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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Chapter 8: Life-Cycle Testing and Analysis

Prior to a spacecraft launch, program mission assurance standards dictate that the flight battery power system should comply with mission requirements under the intended operating conditions. Ground life cycle testing (LCT) combined with an analysis on the electrical power system (EPS) battery is an empirical method used to demonstrate compliance to satellite service life requirements. The LCT compliance method adopted by the aerospace industry is based on demonstrating a space battery's life expectancy as a part of battery qualification. Real-time cell and lithium-ion battery (LIB) LCT data are commonly used for model inputs to EPS power and energy balance analyses, in LIB reliability analysis estimates, and to support on-orbit spacecraft mission life extension opportunities. This chapter describes the LCT planning steps, process approach, and analysis techniques commonly used to qualify space LIB power systems.

accelerated aging↗

DOE OSTI2025

High Power FPC Progress for EIC ESR Cavities

Electron-Ion Collider (EIC) is a next generation particle accelerator to be built at Brookhaven National Laboratory, in partnership with Thomas Jefferson National Accelerator Facility. In Electron Storage Ring (ESR), 18 single-cell 591 MHz SRF cavities are required to compensate for up to 10 MW energy loss due to synchronic radiation. Two high power FPCs for each cavity are used to deliver up to 800 kW power to the beam. The high power FPC were designed and reviewed. The FPC prototypes will be ready for high power test around mid-2026. This paper presents the latest development of FPC prototyping and path forward for FPC conditioning.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

COR-0011 Rev 6 MARVEL Project Code of Record

This Code of Record identifies the codes, standards, and procedures necessary to design, develop, construct, and startup the Microreactor Applications Research Validation and Evaluation (MARVEL) Project at the Materials and Fuel Complex (MFC) Transient Reactor Test (TREAT) Facility and the Idaho National Laboratory (INL). The MARVEL Project is an INL test microreactor funded by the United States Department of Energy (DOE) via the Microreactor Program (MRP). The goal of the project is to establish an operational nuclear applications test bed that can generate combined heat and power to enable integration and R&D with end-user technologies, as well as allow microreactor technologists to test next-generation control systems. The microreactor is a thermal reactor utilizing Uranium Zirconium Hydride (UZrH) fuel with review and authorization by the Department of Energy Idaho Operations Office (DOE-ID) for National Environmental Policy Act (NEPA) compliance, safety review, and supplemental readiness assessments for startup and operation. To enable rapid deployment, the MARVEL reactor will reside in the Transient Reactor Test (TREAT) Facility and utilize the existing operating Category B reactor facility, approved facility safety basis, operating crews, and recent re-start experience.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗

DOE OSTI2023

ASC L2 Milestone Review CTS-2 Production Readiness [Slides]

Open and secure CTS-2 are ready for production capability to include accessibility for users and integration with existing storage and network environment. Hardware testing, early user testing is accomplished, and the systems are generally available for ASC production users.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Let’s Get Real: Are Wearable Plant Sensors Ready for Crop Monitoring?

In recent years, the number of publications describing new and exciting developments in wearable plant sensors (WPSs) has skyrocketed. These small, lightweight sensors hold promise to assist precision agriculture and may thus help reduce crop losses, increase resource use efficiency, and automate crop production. However, WPSs are often not adequately tested in environments relevant for crop growth, and the majority of experimental WPS studies reveal a glaring lack of basic knowledge of plant biology. This review aims to bridge the communication gap between WPS developers and the wider plant research community by (1) providing essential physiological and environmental background information for engineers in relation to WPS sensing capabilities, (2) offering a step-by-step guide to conduct sensor tests on plants correctly, and (3) highlighting potential challenges and suggesting WPS applications in the open field, greenhouses, and vertical farming systems. We hope this review facilitates the development of WPSs and guides them to be truly “ready for the world”.

crop monitoring↗

DOE OSTI2025

Non-destructive evaluation and machine learning methods for inspection of spent nuclear fuel canisters: A state-of-the-art review

Nuclear energy is among the cleanest and most efficient energy sources currently available. The operation of nuclear power plants (NPPs) produces large amounts of high-level radioactive waste known as spent nuclear fuel (SNF). Currently, large amounts of SNF is stored in dry cask storage systems (DCSSs) for extended interim storage until a permanent disposal solution becomes available. During the extended interim storage, the DCSS, particularly the SNF canisters, may degrade and abnormal conditions may occur. Therefore, non-destructive evaluation (NDE) and machine learning (ML) approaches are necessary for inspection of SNF canisters. This paper presents a state-of-the-art review of literature by summarizing recent progress made on the applications of NDE and ML for inspection of SNF canisters. Sixteen NDE methods are examined and compared: visual inspection, ultrasonic guided waves (UGWs), laser-based approaches, acoustic emission (AE), eddy current testing (ECT), non-invasive acoustic sensing, dynamic modal testing, cosmic ray muons tomography, neutron imaging, gamma rays detection, fiber optical sensors, through-wall communications, X-ray computed tomography (CT), vibrothermography, monoenergetic photon sources, and surface acoustic wave (SAW) sensors. The technology readiness level (TRL) for each method is assessed and compared. Recent publications on ML-enhanced visual inspection, AE, non-invasive acoustic sensing, dynamic modal testing, and neutron imaging for SNF canisters are summarized and future research needs are identified. In conclusion, this review article provides a convenient reference on the state-of-the-art applications of NDE and ML methods for inspection of SNF canisters.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

2023 Project Peer Review Report

The Bioenergy Technologies Office (BETO) within the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy supports the research, development, and demonstration (RD&D) of technologies aimed at mobilizing domestic renewable carbon resources for the reduction of greenhouse gas emissions across the U.S. economy. BETO systematically prioritizes RD&D into technology opportunities across a range of emerging scientific breakthroughs and technology readiness levels in the subprogram areas illustrated in Figure 1. This approach supports a diverse portfolio while developing the most promising and widely applicable technologies, testing technologies as integrated processes, and demonstrating integrated processes to support scale-up. These technologies will use a broad variety of renewable carbon resources to produce increasing volumes of biofuels and bioproducts. More information on BETO’s mission, goals, and strategic approaches can be found in the Bioenergy Technologies Office Multi-Year Program Plan. The biennial Peer Review process enables external stakeholders to provide feedback on the responsible use of taxpayer funding and develop recommendations for the most efficient and effective ways to accelerate the development of a bioenergy industry. This report includes the results of the Project Peer Review meeting held on April 3–7, 2023, in Denver, Colorado.

09 BIOMASS FUELS↗

DOE OSTI2023

Sizing and Selection of Pressure Relief Valves for High-Pressure Thermal–Hydraulic Systems

This study covers the critical concerns related to the sizing, selection, installation, maintenance, and testing of pressure safety valves (PSVs). The aim is to ensure the safety of pressurized systems, hydrostatic transmission systems, and hydraulic plants, including process plants, thermal power plants, and nuclear reactor systems. PSVs are devices that ensure the safety and reliability of pressurized vessels, lines, and systems during overpressure events. The task of selecting which PSV features are of greatest value for a specific purpose is complex—especially in the design of a high-pressure experimental thermal–hydraulic facility for hydrostatic and transient testing of the reactor system—when the systems are in the design and development phases and require qualification and demonstration to prove that they have reached a given level of technological readiness. The present study highlights the required steps for users to follow the associated rules, guidelines, and recommendations. As a part of this research, case studies are presented to help readers better understand the applicable strategy and standards. A discussion and a review of PSV performance degradation and failure are summarized to provide a better understanding of varied process applications and conditions, including fluid flow dynamics, boundary-layer formation and pressure drops, gas bubble formation and collapse, geometric configurations, inlet/outlet piping, abrupt pressure fluctuations, and acoustic resonance. Moreover, this study discusses the servicing and testing of PSVs in a multiphase pressurized system. Overall, it provides a basic overview of how PSVs ensure the safety of pressurized systems, supported by case studies and industrial practices.

02 PETROLEUM↗

DOE OSTI2022

Irradiation Testing of Sensors - ASI Program Review 2022

This is a powerpoint presentation for the annual Advanced Sensors and Instrumentation (ASI) program webinar. The following is a summary of the presentation. Advanced instrumentation enables testing of nuclear fuels and materials in support of the US advanced nuclear technology industry. A number of promising sensor technologies are in the pipeline. The early part of sensor development can be done outside of the reactor environment, but full technical readiness requires experience gained from in-core performance testing. Customers usually have only one shot to conduct their irradiation experiments. Because of the high costs test sponsors are frequently reluctant to incorporate unproven technologies in reactor experiments. Therefore, it is vital to demonstrate newly-developed sensors in operational conditions, prior to incorporating them into long-term high-value experiments. Successful completion of these activities will create new monitoring capabilities in high-power test reactors (such as ATR) as well as specialty reactors such as TREAT.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

Heuristic Evaluation Methods Applied to a Predictive Maintenance Chatbot

The need for an accessible iterative approach for evaluating prospective artificial intelligence (AI)/ML based technologies in the nuclear industry is needed, given the nature of algorithms and rapid advancements. This paper explores existing heuristic design principles for user-centered design and evaluates them based on their relevancy and usefulness for evaluating AI/ ML based technologies. Researchers at the Idaho National Laboratory (INL) have developed a machine learning software application called VIsualization for PrEdictive maintenance Recommendation (VIPER), which is used to help users understand and engage with the tool to learn more about work orders, data used, predictive maintenance, and machine learning (ML) algorithms. Early user research studies used to access VIPER?s technology readiness level have occurred; however, there is room for further improvement of the software through heuristic evaluations along with other methods and user testing. This work describes the applicability of heuristic evaluation methods and cognitive walkthroughs to help ensure human readiness for prospective AI/ ML based applications, using VIPER as a candidate use case. This work supports industry in ensuring that prospective AI/ML based technologies are usable and useful for plant personnel at nuclear power plants, ultimately leading to their safe, reliable, and efficient use. PowerPoint for conference that was reviewed in PRS and LRS PRS/CON-25-05379 and INL/CON-25-82946

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Machine Learning and Artificial Intelligence for Advanced Condition Monitoring Techniques

With the shift away from time-based maintenance and toward condition-based maintenance, and to reduce overall maintenance costs, there has been an upsurge in the usage and development of advanced condition monitoring (ACM) techniques for real-time monitoring of nuclear power plant (NPP) components. ACM is particularly useful in the development of digital twins, which are designed to predict the failure or degradation of plant components. Successful implementation of ACM requires an assessment to inform the development of a risk-informed approach to evaluate the use of ACM to meet Nuclear Regulatory Committee (NRC) regulations for in-service testing (IST) programs. This includes the monitoring and diagnostics of reactor components and systems in current, new, and advanced reactors. A key component in ACM is the usage of machine learning (ML) and artificial intelligence (AI) algorithms that can employ real-time data from instrumentation and sensors to detect and predict reactor component degradations. Such predictive capabilities enable early detection of component degradation so as to help plant personnel plan and execute necessary maintenance. For successful implementation of ML/AI in ACM such that regulatory requirements are met, a risk-informed graded approach is needed to assess the reliability and performance of ML/AI for ACM. The American Society for Mechanical Engineers (ASME) developed their Operations and Maintenance (O&M) Code to provide guidance on safe, reliable O&M of NPPs. The IST section of the O&M Code specifically establishes requirements for IST and examination to gauge operational readiness of components in water-cooled NPPs. This paper presents a state-of-the-art review of how reliability and risk assessment can be integrated with ACM to assess component performance by non-nuclear industries. This is followed by different methodologies and approaches for conducting performance and reliability assessments so as to meet IST requirements for NPP components.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment for Advanced Condition Monitoring Techniques

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Bat Smart Curtailment: Efficacy and Operational Testing

Curtailment, or blanket curtailment, is a leading method to mitigate the impacts to bats from operating wind turbines. Although this strategy results in considerable decreases in bat fatalities, it also results in decreased energy production. In 2019, Natural Power was awarded funding by the Department of Energy to assess the readiness of the informed smart curtailment technology, EchoSense (formerly referred to as Detection and Active Response Curtailment, [DARC]). The research undertaken by this project expands the understanding of alternative methods, known as smart curtailment, to maintain a reduction in bat fatalities while simultaneously recovering lost energy associated with blanket curtailment. The overall project was composed of three major tasks; Task 1 was focused on cybersecurity compliance of the EchoSense system in accordance with the North American Electric Reliability Corporation Critical Infrastructure Protection (“NERC CIP”) standards, Task 2 assessed the mechanical loads exerted on turbines when operating under a smart curtailment regime, and Task 3 assessed the efficacy of the EchoSense system at an operational wind farm. Regarding Task 1, an external review by the National Renewable Energy Laboratory determined that the EchoSense system did not create any new cybersecurity weaknesses and was compliant with the NERC CIP standards. As a result of this process, Natural Power developed some best practices (10.1) for wind- wildlife technology developers. In conjunction with the National Renewable Energy Laboratory, the results (10.2) of the loads testing demonstrated that the periodic curtailment and release of turbines by the EchoSense system did not have any detrimental impact on the mechanical components of a wind turbine (Task 2). During the late summer to fall of 2020 and 2021, Natural Power demonstrated that the use of the EchoSense smart curtailment system resulted in no significant difference in bat fatalities compared to blanket curtailment with cut-in speeds at 6.9 m/s (2020) and 5.0 m/s (2021) while resulting in a significant difference in decreased lost energy (Task 3). This translates to an average of 41% (2020) and 56% (2021) reduction in per turbine energy loss compared to blanket curtailment. The reduction in energy loss that would have been achieved by EchoSense curtailment compared to blanket curtailment, if applied across all 69 turbines, is roughly equivalent to having an additional turbine on site. These results are notable for finding a balance between the environmental impact of wind energy and the economic feasibility in energy production associated with mitigating that impact. https://www.naturalpower.com/us/expertise/service/engineering-operations/echosense

17 WIND ENERGY↗

DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗

DOE OSTI2024

High-Field Magnets for Future Hadron Colliders

Recent strategy updates by the international particle physics community have confirmed strong interest in a next-generation energy frontier collider after completion of the High-Luminosity LHC program and construction of a e + e - Higgs factory. Both hadron and muon colliders provide a path toward the highest energies, and both require significant and sustained development to achieve technical readiness and optimize the design. For hadron colliders, the energy reach is determined by machine circumference and the strength of the guiding magnetic field. To achieve a collision energy of 100 TeV while limiting the circumference to 100 km, a dipole field of 16 T is required and is within the reach of niobium–tin magnets operating at 1.9 K. Magnets based on high-temperature superconductors may enable a range of alternatives, including a more compact footprint, a reduction of the cooling power, or a further increase of the collision energy to 150 TeV. The feasibility and cost of the magnet system will determine the possible options and optimal configurations. In this article, I review the historical milestones and recent progress in superconducting materials, design concepts, magnet fabrication, and test results and emphasize current developments that have the potential to address the most significant challenges and shape future directions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

The long road to ignition: An eyewitness account

This paper reviews the many twists and turns in the long journey that culminated in ignition in late 2022 using the laser heated indirect-drive approach to imploding DT filled targets at the National Ignition Facility (NIF), located at the Lawrence Livermore National Laboratory (LLNL). We describe the early origins of the Laser Program at LLNL and key developments such as the paradigm shifting birth of high energy density physics (HEDP) studies with lasers, changes in choice of laser wavelength, and the development of key diagnostics and computer codes. Fulfilling the requirements of the multi-faceted Nova Technical Contract was a necessary condition for the approval of the NIF, but more importantly, the end of the Cold War and the cessation of nuclear testing were key catalysts in that approval, along with the ready-and-waiting field of HEDP. The inherent flexibility of the field of laser driven inertial confinement fusion played a fundamental role in achieving success at the NIF. We describe how the ultimately successful ignition target design evolved from the original “point design” target, through the lessons of experiment. All key aspects of that original design changed: The capsule's materials and size were changed; the hohlraum's materials, size, laser entrance hole size, and gas fills were also all changed, as were the laser pulse shapes that go along with all those changes. The philosophy to globally optimize performance for stability (by raising the adiabat and thus lowering the implosion convergence) was also key, as was progress in target fabrication, and in increasing NIF's energy output. The persistence of the research staff and the steadfast backing of our supporters were also necessary elements in this success. We gratefully acknowledge seven decades of researcher endeavors and four decades of the dedicated efforts of many hundreds of personnel across the globe who have participated in NIF construction, operation, target fabrication, diagnostic, and theoretical advances that have culminated in ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

5G-TSN Architecture Capable of Providing Real-time Situational Awareness to Fossil-Energy (FE) Generation Systems (Final Technical Report)

This final report highlights the comprehensive achievements of the project focused on developing and validating a 5G-Time Sensitive Networking (TSN) architecture tailored for real-time operational awareness in fossil energy systems. The initiative successfully advanced through a series of technical milestones, including the integration of EMI-aware network models, deployment of advanced simulation frameworks, and real-world performance characterization at key sites such as UTEP and Fabens. Through the strategic use of NetSim® software, the team created and validated network configurations for wired and wireless environments, tested under varying congestion conditions, and verified network slicing implementations for URLLC-specific applications. Major accomplishments include the migration of simulation tools to the latest NetSim® version to support accurate modeling of TSN and network slicing, extensive EMI measurement campaigns, and the development of a robust simulation model for end-to-end SCADA system integration. Simulations compared both TDD and FDD duplexing modes, revealing insights into their performance under congested conditions. The wireless network was benchmarked for throughput, jitter, and delay metrics, aligning with 3GPP Release 15/16 and IEEE 802.1-TSN standards. A peer-reviewed conference paper was accepted and published, contributing to the broader academic and industrial discourse on 5G-TSN integration in energy systems, in addition to a journal article. Despite minor delays due to software limitations, the project achieved its objectives and delivered validated architecture ready for deployment in advanced energy network environments.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

Explainable Artificial Intelligence Technology for Predictive Maintenance

The domestic nuclear power plant fleet has relied on labor-intensive and time-consuming preventive maintenance programs, thus driving up operation and maintenance costs to achieve high-capacity factors. Artificial intelligence and machine learning can help simplify complex problems, such as diagnosing equipment degradation, to enable more effective decision-making. Benefits will be felt not only within existing analog and digital instrumentation and control, but also work processes, the integration of people with technology, and most importantly, the business case. Together, these hold promise to make nuclear power more efficient and reduce costs associated with operation and maintenance. While the artificial intelligence and machine learning technologies hold significant promise in the nuclear industry, there are challenges or barriers to their adoption. This report outlines the those different machine learning adoption barriers (categorized as historical, technical, economic, regulatory, and user) that the industry must overcome to realize the full benefits of artificial intelligence and machine learning capabilities for long-term economic sustainability. This report also provides solutions for some of these barriers by focusing on improving the explainability of machine learning to encourage trust from the end-user. Trust and explainability are essential to machine learning adoption. This report focuses on research-developed solutions to some of these barriers while analyzing a non-safety-related system, namely the circulating water system. This system frequently experiences waterbox fouling which our models preemptively diagnoses then explains to the operator how those conclusions were reached. This report presents and discusses the inherent trade-off between machine learning performance (in terms of accuracy) and explainability, where highly accurate machine learning methods (such as deep-learning) are the least explainable, and the most explainable methods (such as decision trees) are the least accurate. In addition, explainability of artificial intelligence techniques in terms of transparency and post-hoc metrics are discussed. This report outlines the importance of data novelty and value of new information in evaluating both the explainability and trustworthiness. Novelty detection helps to establish consistency or inconsistency of the new data with respect to the training data. On the other hand, value of information could be a part of the user-centric visualization recommendation system that request additional information to be collected, thereby strengthening the machine learning outcomes. During this project, a copyrighted user-centric visualization that aligns with a human-in-the-loop approach was developed. The user-centric visualization presents different levels of information and can be tailored as per user credentials to gain user confidence. One of the salient features of the user-centric visualization is it presents machine learning methods with explainability metrics. A simplified version of the user-centric visualization was presented to 32 users with varying levels of machine learning expertise. Feedback was solicited to test the hypothesis that the app contained sufficient explainability and that the users would trust the algorithm. Overall, the app was positively received, and the hypothesis was supported. This report discusses the trust-but-verify framework – a potential approach to build user trust artificial intelligence. The framework discusses trust from the human level to artificial intelligence level. The fundamental premise of the trust but verify framework is derived from an observation of nuclear safety culture (i.e., nuclear power plant personnel do not rely on a singular source of data to make a decision). This also ties back to the user-centric visualization that presents different levels of information to achieve both explainability and trustworthiness of artificial intelligence. Even so, the adoption of artificial intelligence and machine learning in the nuclear industry faces additional barriers, namely regulatory and stakeholder readiness. To overcome these challenges, new solutions must gain regulatory approval and cater to stakeholder needs. The Nuclear Regulatory Committee has a 5-year strategic plan which prepares them for reviewing artificial intelligence technologies in licensee submissions. Early and frequent engagement with the regulator is encouraged. Additionally, artificial intelligence solutions should incorporate human-in-the-loop considerations and offer explainability. Stakeholders must prepare by hiring or training staff to adapt to advancing technology in everyday plant tasks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗