance. It is an essential measurement during engine development and testing. The efficiency of the major modules like fan, compressor and turbine depends on the optimum running clearances. The capacitive sensor is preferred for tip clearance measurements because of various inherent advantages. They are fabricated through complex processes for turbomachinery application and are expensive. These sensors are exposed to harsh engine environments and are likely to exhibit degraded performance. Insulation failure at sensing head, connector contamination and micro-cracks in the MI (Mineral Insulated) cable leads to noise in the signal output. Frequent mechanical failures are also seen due to work hardening of MI cables, lead connector detachments and failure of micro fins at interconnects. Fault fixing of these is tedious and very expensive. This paper presents the concerted effort in developing a simple capacitance sensor addressing potential sensor failure issues. The design features include simple construction, ease of sensor assembly, component realization and troubleshooting. The design also addresses all safety requirements. The sensor has a large sensing area (7.6 mm diameter) producing very stable, high-level signal. The developed sensor produces output that varies from 1 V{sub P-P} to 11 V{sub P-P} for blade-tip gaps between 3 mm to 0.4 mm respectively. Blade pass simulation studies across the sensor, display almost flat response at minimum clearance. This feature results in very minimal data acquisition errors. The sensor along with RC (Resistance-Capacitance) conditioning electronics was successfully used for clearance measurement on fan stage of gas turbine engine. Test results are in line with the predictions. Extensive tip clearance measurements on the engine validate the design. The current work provides an insight into the development of a simplex capacitance sensor for tip clearance measurement.

42 ENGINEERING↗

DOE OSTI2024

Deep Multitask Learning Models for Radiation Estimation at High Energy Accelerator Facility

Controlling the dose of radiation exposure in potential radioactive facilities is critical for ensuring the safety of staff and the public. Here, in this paper, we developed machine learning models to estimate radiation exposure efficiently at the Thomas Jefferson National Accelerator Facility (JLab), aiming to enhance safety in both accelerator facilities and public areas. Multiple sensors were deployed around the three experimental halls at JLab. Data on single-beam currents, energy levels, and radiation values at the sensor locations were collected during accelerator operation. We proposed a multi-task learning model for radiation estimation, utilizing either one-dimensional convolutional neural networks (1-D CNNs) or long short-term memory networks (LSTMs) as the backbone. The proposed model was trained to simultaneously estimate radiation levels at the sensor locations. Experimental results demonstrated that the proposed model with LSTM backbone achieved the best estimation performance, with an average R 2 score of 0.7557 for estimation within the same year and 0.7157 for estimation across different years. These results significantly surpassed those of competing models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Bisphenol-A sensors on polyimide fabricated by laser direct writing for on-site river water monitoring at attomolar concentration

This work presents an aptamer-based, highly sensitive and specific sensor for atto- to femtomolar level detection of bisphenol A (BPA). Because of its widespread use in numerous products, BPA enters surface water from effluent discharges during its manufacture, use, and from waste landfill sites throughout the world. On-site measurement of BPA concentrations in water is important for evaluating compliance with water quality standards or environmental risk levels of the harmful compound in the environment. The sensor in this work is porous, conducting, interdigitated electrodes that are formed by laser-induced carbonization of flexible polyimide sheets. BPA-specific aptamer is immobilized on the electrodes as the probe, and its binding with BPA at the electrode surface is detected by capacitive sensing. The binding process is aided by ac electroosmotic effect that accelerates the transport of BPA molecules to the nanoporous graphene-like structured electrodes. The sensor achieved a limit of detection of 58.28 aM with a response time of 20 s. The sensor is further applied for recovery analysis of BPA spiked in surface water. In conclusion, this work provides an affordable platform for highly sensitive, real time, and field-deployable BPA surveillance critical to the evaluation of the ecological impact of BPA exposure.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Validating Safecast data by comparisons to a U. S. Department of Energy Fukushima Prefecture aerial survey

Safecast is a volunteered geographic information (VGI) project where the lay public collects radiation measurements via hand-held sensors that are uploaded to a central server and then made freely available. However, Safecast data fidelity is uncertain given that the these sensors are hand assembled with various levels of technical proficiency, and that the sensors may not be properly deployed. We validated Safecast data gathered in the Fukushima Prefecture shortly after the Daiichi nuclear power plant catastrophe by comparing it with authoritative data collected by the Department of Energy (DOE) and the National Nuclear Security Administration (NNSA) during the same period. We found that the two data sets were highly correlated, though the DOE/NNSA observations were generally higher than the Safecast measurements. We concluded that this high correlation alone makes Safecast a viable data source for detecting and monitoring fallout during radiological emergencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2026

CHESS 2025: Spectrometer orthorectified at-sensor radiance from NEON AOP imaging spectroscopy surveys

This dataset provides Level 1 (L1) orthorectified at-sensor radiance derived from measurements collected by the Imaging Spectrometer-1 (NIS-1) onboard the NEON (National Ecological Observatory Network) Airborne Observation Platform (AOP) for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). NIS-1 captures light reflected from the Earth’s surface in 426 discrete wavelength bands as raw digital numbers (DNs; Level 0). These data are then calibrated to physical units (uW/cm²·sr·nm) following the processing steps described in the NEON Imaging Spectrometer Level 1B Calibrated Radiance Algorithm Theoretical Basis Document (ATBD; Gallery 2022). The data delivered here are the primary inputs for the surface reflectance product in “Custom surface reflectance, shade masks, and equivalent water thickness maps for the Colorado Headwaters Ecological Spectroscopy Study” (Carroll et al. 2026). For intertemporal comparison, the radiance data here are most directly relatable to the v2 radiance data in “NEON AOP Imaging Spectroscopy Survey of Upper East River Colorado Watersheds: Raw-Space Radiance and Observational Variable Dataset” (Goulden et al. 2018), to which the same processing methodology was applied. Together, the radiance and reflectance data enable users to exploit the unique reflection signatures of different surface objects for land cover classification, foliar trait mapping, plant vigor assessment, water content estimation, trace-element identification, and other scientific applications. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. Within each domain, data are delivered by flightline as orthorectified and calibrated hyperspectral rasters in Hierarchical Data Format version 5 (HDF5) format, with radiance values provided in uW/cm²·sr·nm on a fixed, uniform Universal Transverse Mercator (UTM) grid at 1 meter spatial resolution. The radiance rasters include all 426 NIS-1 spectral bands, along with associated quality-assurance (QA) and diagnostic and ancillary layers needed for atmospheric correction workflows. Orthorectified radiance is produced from pushbroom spectrometer observations by applying NEON’s radiometric calibration (including bad pixel masking, dark subtract, dark pedestal shift correction, electronic panel ghost correction, grating ghost correction, deblur correction and flat-fielding) and spectral calibration (using spectral response function band centers and full-width at half-maximum intensity), followed by geolocation and regridding to the fixed grid. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗

DOE OSTI2013

Development of an Integrated Raman and Turbidity Fiber Optic Sensor for the In-Situ Analysis of High Level Nuclear Waste - 13532

Stored nuclear waste must be retrieved from storage, treated, separated into low- and high-level waste streams, and finally put into a disposal form that effectively encapsulates the waste and isolates it from the environment for a long period of time. Before waste retrieval can be done, waste composition needs to be characterized so that proper safety precautions can be implemented during the retrieval process. In addition, there is a need for active monitoring of the dynamic chemistry of the waste during storage since the waste composition can become highly corrosive. This work describes the development of a novel, integrated fiber optic Raman and light scattering probe for in situ use in nuclear waste solutions. The dual Raman and turbidity sensor provides simultaneous chemical identification of nuclear waste as well as information concerning the suspended particles in the waste using a common laser excitation source. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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 OSTI2017

Wireless Chemical Sensor Systems (Optimization of Field-Structured Chemiresistors for in situ Quantification of Propellant Stabilizer Concentration)

This project is a Cooperative Research and Development Agreement (CRADA) with International Electronic Machines Corp. (IEM, Troy, NY), who is contracted through the U.S. Naval Air Systems Command (NAVAIR) (under SBIR N111-010) to develop an in situ wireless chemical sensor system called the Stabilizer Level Accurate Measurement System (SLAMS). The SLAMS device is specified to include a miniature, very thin, self-contained chemical-sensor node to detect and track the level of stabilizer present in various PADs and CADs. IEM is focused on the wireless sensor platform, node communication, and electronics; whereas Sandia National Laboratories’ (SNL’s) efforts are focused on the chemical sensor development and integration. The sensor technology being employed is known as field-structured chemiresistors (FSCRs), which was invented by James Martin, et al. (SNL) and refined by Martin and Read. These sensors are small, require very little power, and have tunable sensitivity and selectivity. In the following, we discuss SNL’s efforts to optimize the FSCRs for the demanding chemical and environmental conditions that will be encountered in this application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Resistive wall mode active control physics design for KSTAR

As KSTAR H-mode operation approaches the region where the resistive wall mode (RWM) can be unstable, an important issue for future long pulse, high beta plasma operation is to evaluate RWM active feedback control performance using a planned active/passive RWM stabilization system on the device. In particular, an optimal design of feedback sensors allows mode stabilization up to the highest achievable β{sub N} close to the ideal with-wall limit, β{sub N}{sup wall}, with reduced control power requirements. The computed ideal n = 1 mode structure from the DCON code has been input to the VALEN-3D code to calculate the projected performance of an active RWM control system in the KSTAR three-dimensional conducting structure device geometry. Control performance with the midplane locked mode detection sensors, off-midplane saddle loops, and magnetic pickup coils is examined. The midplane sensors measuring the radial component of the mode perturbation is found to be strongly affected by the wall eddy current. The off-axis saddle loops with proper compensation of the prompt applied field are computed to provide stabilization at β{sub N} up to 86% of β{sub N}{sup wall} but the low RWM amplitude computed in the off-axis regions near the sensors can produce a low signal-to-noise ratio. The required control power and bandwidth are also estimated with varied noise levels in the feedback sensors. Further improvements have been explored by examining a new RWM sensor design motivated by the off-midplane poloidal magnetic field sensors in NSTX. The new sensors mounted off of the copper passive stabilizer plates near the device midplane show a clear advantage in control performance corresponding to achieving 99% of β{sub N}{sup wall} without the need of compensation of the prompt field. The result shows a significant improvement of RWM feedback stabilization using the new sensor set which motivates a future feedback sensor upgrade.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Hybrid Plasmonic Coupled‐Mode Metasurface Design for Shot‐Noise Limited Ppb‐Level Hydrogen Detection

Plasmonic hydrogen sensors have enabled hydrogen detection below parts-per-million (ppm) range by boosting the sensitivity using localized surface plasmonic resonant (LSPR) structures. However, the intrinsic optical losses of Palladium (Pd), the primary plasmonic metal used for hydrogen detection, result in a low quality (Q) factor LSPR, which fundamentally hinders further improvement. In this work, a hybrid plasmonic metasurface is proposed that couples Pd-based LSPR structure with an Au film supporting surface plasmon polariton mode (Au-SPP). The coupled near-perfect absorber resonance yields a spectrally narrow, high Q response that retains strong sensitivity to hydrogen while improving resonance localization. Numerical analysis shows that, under shot-noise-limited conditions, the limit of detection (LoD) can be improved by over threefold compared to the state-of-the-art designs. Furthermore, this hybrid plasmonic coupled-mode metasurface thus presents a promising pathway to achieve parts-per-billion-level (ppb-level) hydrogen detection with enhanced spectral precision and robustness.

coupled modes↗

DOE OSTI2025

Cold Spray Cobalt Magnetostrictive Electromagnetic Acoustic Transducers for High Temperature Structure Monitoring

The Department of Energy’s Advanced Sensors and Instrumentation program seeks to develop and qualify advanced sensors for the nuclear industry. Reliable high temperature and high radiation sensors for detection and characterization of structural flaws in pipes, vessels, and structurally critical components is a weakness for both conventional light water reactors with coolant T-hot approaching 350oC, and for advanced reactors with T-hot temperatures in excess of 500 to 800oC. Magnetostrictive Electromagnetic Acoustic Transducers using a cold spray cobalt coating have been proposed as a sensor design that can withstand these kinds of temperatures and radiation levels to serve as online sensors to detect flaws before cracks, pits, or erosion/corrosion flaws progress to through-wall failures. This report tests cold spray cobalt as part of a magnetostrictive EMAT for high temperature service. Cobalt is known to have strong magnetostrictive properties however the effect of cold spray application is not well studied. This program was surprised to discover that cold sprayed cobalt exhibited little or no magnetostrictive behavior until it was thermally annealed. Following annealing to 650oC however, cold spray cobalt did exhibit a magnetostrictive response. Work to date prior to this milestone report publication showed that magnetostrictive EMAT was successfully tested to 400oC with an alnico permanent magnet. The program plans to extend testing with an electromagnet to higher temperatures. This follow-on work will be reported under subsequent publications or as a revision to this report.

Glass, Samuel W.↗

DOE OSTI2017

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.

fault detection↗

DOE OSTI2013

Development of an Integrated Raman and Turbidity Fiber Optic Sensor for the In-Situ Analysis of High Level Nuclear Waste

Raman, tank waste,, quatitative analysis, turbidit↗

DOE OSTI2024

Sixteen multiple-amplifier sensing charge-coupled devices and characterization techniques targeting the next generation of astronomical instruments

We present a candidate sensor for future spectroscopic applications, such as a Stage-5 Spectroscopic Survey Experiment or the Habitable Worlds Observatory. This type of charge-coupled device (CCD) sensor features multiple in-line amplifiers at its output stage allowing multiple measurements of the same charge packet, either in each amplifier or in the different amplifiers. Recently, the operation of an eight-amplifier sensor has been experimentally demonstrated, and we present the operation of a 16-amplifier sensor. This new sensor enables a noise level of ∼1 erms− with a single sample per amplifier. In addition, it is shown that sub-electron noise can be achieved using multiple samples per amplifier. In addition to demonstrating the performance of the 16-amplifier sensor, we aim to create a framework for future analysis and performance optimization of this type of detectors. New models and techniques are presented to characterize specific parameters, which are absent in conventional CCDs and Skipper CCDs: charge transfer between amplifiers and independent and common noise in the amplifiers and their processing.

16 multiple-amplifer sensing CCD (MAS-CCD)↗

DOE OSTI2018

Source Physics Experiment Data Quality Using Background Noise

We computed time-domain root-mean-square (TRMS) amplitudes and power spectral densities (PSD) from the Source Physics Experiment (SPE) seismic data using the 2018 version 1.4 of the instrument response metadata. The geophone instrument gains were set lower about a few weeks prior to the SPE shots to record on-scale and then reset soon afterward to record background noise and seismicity. When the gains were set lower, we determined that the 1-2 Hz bandpass filtered geophone amplitudes increased 20 decibels (dB) or factor of 10 relative to the geophone baseline or background noise levels from nearby broadband sensors. This indicates an inconsistency between the released metadata reporting a decrease in gain factor of 32 and the likely true geophone gain setting of 3.2. After the shots, the geophone gains were reset and recorded background noise levels similar to nearby broadbands consistent with the metadata for those time intervals. There was also a similar issue with line-3, line-4 and AF Trillium broadband horizontals gains during SPE-5 where there is a factor of 32 difference relative to baseline levels. The other Guralp and Trillium broadbands performed very well recording a 1-2 Hz noise level of -150 to -160 dB similar to the nearby station at Tonopah Springs, Nevada (US.TPNV) and compared with -170 dB from the new low noise model (NLNM) for the continental US (e.g., McNamara and Buland, 2004). The geophones were only 5-10 dB higher but performed as well as broadbands above 6 Hz. The accelerometers have poor low frequency response and were 40-50 dB higher in the 1-2 Hz band relative to broadbands.

58 GEOSCIENCES↗

DOE OSTI2019

Development of a Corrosion Monitoring System for Advanced Molten Salt Reactors

Molten salt reactors (MSRs) have been identified as one of several possible next-generation advanced reactor designs that could replace the aging fleet. There are many inherent advantages to MSRs, including higher power density, lower pressure, low stored energy, and prompt negative temperature reactivity coefficient Ezell et al. [2018]. However, there are challenges related to the operating temperatures, as well as, the corrosive salts attacking the structural materials. During the Molten Salt Reactor Experiment (MSRE), which was operated at Oak Ridge National Laboratory (ORNL) (Ignatiev et al. [2014], Forsberg et al. [2007], Forsberg et al. [2008], Delpech et al. [2009], Serp et al. [2014], Ignatiev and Surenkov [2017]) corrosion was monitored by sampling the salt for chromium. However, this type of monitoring does not identify the location of corrosion. In FY18, ORNL developed a novel in-situ corrosion sensor that could be placed on the outside of the structure to monitor changes in magnetic susceptibility of salt-wetted alloys as corrosion occurs Holcomb et al. [2019]. Initial testing with this novel sensor demonstrated a correlation between the change in signal output from the sensor to the various levels of pre-corroded test specimens. However, all initial testing was performed at room temperatures, which is not realistic for the harsh MSR environment. Based on this research, FY19 funding allowed for development of a high-temperature version of the sensor to be installed at various locations on the reactor infrastructure. The high-temperature sensor can operate at up to 750°C and can monitor development of corrosion inside the pipe. This sensor measures the increase in magnetic susceptibility that occurs as chromium dissolves from the inner surface of the pipe, when paramagnetic structural alloys become ferromagnetic with the loss of chromium. This report will discuss the development of corrosion monitoring sensor and the testing with results of corroded specimens at increasing temperatures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗