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At least 595 records · Page 33

Noncontact Monitoring of Overhead Transmission Lines Using Space Potential Phasor Measurements

Power system reliability requirements necessitate the development of measurement systems to monitor the operational state of critical transmission lines. Here in this article, a novel noncontact method for monitoring overhead transmission lines is proposed. The method is based on measuring the amplitude and relative phase of the transmission line's space potential at two or more strategically located points in space along with the use of passive phase shifters to enhance the sensitivity of the measurements. Applications of voltage unbalance and conductor sag measurements are demonstrated along with the results of conductor sag measurements on an operating 230-kV transmission line. It is shown that using enhanced relative phase measurements can significantly improve the sensitivity of the measurements.

47 OTHER INSTRUMENTATION↗

Performance of High Stopping Power Bismuth-Loaded Plastic Scintillators for Radiation Portal Monitors

Plastic scintillators are widely used in radiation portal monitors because of their low cost and availability in large sizes. However, due to their low density and low effective atomic number (Z), they offer low intrinsic efficiency and little spectroscopic information. The addition of high-Z constituents to these plastics can greatly increase both their total stopping power and the amount of photoelectric absorption, leading to full-energy deposition and thus useful gamma spectra. Herein, we present the performance of the latest formulation of Bi-loaded plastic scintillators showing their useful spectroscopic information up to relatively high energy (~1 MeV) due to their high stopping power compared to the current commercially available plastics. These Bi-loaded plastics use 20 weight percent (wt%) Bi-pivalate (8 wt% elemental Bi) dissolved in polyvinyltoluene (PVT) matrix and conventional fast fluors (~10 ns decay time). These Bi-loaded plastics achieve up to approximately 6000 photons/MeV and have been produced in sizes up to 17 in 3 . The performance of these Bi-loaded plastics is also demonstrated in the existing portal monitor hardware (Rapiscan Model TSA Trainer 770) showing the possibility to provide improved sensitivity as a drop-in replacement with continued scale-up.

42 ENGINEERING↗

Circular Trajectory Approach for Online Sinusoidal Signal Distortion Monitoring and Visualization

The increasing complexity and uncertainties of modern power systems are placing significant demands on signal monitoring techniques. This work proposes the Circular Trajectory Approach (CTA) for online sinusoidal signal distortion monitoring and visualization. CTA can detect distortions of a sinusoidal signal. Compared with existing waveform anomaly detection techniques, CTA is faster in detection and less computation intensive. It thus supports edge devices and online applications. CTA also offers a new means of sinusoidal signal distortion visualization. It can reveal the distorted sections in a sinusoidal cycle and clearly display the distortions. The proposed approach is tested on real data from an open source EPRI dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗

DEVELOPMENT AND DEMONSTRATION TESTBED FOR THE REMOTE OPERATIONS AND MONITORING OF MICROREACTORS

The nuclear industry is rapidly developing many advanced-reactor concepts for near-term deployment in both traditional and non-traditional nuclear-powered applications. One such category of advanced reactor is the microreactor, a class of reactor with less than 20MWth power output, intended for applications where the economics or logistics of traditional power sources are difficult. This includes applications such as remote communities, mining sites, defense installations, or humanitarian and disaster-relief missions. One key enabling feature for the successful deployment of microreactors is a remote operations capability. Remote operations provide monitoring and control capabilities which can significantly reduce staffing costs by eliminating the need for licensed operators at each reactor facility and improve the economic viability for microreactor deployment. A remote concept of operations is not currently an established capability in the nuclear industry. In addition, no demonstration microreactor is expected to complete construction or go critical until at least 2026. This leaves two major capability gaps: the successful demonstration of a remote concept of operations for microreactors and a test bed suitable for said demonstration. Both gaps must be addressed in order to advance the remote concepts of nuclear operation and, more broadly, microreactors themselves from paper to reality. This paper aims to fill these gaps and describes a test bed that would support development and deployment of a remote concept of nuclear operations, initial experimental results from that test bed, and the application of the test bed and experimental results for a digital-twin-based remote concept of operations underdevelopment at Idaho National Laboratory (INL). The platform chosen as a remote concept of nuclear operations test bed is the Single Primary Heat Extraction and Removal Emulator, known as SPHERE, located at INL. SPHERE is a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The small-scale and non-nuclear nature of SHPERE limit safety concerns associated with remote operations while still providing the physical response representative of a microreactor. A network connection was added to SPHERE that enables remote-monitoring capability. This allows for real-time data streaming to networked workstations, data historians, and human-machine interfaces (HMIs). These are all critical components in a remote concept of operations, thus providing a robust development and demonstration platform. An initial experiment was performed using the SPHERE remote operations testbed. This included running a comprehensively instrumented SPHERE through a series of steady-state and transient operating scenarios in both normal and abnormal operating conditions, all while streaming live test data to a remote HMI and data warehouse. This initial experiment served three purposes: (1) characterizing the response of SPHERE, (2) demonstrating the remote connection to SPHERE, and (3) providing a baseline data set for development of a digital-twin-based remote concept of operations that is under development at INL.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗

EGS Collab Experiment 2: Shear Stimulation ERT Monitoring Data

This repository contains the electrical resistivity tomography (ERT) monitoring data that was collected before, during, and after shear stimulation attempts were conducted at EGS Collab. These tests were carried out on the SURF 4100 level during Experiment 2 in March, 2022. Flow and stimulation data corresponding to this dataset are available in a different GDR dataset, which is linked below. Also included here are the E4D input files that were used to process the ERT data. E4D is an open source ERT monitoring and inversion code which is linked for use below.

15 GEOTHERMAL ENERGY↗

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗

Sensors with Intelligent Measurement Platform and Low-cost Equipment (SIMPLE) for Monitoring and Control of Medium Voltage Distribution Systems with High Penetration of Intermittent Distributed Energy Resources

The primary focus of the SIMPLE project was to develop, commission, test and introduce intelligent voltage and current sensor platform with enhanced characteristics (accuracy, bandwidth and harmonic range) and high measurement granularity for medium voltage distribution system monitoring, protection and control. The enhancements incorporated existing (commercially available) sensors and made them suitable for applications such as voltage regulation, frequency support, fault detection and location, distribution system state estimation, power quality measurements and electrical distribution network topology processing. The development of the intelligent sensor platform was driven by the need for high-fidelity monitoring and control of medium voltage distribution feeder to enable higher penetration of renewable resources and microgrid operation.

14 SOLAR ENERGY↗

Reference document for LANL stack sampling and ANSI N13.1 (Article) Gielow RL and McNamee MR 1993. Numerical Flue Gas Flow Modeling for Continuous Emissions Monitoring Applications. EPRI CEM Users Group Meeting. Baltimore. RP1961-13

American National Standard N13.1 “sets forth guidelines and performance criteria for sampling the emissions of airborne radioactive substances in the air discharge ducts and stacks of nuclear facilities. Emphasis is on extractive sampling from a location in a stack or duct where the contaminant is well mixed. At such a location, sampling may be conducted at a single point. This standard provides performance-based criteria for the use of air sampling probes, transport lines, sample collectors, sample monitoring instruments, and gas flow measuring methods. This standard also covers sampling program objectives, quality assurance issues, developing air sampling action levels, system optimization, and system performance verification. Workplace, containment, and environmental air monitoring are not addressed. Specific sample analysis methods and the reporting or interpreting of results are also not addressed.” (HPS 2011).

61 RADIATION PROTECTION AND DOSIMETRY↗

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

H-Canyon Dissolver Offgas Monitoring Using Raman Spectroscopy

This report describes the development and in facility demonstration of a Raman spectrometer to monitor known aluminum offgas components generated during the dissolution of Spent Nuclear Fuel at the Savannah River Site H-Canyon facility. The presence of these gases is an indicator that material is actively being dissolved. This process monitoring technique, which measures the offgas in real time, has the potential to improve the efficiency of facility operations. If the dissolution time is too short, time is wasted due to extended dissolution periods with additional probing of the dissolver pot to confirm that fragment levels are below the thresholds for continuing to the next charge or moving the dissolvent to a receiving tank. If the dissolution time is too long, time and energy are wasted.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Analysis and Recommendation of Alpha-Beta Continuous Air Monitor Alarm Setpoints for the RPL Stack Exhaust

The Radiochemical Processing Laboratory (RPL) alpha-beta continuous air monitor (CAM) is used for real-time detection of “artificial” alpha-beta particulates from the stack exhaust. The CAM interfaces with software developed by the Pacific Northwest National Laboratory—called the “PNNL OS3300 Alpha-Beta Monitoring Software”—that provides real-time estimates of alpha-beta air concentrations and integrated activities that are calculated from measured CAM counts. The OS3300 software has alarm setpoints that can be used to provide an early indication of larger releases, that if allowed to persist, could approach defined dose limits. This report performs a detailed review of historical and current alarm setpoints used at RPL, including discussion of the technical basis used in their development, analysis of alarm frequencies using measured historical data, and performs a detailed dose assessment using more realistic release scenarios, meteorology, and adjustment factors used in estimating released stack activity. Based on the results, the alpha and beta air concentration alarm setpoints will remain 1.77 × 10 -8 µCi/ml and 3.47 × 10 -8 µCi/ml, respectively; and the alpha and beta integrated activity alarm setpoints will remain 70.22 µCi and 137.63 µCi, respectively. These setpoints achieve the right operational balance in identifying larger releases from planned radiological work at RPL, without being overly conservative so as to cause nuisance alarming. Furthermore, implied doses associated with these setpoints are below defined and regulatory limits.

325RPL↗

Analysis and Recommendation of Tritium Gas Continuous Air Monitor Alarm Setpoints for the RPL Stack Exhaust

The Radiochemical Processing Laboratory (RPL) tritium continuous air monitor (CAM) is used for near real-time detection of tritium gas (i.e., elemental tritium [HT] and tritiated water vapor [HTO]) in the stack exhaust. The CAM interfaces with software developed by the Pacific Northwest National Laboratory (PNNL)—called the “PNNL OS3700 Tritium Monitoring Software”—that provides near real-time estimates of tritium gas air concentrations and integrated daily activities that are calculated from measured CAM counts. The OS3700 software implements alarm setpoints to alert the facility of larger tritium gas releases that if allowed to persist, could begin to challenge permitted emission and established dose constraints. This report performs a detailed review of historical and current tritium alarm setpoints used at RPL, including discussion of the technical basis used in their development, analysis of alarm frequencies using measured historical data, and performs a detailed dose assessment using more realistic release scenarios and meteorology. Based on the results, the tritium air concentration and integrated daily tritium activity alarm setpoints will remain 2.0 × 10 -5 µCi/ml and 25 Ci/day, respectively. These setpoints achieve the right operational balance in identifying larger releases from planned tritium work at RPL, without being overly conservative so as to cause nuisance alarming. Furthermore, implied doses associated with these setpoints are well below defined and regulatory limits.

325RPL↗

Assessment of Acoustic Sensor Application to Structural Health Monitoring of Reactor Components

The objectives of SHM of advanced fission reactors include the following: (1) Maintain safe, reliable, and efficient operation of structures, systems, and components in accordance with design intent; (3) Reduce cost; (4) Improve the comprehensive life management of structures, systems, and components; and (5) Extend the operational life of a power system through retirement for cause. Advanced reactor designs currently being considered are expected to operate at higher temperatures than light water-cooled reactors and to support missions beyond baseload electricity generation. Methods to monitor and detect for these mechanisms will require structural health monitoring (SHM) techniques, with ultrasonic methods an ideal candidate given their widespread use for NDE. This document summarized the state of technology for ultrasonic technologies, as part of an assessment of technology gaps and needed research. The great majority of sensor development to date addresses elevated temperatures and radiation tolerance, and more work is needed in both areas. Little data exists regarding corrosion effects of advanced coolants on sensor materials and couplants. Of high importance is the need for ARDs and regulatory bodies to engage and define what level of SHM is needed for autonomous or semi-autonomous control of reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Roadmap for the Completion of the Design and Implementation of a Remote Canister Monitoring System

The Office of Environmental Management within the Department of Energy is currently investigating technical issues associated with extended dry storage (50+ years) of aluminum spent nuclear fuel. As part of these efforts, a remote canister-monitoring system (RCMS) has been proposed to collect the real-time data of the environmental conditions in the canisters used for the dry storage of aluminum spent nuclear fuel in the CPP-603 facility at Idaho National Laboratory (INL). The proposed RCMS would provide the opportunity to evaluate the appropriate technologies for monitoring, to collect canister environment conditions as soon as possible, to verify and validate current laboratory-based study results and analytic modeling approaches, and to potentially identify additional dry storage options for aluminum spent nuclear fuel at the INL Site. The document provides a brief update on the progress that has been made toward deploying the RCMS and will present at a high level, the general system design philosophy behind the project and the key future tasks necessary for the completion of the RCMS system design, fabrication of the RCMS prototype, testing, and eventual deployment of the RCMS, and a tentative timeline for these tasks.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Concrete Structure Health Monitoring Using Vibro-acoustic Testing and Machine Learning

Assessment and management of aging concrete structures in nuclear power plants require a more systematic approach than simple reliance on margins of safety in existing codes. Research is required to deploy online monitoring capability to assess the health of concrete structures in nuclear power plants and perform predictive analytics based on heterogeneous measurements, in order to reduce the operation and maintenance costs. The research effort described in this report focuses on the health monitoring of concrete slabs containing reactive aggregates using the vibro-acoustic modulation (VAM) technique and predictive modeling. The research is developing an approach to enable detection, localization, and estimation of the extent of degradation in concrete slabs due to alkali-silica reaction (ASR). The project outcomes are generic and extendable to other degradation modes in concrete. A concrete slab without reinforcements was cast at Vanderbilt University and cured in aggressive conditions to accelerate ASR degradation. In this report, we develop a methodology for the automation of VAM testing, and the construction of machine learning models to facilitate damage localization in concrete structures. We investigate how machine learning models based on physics simulation data can be effectively utilized for damage localization (damage location and depth) and estimation in a concrete slab specimen. Our study indicates that these models could potentially support damage localization and estimation for NPP structures.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Survey of Neutron Flux Sensors for Monitoring Advanced Reactor Concepts Operating with Low Neutron Fluence Rate and Extended Uninterrupted Lifespans

The purpose of this report is to provide a survey of neutron sensors applicable to the low power levels (low neutron fluence rate) for reactor monitoring and controls. A survey of detectors for reactor power monitoring and controls from various commercial vendors—Reuter Stokes, Framatome, Exosens, and Mirion—is presented in the following sections based on application of reactor power ranges. Detector specifications are provided where available per each of the vendor’s catalogues and specification sheets found on their websites. While this is not an all encompassing list, it provides an example of detector options. Fission chambers and self powered neutron detectors (SPNDs) for in-core application with changes to the neutron-sensitive materials—fissile depots for fission chambers and emitters for SPNDs—for regenerative capabilities will be discussed in the latter sections.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗