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NASA NTRS2018

Using the Hottest Particles in the Universe to Probe Icy Solar System Worlds

We present results of our Phase 1 NIAC Study to determine the feasibility of developing a competitive, low cost, low power, low mass passive instrument to measure ice depth on outer planet ice moons, such as Europa, Ganymede, Callisto, and Enceladus. Indirect measurements indicate that liquid water oceans are likely present beneath the icy shells of such moons (see e.g.,the JPL press release "The Solar System and Beyond is Awash in Water"), which has important astrobiological implications. Determining the thickness of these ice shells is challenging given spacecraft SWaP (Size, Weight and Power) resources. The current approach uses a suite of instruments, including a high power, massive ice penetrating radar. The instrument under study, called PRIDE (Passive Radio Ice Depth Experiment) exploits a remarkable confluence between methods from the high energy particle physics and the search for extraterrestrial life within the solar system. PRIDE is a passive receiver of a naturally occurring radio frequency (RF) signal generated by interactions of deep penetrating Extremely High Energy (> 10^18 eV) cosmic ray neutrinos. It could measure ice thickness directly, and at a significant savings to spacecraft resources. At RF frequencies the transparency of modeled Europan ice is up to many km, so an RF sensor in orbit can observe neutrino interactions to great depths, and thereby probe the thickness of the ice layer.

Exploration↗

NASA NTRS2002

Commercial Research and Development: Power to Explore, Opportunities from Discovery

The technical and economic goals of commercial use of space are laudable, and are addressed as a high priority by almost every national space program and most major aerospace companies the world over. Yet, the focus of most organizational agendas and discussions tends to focus on one or two very narrow enabling aspects of this potentially large technological and economic opportunity. While government sponsored commercial launch activities and private space platforms are an integral part of efforts to leverage the commercial use of space, these activities are possibly one of the smallest parts of creating, a viable and sustainable market for the commercial use of space. Most of the current programs usually do not appropriately address some of the critical issues of the current, already interested, potential space user communities. Current programs place the focus of the majority of the user requirements on the vehicle payload weight and mass performance considerations as the primary payload economical factor in providing a commercial market with a stimulating price for gaining access to the space environment. The larger user challenges of transformation from Earth-based research and development approaches to space environment approaches are not addressed early enough in programs to impact the new business considerations of potential users. Currently, space-based research and development user activities require a large user investment in time, in development of new areas of support expertise, in development of new systems, in risk of schedule to completion, and in long term capital positioning. The larger opportunities for stimulating a strong market driven interest in commercial use of space that could result from the development of vehicle payload "leap ahead technologies" for users are being missed, and there is a real risk of limiting the potentially broader market base to support a more technologically advanced and economically lucrative outcome. A major driving force for strengthening the commercial space activities is not only the technological advances in launch vehicle, or newer satellites, but the myriad of enabling payloads technologies that could, as a goal, result in an almost transparent facilitation to regular CD a, -n access to space and microgravity environments by the future users from the existing Earth-based research and development organizations market segments. Rather than focusing only on developing high lift performance launch vehicles and then developing payloads to fit them, the real focus from a business model perspective should to be on the customer payloads requirements, and on designing launch vehicles and platforms systems for a space transportation and facility infrastructure to support all aspects of the business model for the user market. To harness the full potential of space commercialization, new efforts need to be made to comprehensively examine all the critical business model areas for commercial research, development, and manufacturing in space so as to identify specific products and efforts; to determine how such operations must be both similar to and different from current Earth-based activities; to evaluate the enabling technological devices, processes and efforts so that like efforts can be addressed in a synergistic fashion for maximum user cost effectiveness; to delineate the services that are both needed and can be provided by such activities; and to use this information to drive design and development of space commercialization efforts and policy.

Casas, Joseph C.↗

DOE OSTI2025

Spatio-spectral optical fission in time-varying subwavelength layers

Transparent conducting oxides are highly doped semiconductors that exhibit favourable optical features compared with metals, including reduced material losses, tuneable electronic and optical properties, and enhanced damage thresholds. Recently, the photonic community has renewed its attention towards these materials, recognizing their remarkable nonlinear optical properties in the near-infrared spectrum. The exceptionally large and ultrafast change in the refractive index, which can be optically induced in these compounds, extends beyond the boundaries of conventional perturbative analysis and makes this class of materials the closest approximation to a time-varying system. Here we report the spatio-spectral fission of an ultrafast pulse trespassing a thin film of aluminium zinc oxide with a non-stationary refractive index. By applying phase conservation to this time-varying layer, our model can account for both space and time refraction and explain, in quantitative terms, the spatial separation of both spectrum and energy. Our findings represent an example of extreme nonlinear phenomena on subwavelength propagation distances, which provides new insights into transparent conducting oxides’ transient optical properties. This can be critical for the ongoing research on photonic time crystals, on-chip generation of non-classical states of light, integrated optical neural networks, ultrafast beam steering and frequency-division multiplexing.

integrated optics↗

NASA NTRSDate not supplied

New developments in space radiation research at NASA: Annotating data using a novel radiation biology ontology

Like many interdisciplinary sciences, data producers and consumers in the field of radiation biology often use a wide variety of terminology to describe their experiments and data. Furthermore, space systems and technologies are rapidly evolving, and a shared understanding and common terminology for these is also lacking. The efficiency of research organizations can be enhanced by standardizing metadata through the use of knowledge resources like ontologies. Employing a sophisticated model such as a formal ontology to standardize metadata enables automated data acquisition processes and supports more complete, accurate meta-analysis through more efficient and complete data discovery and retrieval, particularly when using multiple data sources. Thus, we developed the Radiation Biology Ontology (RBO) in order to improved radiation biology metadata uniformity and transparency. We used open-source software (the Ontology Development Kit, Protégé and WebProtégé) and worked within the OBO Foundry framework, which includes a set of ontology development principles and practices for ontology consistency, uniformity, and accountability. The RBO has now been incorporated into two radiation research data repositories, NASA’s GeneLab omics database (https://genelab.nasa.gov), and the European Commission STORE database (https://www.storedb.org/). Continuous build integration tools allowed our international RBO collaboration to be more efficient and focus its efforts on semantic model design. Currently, the RBO contains over 300 annotated classes and individuals specific to the study of radiation on biological systems, as well as imports of many additional classes from other OBO Foundry ontologies that relate to and/or provide context for these RBO entities. We publish the RBO through the OBO Foundry, so that it is available for browsing, download, and querying through NCBI Bioportal web site and application programming interface. The NASA Ames Life Science Data Archive (ALSDA) is also in the process of adopting use of the RBO, taking NASA one step closer to a knowledge-based system for space biology data. It is our hope that the global communities of radiation research Investigators, data curators and data analysts can similarly leverage the RBO and will contribute to its further development.

radiation↗

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↗

DOE OSTI2022

Large Scale Caching and Streaming of Training Data for Online Deep Learning

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples and the streaming I/O patterns for which traditional HPC data storage (e.g., parallel file systems) are designed. In addition, as more data are obtained, it is feasible neither simply to train learning models incrementally, due to catastrophic forgetting (i.e., bias towards new samples), nor to train frequently from scratch, due to prohibitive time and/or resource constraints. In this paper, we study data management techniques that combine caching and streaming with rehearsal support in order to enable efficient access to training samples in both offline training and continual learning. We revisit state-of-art streaming approaches based on data pipelines that transparently handle prefetching, caching, shuffling, and data augmentation, and discuss the challenges and opportunities that arise when combining these methods with data-parallel training techniques. We also report on preliminary experiments that evaluate the I/O overheads involved in accessing the training samples from a parallel file system (PFS) under several concurrency scenarios, highlighting the impact of the PFS on the design of the data pipelines.

data pipelines↗

NASA NTRS1988

A relational data-knowledge base system and its potential in developing a distributed data-knowledge system

A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.

Rahimian, Eric N.↗

NASA NTRS1996

Development of an Intelligent Monitoring and Control System for a Heterogeneous Numerical Propulsion System Simulation

The NASA Numerical Propulsion System Simulation (NPSS) project is exploring the use of computer simulation to facilitate the design of new jet engines. Several key issues raised in this research are being examined in an NPSS-related research project: zooming, monitoring and control, and support for heterogeneity. The design of a simulation executive that addresses each of these issues is described. In this work, the strategy of zooming, which allows codes that model at different levels of fidelity to be integrated within a single simulation, is applied to the fan component of a turbofan propulsion system. A prototype monitoring and control system has been designed for this simulation to support experimentation with expert system techniques for active control of the simulation. An interconnection system provides a transparent means of connecting the heterogeneous systems that comprise the prototype.

Reed, John A.↗

NASA NTRS2018

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine “thinking”. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASA’s Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agent’s active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems.

Allen, B. Danette↗

DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗

DOE OSTI2020

Electric Distribution System Planning with DERs - High-level Assessment of Tools and Methods

This document addresses analytical tools and methods to support electric distribution system planning with distributed energy resources (DERs) and grid modernization, drawing on leading-edge research and examples from around the United States. In this report, tools include computer models and software, as well as other analytical aids and practices that support distribution system planning. At its core, distribution system planning is about supporting investment decisions. Enhanced and integrated distribution system planning allows utilities to move from a passive reactive role to smart proactive planning with respect to DERs. Distribution system planning can increase transparency around utility distribution system investments before they are brought to the regulatory body for cost recovery, can support deferring traditional infrastructure investments, and can provide information and incentives to customers and third parties about where DERs can help the grid. A growing number of states are beginning to consider a comprehensive distribution system planning process that addresses the costs and benefits of DERs, reflects more choices coming from the customer/third-party domains, is integrated with resource and transmission planning, and incorporates planning scenarios that reflect the dynamic and changing nature of the electricity system

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Safety Expertise and the Perils of Novelty

Emerging aviation markets such as urban air mobility are giving rise to new technologies and means of operation. However, novelty may hide ‘unknown unknowns,’ raising new hazards. This paper examines how expertise and safety techniques enable transformative technologies such as reduced crew operations, hybrid wing-borne and rotor-born flight, federated air traffic services, and urban operations. We explore how analysts use expertise to address common-cause failures, collect and interpret safety data, and perform exacting tradeoffs between dissimilarity, redundancy, independence, and diversity (human, process lifecycle, or otherwise) to ensure safety. When novelty is present, analysts might not possess the expertise needed to fully understand the implications of design decisions and tradeoffs being made, especially in early lifecycle phases, on emergent properties such as safety. Safety expertise must be carefully cultivated. The conflicting views of safety experts must be unpacked to identify the divergence in fundamental assumptions, models, means, and methods that may be causing them. Once systems venture beyond the basis of what safety expertise can reliably guarantee, projects take on risk that must be managed. The paper contains key takeaways and actionable recommendations for novel OEMs and regulators touching on topics such as robust monitoring; clear and transparent reporting; incremental approaches to fielding novel systems in hazard-rich, risk-tolerant environments; the cultivation of safety culture and expertise in an organization; and the use of scientific study to reduce epistemic uncertainty in novel operations with new technologies. Since excessive novelty in aviation can undermine the current foundation of safety, humility and incrementalism are necessary to enable emerging aviation markets safely.

safety expertise↗

NASA NTRSDate not supplied

Safety Expertise and the Perils of Novelty

safety expertise↗

DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗

DOE OSTI2024

Beginner's Guide to Understanding Power System Model Results for Long-Term Resource Plans

This guide was developed to improve decision-making in the electricity planning process by strengthening dialogue between electricity system planners and stakeholders throughout the process. This document was created for anyone who is first getting exposed to scenario results from power system planning models, or for those who are beginning to interact with power system modelers. It does not assume any experience with power system modeling, or familiarity with electricity planning concepts. This guide can be helpful to a wide range of people, including but not limited to state utility commissioners and commission staff, state energy offices, and intervenors in the planning process. It is targeted to those who are evaluating or reviewing model results presented by a utility, which typically span utility-scale generation and storage (and associated transmission) assets. This guide is designed to provide enough background and guidance that stakeholders can formulate and ask questions to evaluate model results through a deeper understanding of the key assumptions, data, and methods that drive decision outcomes. The ultimate purpose of this work is to increase transparency in the electricity planning process by enabling stakeholders to better engage and evaluate model results. A follow-on guide will provide a deeper dive into emerging topics, which are expected to become more prominent in the electricity planning process, but whose methods and approaches are still being refined.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Probabilistic Deliverability Assessment of Distributed Energy Resources via Scenario-Based AC Optimal Power Flow

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts.

14 SOLAR ENERGY↗

NASA NTRSDate not supplied

Open Science Practices at the Community Coordinated Modeling Center

Open Science is defined as “the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity” by Federal Agencies. The CCMC has been practicing open science based on FAIR (Findable, Accessible, Interoperable and Reusable) principle by providing access to the state-of -the art space science and space weather models to users around the world through various simulation services such as Runs-on-Request, Instant Runs, Real time runs on iSWA system. The CCMC also provides a wide range of tools and framework to help users easily utilize modeled data. One of the tools is the official NASA open-sourced software called Kamodo. Kamodo allows users to work with complex space weather models and data with little or no coding experience. Additionally, to support transparent model validation efforts, the CCMC is providing an integrated and flexible framework called CAMEL. CAMEL allows users to seamlessly compare model outputs with observational data sets. Currently, we are working on a user-friendly database of the papers and research that used CCMC services, so that the future users will have open access to previously performed research by other users and its details. In this presentation, we will show the open tools and resources provided by the CCMC. Furthermore, we will share our new efforts to support open data and open science results.

Ja Soon Shim↗

DOE OSTI2025

Technical Assessment of the Application of Digital Twin and Prognostic Tools for Condition Monitoring

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to present use cases of the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components (SSCs). The advanced technologies considered in this work, collectively referred to as digital twin (DT) technologies, are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), and physics-based models. The report presents two use cases of reactor coolant pumps (RCPs) and heat pipes in nuclear power plants (NPPs) with technical and regulatory considerations and opportunities in using advanced technologies for conditional monitoring. Key findings from the exploration of these considerations are as follows: - Uncertainties in sensor data and model predictions must be rigorously addressed through validation and verification processes - Regulatory compliance is paramount, necessitating data driven models to be developed in line with existing codes and standards, as well as considering potential future guidelines for advanced reactors - Explainability and transparency in ML/AI models are essential for developing operator trust and regulatory review, including methods that enhance the interpretability of complex data-driven predictions - Condition monitoring programs must be evaluated for their effectiveness in reducing maintenance-preventable function failures (MPFF) and aligning with plant performance criteria - The deployment of advanced technologies for condition monitoring could lead to a transition from periodic to continuous monitoring, thereby optimizing maintenance schedules - Collaborative efforts between industry stakeholders, regulatory bodies, and technology developers are crucial for the successful adoption of advanced technologies for condition monitoring systems in nuclear facilities In summary, the introduction of advanced technologies into condition monitoring programs represents a significant leap forward in the domain of NPP maintenance. By harnessing the capabilities of advanced sensors, data analytics, and ML/AI, NPP operators can transition from a time-based to a condition-based maintenance approach. This shift can potentially enhance the reliability and safety of critical plant components while optimizing maintenance efforts and minimizing unnecessary outages. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of inservice inspection and inservice testing (ISI and IST) programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗