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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Leveraging The Open Provenance Model as a Multi-Tier Model for Global Climate Research

Global climate researchers rely upon many forms of sensor data and analytical methods to help profile subtle changes in climate conditions. The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program provides researchers with curated Value Added Products (VAPs) resulting from continuous sensor data streams, data fusion, and modeling. The ARM operations staff and software development teams (data producers) rely upon a number of techniques to ensure strict quality control (QC) and quality assurance (QA) standards are maintained. Climate researchers (data consumers) are highly interested in obtaining as much provenance (data quality, data pedigree) as possible to establish data trustworthiness. Currently all the provenance is not easily attainable or identifiable without significant efforts to extract and piece together information from configuration files, log files, codes, and status information from ARM databases. The need for a formalized approach to managing provenance became paramount with the planned addition of 120 new instruments, new data products, and data collection scaling to half a terabyte daily. Last year our research identified the need for a multi-tier provenance model to enable the data consumer easy access to the provenance for their data. This year we are leveraging the Open Provenance Model as a foundational construct that serves the needs of both the VAP producers and consumers, we are organizing the provenance in different tiers of granularity to model VAP lineage, causality at the component level within a VAP, and the causality for each time step as samples are being assembled within the VAP. This paper shares our implementation strategy and how the ARM operations staff and the climate research community can greatly benefit from this approach to more effectively assess and quantify VAP provenance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Microwave diagnostics and passive sensors for pipeline, well-bore, and boiler-tube monitoring

Materials, methods of making, and methods of using an integrated wireless detector for real time interrogating metallic tubular structures comprising: an RF patch antenna; a passive surface acoustic wave (SAW) sensor; and data analytic methodologies. An embodiment relates to interrogating a metallic structure having a uniform cross section using an antenna which launches electromagnetic radiation. A sensor may be located within the structure is configured to re-emit electromagnetic radiation modified depending on parameters for which the sensor has been functionalized. An antenna may receive radiation as modified by the sensor, or may receive the transmitted or backscattered radiation directly, without use of a sensor. The antenna then communicates wirelessly with an interrogator providing data which may be used to understand the operational status of the structure in real-time.

Ohodnicki, Paul R.↗
DOE OSTI2018

Technical Advances for Sustainable Remediation: In Situ Monitoring and Climate Resiliency - 18436

Recent advances in sustainable remediation techniques show promise as solutions for contaminant removal in soil and groundwater media. Although passive remediation (or natural attenuation) has gained momentum as an acceptable technique, there are still challenges in regulatory frameworks as well as in public acceptance. Leaving contaminants in subsurface requires increased burden of proof to show that plumes are stable and residual contaminants do not pose a significant health risk. Climate changes and frequent extreme climate events contribute another level of complexity and make it difficult to demonstrate system stability and predict future plume conditions. In this study, we show two coupled approaches to tackle this challenge: (1) in situ groundwater monitoring based on advanced sensor and data analytics technologies (i.e., Big Data), and (2) state-of-art modeling for optimizing monitoring and site closure strategies. In the new monitoring approach, in situ sensors are used to continuously measure the key variables that control contaminant plume mobility and its spatial and temporal distribution (such as pH, redox potential, electrical conductivity, and groundwater level). Coupled with a limited number of groundwater sampling, the data analytics methods - data mining and machine learning - allow us to estimate contaminant concentrations, and also to detect any significant changes associated with the plume mobility. In parallel, the state-of-art numerical flow and reactive transport model - ASCEM software under DOE Office of Environmental Management - provides a physical and mechanistic understanding of the system behavior, and predicts the long-term plume migration for optimizing the monitoring and closure strategies. In particular, we focus on the effects of extreme precipitation or drought on the site closure as well as monitoring strategies. We demonstrate this approach at the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

MULTI-MODAL global surveillance methodology for predictive and on-demand characterization of localized processes using cube satellite platforms and deep learning techniques

This paper presents the work completed towards the development of a multi-modal global surveillance methodology using cube satellite (CubeSat) platforms and novel data analysis techniques. A CubeSat system equipped with adequate sensors and data analytics capabilities can autonomously characterize various phenomena of interest on the Earth’s surface. CubeSats are advantageous over conventional satellites in certain remote monitoring applications because of their reduced construction costs (due to the availability of commercially-off-the-shelf components) and are easier to launch. The CubeSat surveillance system developed in this paper focused on phenomena of interest surrounding the nuclear fuel cycle in support of nuclear non-proliferation and emergency response. To observe the phenomena, a constellation of 3U and 6U CubeSats deployed from the ISS with adequate components was chosen. Four different sensor configurations were identified for remote sensing: panchromatic/multispectral in the visible and near-infrared spectrum, multispectral in infrared spectrum, hyperspectral in infrared spectrum, and multispectral in ultraviolet spectrum. While a panchromatic/multispectral sensor configuration has CubeSat flight heritage at the required spatial resolutions, the other three sensor types need future 3 development to meet signature and system requirements. Once each sensor onboard the CubeSat system collects data on a target of interest, the onboard computers would then apply the deep learning-based characterization methodology developed in this paper to identify phenomena. Four surrogate datasets containing representative simplified “images” were created for each sensor type to train the characterization methodology. A convolutional neural network was applied to each dataset and produced recall rates for the phenomena between 89.7% - 99.3% and precision rates between 92.3% - 99.9%. Each phenomenon’s presence probability from each network is then combined into a final characterization solution for a target area. This paper covers multiple interdisciplinary areas to develop the foundation for a CubeSat surveillance system focused on phenomena surrounding the nuclear fuel cycle.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

A digital twin platform for building performance monitoring and optimization: Performance simulation and case studies

Advancements in sensor technology, data analytics, affordable compute, and communication infrastructure have paved the way for Digital Twin technology in optimizing building operations and controls. This study presents the development of an open and interoperable web-based Digital Twin platform for integrating diverse data streams and facilitating effective user interactions. The platform utilizes modern technologies for the web framework and time-series data management, ensuring scalability and responsiveness. The backend supports seamless integration of diverse data sources and emulators, incorporating data from building sensors and meters, external weather Application Programming Interfaces, and advanced EnergyPlus simulation models of the building and its energy systems including the Distributed Energy Resources that are formulated in Functional Mockup Units. A simulation case study was conducted with FlexLab, a test facility on Lawrence Berkeley National Laboratory campus. The case study includes normal operations, Distributed Energy Resource integration, and power outage scenarios, to illustrate the Digital Twin’s ability to provide critical insights into energy performance and thermal resilience. The results demonstrated the platform’s potential as a decision-support tool for optimizing building energy performance and enhancing resilience against extreme weather events. Future work will focus on deploying the Digital Twin platform to a real building for field validation, extending its capabilities to cover more scenarios such as bidirectional Electric Vehicle interactions, and enhancing user engagement.

EnergyPlus↗
DOE OSTI2024

Leveraging Hydropower Multi-Sensor Data for Inference and Age-Informed Modeling

Increased demand of operational flexibility such as faster ramp up/down in generation, and more frequent start/stops are putting hydropower plants and their associated components in unprecedented stress. Consequently, these plants are at the high risk of extended and more frequent outage to accommodate unscheduled, and unexpected maintenance. Therefore, hydropower plants are in critical need of data driven and age-informed analysis for their regular and unscheduled operation. Yet not all hydropower plants are exhaustively equipped with sensors and/or measurement streams for their respective components – demanding solutions on how to detect, identify, and locate the cause of any event from the unobservable. Idaho National Laboratory (INL) analyzed the anonymized measurements and event records from the Hydropower Research Institute (HRI) to address this issue, as part of the Water Power Technologies Office (WPTO) funded one year multi-lab project. First, we investigated how time series of multiple sensor measurements can be leveraged to identify an event “root cause” as well as to develop an inference (i.e., estimate the unobservable) problem. INL also investigated how individual hydropower components’ reaction or response times vary across the pre-event, during event, and post-event conditions – enabling the hydropower dynamic models to be age-informed. Finally, the impact of clustering multi-sensor time series on short-term vibration prediction is analyzed. INL will present key findings from these analyses and recommend next steps for stakeholder adoption.

13 HYDRO ENERGY↗
DOE OSTI2017

Distributed wireless sensing for fugitive methane leak detection

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2017

Distributed wireless sensing for methane leak detection technology

Large scale environmental monitoring requires dynamic optimization of data transmission, power management, and distribution of the computational load. In this work, we demonstrate the use of a wireless sensor network for detection of chemical leaks on gas oil well pads. The sensor network consist of chemi-resistive and wind sensors and aggregates all the data and transmits it to the cloud for further analytics processing. The sensor network data is integrated with an inversion model to identify leak location and quantify leak rates. We characterize the sensitivity and accuracy of such system under multiple well controlled methane release experiments. It is demonstrated that even 1 hour measurement with 10 sensors localizes leaks within 1 m and determines leak rate with an accuracy of 40%. This integrated sensing and analytics solution is currently refined to be a robust system for long term remote monitoring of methane leaks, generation of alarms, and tracking regulatory compliance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Coupling Big Data Analytics and Reactive Transport Modeling for Cost-effective Groundwater Monitoring - 17163

This study presents an innovative approach for sustainable and cost-effective groundwater monitoring. This approach takes advantage of recent advances in various technologies: (1) in situ autonomous sensors, (2) big data analytics, and (3) parallel high-performance computing for flow and reactive transport modeling. In situ sensors are used to periodically measure the key variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control contaminant mobility and the plume spatial and temporal distribution. Based on a limited number of groundwater sampling, the data analytics methods-data mining and machine learning-allow us to identify and quantify the correlations between the in situ-measured variables and contaminant concentrations, and also to detect significant changes associated with the plume mobility. In addition, a state-of-the-art parallel numerical flow and reactive transport simulator Amanzi, and uncertainty quantification software Agni are used to provide an improved physical and mechanistic understanding of the contaminated groundwater system behavior, and to predict the long-term plume distribution for optimizing and adapting the monitoring strategy. Amanzi and Agni were developed as part of the Advanced Simulation Capability for Environmental Management (ASCEM) program of the DOE Office of Environmental Management. Such modeling is critical, particularly, for assessing the impact of climate change and associated hydrological shifts. The developed approach is expected to significantly reduce the groundwater sampling frequency and associated cost. In addition, the real-time information on plume mobility serves as an early warning system, improving the resiliency of contaminated or potentially contaminated sites. We demonstrate this approach using as an example the Savannah River Site (SRS) F-Area, where groundwater is contaminated by various radionuclides, including uranium, tritium and technetium. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Plasmonic Conducting Metal Oxide-Based Optical Fiber Sensors for Chemical and Intermediate Temperature-Sensing Applications

The demand for real-time sensors in harsh environments at elevated temperature is significant and increasing. In this manuscript, the chemical and temperature sensing using the optical response through the practical fiber platform is demonstrated, and principle component analysis is coupled with targeted experimental film characterization to understand the fundamental sensing layer properties, which dominate measured gas sensing responses in complex gas mixtures. More specifically, tin-doped indium oxide-decorated sensors fabricated with the sol–gel method show stable and stepwise transmission responses varying over a wide range of H2 concentration (5–100%) at 250–350 °C as well as responses to CH4 and CO to a lesser extent. Measured responses are attributed to modifications to the surface plasmon resonance absorption in the near-infrared range and are dominated by the highest concentrations of the most-reducing analyte based upon systematic mixed gas stream experiments. Principal component analysis is utilized for this type of sensor to improve the quantitative and qualitative understanding of responses, clearly identifying that the dominant principle component (PC #1) accounts for ~78% of total data variance. Correlations between PC #1 and the experimentally derived free carrier concentration confirm that this material property plays the strongest role on the ITO gas sensing mechanism, while correlations between the free carrier mobility and the second most important principle component (PC #2) suggest that this quantity may play a significant but secondary role. As such, the results presented here clarify the relationship between generalized principle components and fundamental sensing materials properties thereby suggesting the pathway toward improved multicomponent gas speciation through sensor layer engineering. Furthermore, the work presented represents a significant step toward the ultimate objective of optical waveguide sensors integrated with multivariate data analytics for multiparameter monitoring with a single sensor element.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Qualification of Daiichi Units 1, 2, and 3 Data for Severe Accident Evaluations - Process and Illustrative Examples from Prior TMI-2 Evaluations

The accidents at the Three Mile Island Unit 2 (TMI-2) Pressurized Water Reactor (PWR) and the Daiichi Units 1, 2, and 3 Boiling Water Reactors (BWRs) provide unique opportunities to evaluate instrumentation exposed to severe accident conditions. Conditions associated with the release of coolant and the hydrogen burn that occurred during the TMI-2 accident exposed instrumentation to harsh conditions, including direct radiation, radioactive contamination, and high humidity with elevated temperatures and pressures. As part of a program initiated in 2012 by the Department of Energy Office of Nuclear Energy (DOE-NE), a review was completed to gain insights from prior TMI-2 sensor survivability and data qualification efforts. This initial review focused on the set of sensors deemed most important by post-TMI-2 instrumentation evaluation programs. Instrumentation evaluation programs focused on data required by TMI-2 operators to assess the condition of the reactor and containment and the effect of mitigating actions taken by these operators. In addition, prior efforts focused on sensors providing data required for subsequent forensic evaluations and accident simulations. To encourage the potential for similar activities to be completed for qualifying data from Daiichi Units 1, 2, and 3, this report provides additional details related to the formal process used to develop a qualified TMI-2 data base and presents data qualification details for three parameters: primary system pressure; containment building temperature; and containment pressure. As described within this report, sensor evaluations and data qualification required implementation of various processes, including comparisons with data from other sensors, analytical calculations, laboratory testing, and comparisons with sensors subjected to similar conditions in large-scale integral tests and with sensors that were similar in design to instruments easily removed from the TMI-2 plant for evaluations. As documented in this report, results from qualifying data for these parameters led to key insights related to TMI-2 accident progression. Hence, these selected examples illustrate the types of activities completed in the TMI-2 data qualification process and the importance of such a qualification effort. These details are documented in this report to facilitate implementation of similar process using data and examinations at the Daiichi Units 1, 2, and 3 reactors so that BWR-specific benefits can be obtained.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

TMI-2 – A Case Study for PWR Instrumentation Performance During a Severe Accident

The accident at the Three Mile Island Unit 2 (TMI-2) reactor provided a unique opportunity to evaluate sensors exposed to severe accident conditions. Conditions associated with the release of coolant and the hydrogen burn that occurred during this accident exposed instrumentation to harsh conditions, including direct radiation, radioactive contamination, and high humidity with elevated temperatures and pressures. As part of a program initiated in 2012 by the Department of Energy Office of Nuclear Energy (DOE-NE), a review was completed to gain insights from prior TMI-2 sensor survivability and data qualification efforts. This new effort focussed upon a set of sensors that provided critical data to TMI-2 operators for assessing the condition of the plant and the effects of mitigating actions taken by these operators. In addition, the effort considered sensors providing data required for subsequent accident simulations. Over 100 references related to instrumentation performance and post-accident evaluations of TMI-2 sensors and measurements were reviewed. Insights gained from this review are summarized within this report. For each sensor, a description is provided with the measured data and conclusions related to the sensor’s survivability, and the basis for conclusions about its survivability. As noted within this document, several techniques were invoked in the TMI-2 post-accident evaluation program to assess sensor status, including comparisons with data from other sensors, analytical calculations, laboratory testing, and comparisons with sensors subjected to similar conditions in large-scale integral tests and with sensors that were similar in design but more easily removed from the TMI-2 plant for evaluations. Conclusions from this review provide important insights related to sensor survivability and enhancement options for improving sensor performance. In addition, this document provides recommendations related to the sensor survivability and data evaluation process that could be implemented in upcoming Fukushima Daiichi recovery efforts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

TMI-2 - A Case Study for PWR Instrumentation Performance during a Severe Accident

The accident at the Three Mile Island Unit 2 (TMI-2) reactor provided a unique opportunity to evaluate sensors exposed to severe accident conditions. Conditions associated with the release of coolant and the hydrogen burn that occurred during this accident exposed instrumentation to harsh conditions, including direct radiation, radioactive contamination, and high humidity with elevated temperatures and pressures. As part of a program initiated in 2012 by the Department of Energy Office of Nuclear Energy (DOE-NE), a review was completed to gain insights from prior TMI-2 sensor survivability and data qualification efforts. This new effort focused upon a set of sensors that provided critical data to TMI-2 operators for assessing the condition of the plant and the effects of mitigating actions taken by these operators. In addition, the effort considered sensors providing data required for subsequent accident simulations. Over 100 references related to instrumentation performance and post-accident evaluations of TMI-2 sensors and measurements were reviewed. Insights gained from this review are summarized within this report. For each sensor, a description is provided with the measured data and conclusions related to the sensor’s survivability, and the basis for conclusions about its survivability. As noted within this document, several techniques were invoked in the TMI-2 post-accident evaluation program to assess sensor status, including comparisons with data from other sensors, analytical calculations, laboratory testing, and comparisons with sensors subjected to similar conditions in large-scale integral tests and with sensors that were similar in design but more easily removed from the TMI-2 plant for evaluations. Conclusions from this review provide important insights related to sensor survivability and enhancement options for improving sensor performance. In addition, this document provides recommendations related to the sensor survivability and data evaluation process that could be implemented in upcoming Fukushima Daiichi recovery efforts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities

Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Data-Driven Preemptive Voltage Monitoring and Control Using Probabilistic Voltage Sensitivities: Preprint

Increased penetration of distributed variable re-newable generation may cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But due to the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network is needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69 node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Analytical Functions for 200 West Pump-and-Treat SCADA Sensor Data

Historical operations at the U.S. Department of Energy’s Hanford Site included disposal of waste fluids to the subsurface in the 200 West Area on the Hanford Central Plateau. Subsequent infiltration of fluids has resulted in groundwater contamination with carbon tetrachloride, nitrate, uranium, technetium-99, and other contaminants. A pump-and-treat (P&T) system, with an extraction/injection well network and an aboveground treatment plant, was implemented as part of interim and final remedies in the 200 West area. The HYPATIA single-page web application (part of the SOCRATES suite) is being developed to provide access to and analysis of chemistry and treatment facility sensor data for this 200 West P&T system. For the web application, analytical algorithms were developed to perform summing, differencing, smoothing, outlier detection, change-point detection, mass flow rate, and injectivity calculations on the data. Candidate algorithms were identified and tested, with the best-performing algorithms then assembled for implementation in HYPATIA. Because HYPATIA is hosted on the Amazon Web Services (AWS) cloud computing platform, algorithms were implemented in a back-end AWS Lambda function that can be called by the HYPATIA front end. The Lambda function applies the requested data processing to specified data via functions written in R, Python, and JavaScript. Development, testing, and review of the data analysis algorithms was completed under an NQA-1 quality program. This new HYPATIA functionality will provide information to support site decisions regarding P&T system performance and optimization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗