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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dynamic probabilistic risk assessment and game theory for cyber security risk analysis in nuclear power plants

Nuclear Power Plants and energy systems have become more prone to cyber-attacks with their digitalization and the increased use of smart equipment. Hence, it is important to quantify the risk associated with cyber-attacks in such systems. Dynamic Probabilistic Risk Assessment which involves studying the evolution of a system due to random events and operator and attacker actions during a cyber-attack by employing a physics-based model of the system is a suitable framework to quantify cybersecurity risk in nuclear power plants. In addition to the plant dynamics, it is also important to model the strategies of the attackers and plant operators for an effective cybersecurity risk assessment. Game theory provides a set of necessary tools to model such strategic interactions. In this research, a framework that integrates dynamic probabilistic risk assessment with game theory for cybersecurity risk analysis in nuclear power plants is presented. The mathematical formulation is derived based on the theory of continuous event trees. We propose a game theory based action model, that utilizes physics-based rewards to define the strategies of attackers and operators at every decision epoch. As a case study, the risk associated with cyber-attacks on the digital components in the secondary side of a pressurized water reactor is studied using a reduced order model. A set of attacker actions and a set of operator actions are defined for the system. The operator and attacker interactions were modelled using simultaneous game, their action policies were computed using the concept of mixed strategy Nash equilibrium and the evolution of the system was studied.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Applicability of the Milestones Approach to Deployments of Transportable Nuclear Power Plants (TNPPs)

Transportable nuclear power plants (TNPPs) can provide potential benefits to countries embarking on nuclear programs, offering reduced infrastructure requirements, shorter timeframes for implementation, cost savings and greater deployment flexibility than larger conventional reactors. However, the deployment of a TNPP in a Host State comes with the obligation to establish sufficient regulatory, institutional, and technical infrastructure, which, among others, includes a legal and regulatory framework and a competent regulatory body to implement a State’s safeguards obligations. This paper considers how the unique technical and deployment features of TNPPs may affect the process of preparing for and implementing safeguards in nuclear newcomer countries. Evaluating this issue through the lens of the IAEA’s Milestones Approach, this paper discusses some potential implications arising from the shortening of some milestones phases due to reduced construction or licensing time for TNPPs, and the need for increased cooperation between Host States and Supplier States in preparing for and meeting certain safeguards obligations. These considerations are potentially relevant to various stakeholders: newcomer States considering TNPP deployment; the States and companies that supply such reactors; as well as organizations that support international safeguards capacity building.

Siserman-Gray, Ioana-Cristina↗
DOE OSTI2025

Evaluation of Machine Learning Models for Automated Data Analysis in In-Service Nuclear Power Plant Inspections

The commercial nuclear power industry is facing a potential shortage of certified nondestructive evaluation (NDE) analysts to meet future in-service inspection demands. Automated data analysis (ADA) currently supports human inspectors in tasks such as eddy current evaluations for steam generator examinations. Machine learning (ML) systems are nearing the capability to pass performance demonstration tests for ultrasonic testing (UT) inspections of reactor pressure vessel upper head penetrations in nuclear power plants (NPPs). Current research and development is focused on assisted analysis (AA) of ADA versus fully automated examinations. This presentation will cover assessment of ML flaw detection on dissimilar metal weld (DMW) piping joints.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reinforcement Learning for Anomaly Detection in Nuclear Power Plant Operation and Maintenance

In nuclear power plants (NPPs), timely identification of sensor and human errors is critical to ensure safe and efficient plant operations. Anomaly detection models can be employed for this task. However, traditional anomaly detection approaches may have high dependency on labeled datasets and struggle with adaptability in complex, dynamic environments. Reinforcement learning (RL) has demonstrated significant potential in fault diagnosis and anomaly detection; however, its application to anomaly detection in NPPs remains a relatively underexplored research direction. Hence, to address this gap, in this study, we present a novel physics-informed reinforcement learning model, PIRL-AD: Physics-Informed Reinforcement Learning for Anomaly Detection, that integrates domain knowledge from calorimetric equations into the RL framework for enhanced sensor and human error anomaly detection. We evaluate the performance of PIRL-AD against a non-physics informed RL benchmark and a support vector machine (SVM) on data collected from a forced flow loop testbed. Experimental results suggest that PIRL-AD outperforms other baselines on a range of anomalous datasets that include both sensor and human-induced anomalies across key performance metrics, statistically outperforming the RL and SVM benchmarks with respect to geometric mean (respectively, 92.96% vs. 91.06% vs. 83.01%) and F1-score (respectively, 89.23% vs. 86.98% vs. 77.01%). Furthermore, the findings suggest the potential of physics-integrated reinforcement learning models for enhanced anomaly detection performance in NPPs.

Reinforcement learning↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Nuclear Power Plant (Rev. 1)

With threats to nuclear facilities continuously evolving, the development and implementation of insider threat mitigation programs (ITMPs) are increasingly important. The Office of International Nuclear Security (INS) within the U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA’s) developed Capability Building Progression of an Insider Threat Mitigation Program at an International Nuclear Power Plant to assist newcomers and existing nuclear power plant (NPP) operators in addressing insider threats and establishing effective response measures for insider activities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1973

Seismic analysis of nuclear power plant structures

Primary structures for nuclear power plants are designed to resist expected earthquakes of the site. Two intensities are referred to as Operating Basis Earthquake and Design Basis Earthquake. These structures are required to accommodate these seismic loadings without loss of their functional integrity. Thus, no plastic yield is allowed. The application of NASTRAN in analyzing some of these seismic induced structural dynamic problems is described. NASTRAN, with some modifications, can be used to analyze most structures that are subjected to seismic loads. A brief review of the formulation of seismic-induced structural dynamics is also presented. Two typical structural problems were selected to illustrate the application of the various methods of seismic structural analysis by the NASTRAN system.

Go, J. C.↗
DOE OSTI2026

Robotic automation of maintenance work in nuclear power plants a cross-sector survey and roadmap

Nuclear power plants face increasing cost pressures, workforce constraints (aging workforce and skilled labor shortages), and safety requirements that are accelerating interest in robotic systems for inspection and maintenance. We conducted semi-structured interviews with personnel from seven U.S. nuclear utilities and compared deployment models, operational use cases, and integration practices with those reported by participants in the oil, gas, and petrochemical sector. In nuclear plants, robotic use remains concentrated in inspection—particularly indoor unmanned aerial vehicles and submersible remotely operated vehicles—with limited application to physical maintenance tasks. Reported near-term value includes reduced radiological and industrial risk, reduced outage labor, and improved data for planning and condition assessment. Key barriers include integration and data-interoperability constraints, operator qualification requirements, cybersecurity review burden, and difficulty demonstrating reliability in plant-representative environments. Cross-sector benchmarking highlights organizational and deployment practices that may help nuclear plants scale from pilots to routine use. We propose a deployment-oriented roadmap emphasizing modular payload strategies, representative qualification pathways and testing environments, and improved data governance to support safe and economically justified expansion of robotics in operating nuclear power plants.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Harvesting Reactor Pressure Vessel Beltline Material from the Decommissioned Zion Nuclear Power Plant Unit 1

The decommissioning of the Zion Nuclear Power Plant (NPP) provided a unique opportunity to harvest and study service-aged reactor pressure vessel (RPV) beltline materials. This work, conducted through the U.S. Department of Energy’s Light Water Reactor Sustainability (LWRS) Program, aims to improve the understanding of radiation-induced embrittlement to support extended nuclear plant operations. Material segments containing the Linde 80 flux, wire heat 72105 (WF-70) beltline weld and the A533B Heat B7835-1 base metal, obtained from the intermediate shell region with a peak fluence of 0.7 × 10 19 n/cm 2 (E > 1.0 MeV), were extracted, cut into blocks, and machined into test specimens for mechanical and microstructural characterization. The segmentation process involved oxy-propane torch-cutting, followed by precision machining using wire saws and electrical discharge machining (EDM). A chemical composition analysis confirmed the expected variations in alloying elements, with copper levels being notably higher in the weld metal. The harvested specimens enable a detailed evaluation of through-wall embrittlement gradients, a comparison with the existing surveillance data, and the validation of predictive embrittlement models. This study provides critical data for assessing long-term reactor vessel integrity, informing aging-management strategies, and supporting regulatory decisions to extend the life of nuclear plants. This article is a revised and expanded version of a paper entitled, “Current Status of the Characterization of RPV Materials Harvested from the Decommissioned Zion Unit 1 Nuclear Power Plant”, PVP2017-65090, which was accepted and presented at the ASME 2017 Pressure Vessels and Piping Conference, Waikoloa, HI, USA, 16–20 July 2017.

harvesting beltline material↗
NASA NTRS1975

The transport of nuclear power plant components

The problems of transporting nuclear power plant components to landlocked sites where the usual mode of transport by barge cannot be used are considered. Existing methods of ground-based overland transport are discussed and their costs presented. Components are described and traffic density projections made to the year 2000. Plots of units transported versus distance transported are provided for units booked in 1973 and booked and proposed in 1974. It is shown that, for these cases, overland transport requirements for the industry will be over 5,000,000 ton-miles/year while a projection based on increasing energy demands shows that this figure will increase significantly by the year 2000. The payload size, distances, and costs of existing overland modes are significant enough to consider development of a lighter than air (LTA) mode for transporting NSSS components.

Keating, S. J., Jr.↗
DOE OSTI2025

A Reinforcement Learning Approach to Augment Conventional PID Control in Nuclear Power Plant Transient Operation

The ability of nuclear reactors to operate their power conversion cycles more flexibly will enhance their value to energy grids with variable pricing. Current nuclear control systems are typically classical controllers that are often based on proportional-integral-derivative (PID) control. This paper presents a method of augmenting the existing PID control for difficult transient operations in nuclear power plants using a reinforcement learning–derived feedforward signal applied in real time. The agents, which are trained on a test thermal load-following problem, are designed to improve steam generator outlet temperature control for a range of fast load-following scenarios covering ramp rates from 9%/min to 15%/min. Several reinforcement learning algorithms were initially investigated for the training of the feedforward agents with deep Q-learning (DQN) and proximal policy optimization (PPO) networks, which were found to be the most promising. The DQN controllers utilize discrete actions, giving them a better disturbance rejection at steady state but inconsistent response to initial temperature deviations. In contrast, PPO-trained agents, which take continuous actions except for a dead zone around zero, were shown to have the best combination of high disturbance rejection at steady state and good tracking of the desired temperature value. The ability of the PPO agent was also examined, with the average time of decision making found to be on the order of 1 ms. The fault properties of the controller under the loss of the reinforcement learning agent feedforward signal were also examined. The controller showed strong performance in situations of “no-signal” faults. but was less good at handling “stuck-at” faults, where the feedforward signal remains at a set value. In both cases, however, the PID was able to successfully maintain stability, eventually returning the system to a steady state. It is hoped that this work will allow for the proposed control architecture to be examined for more difficult control problems such that it may eventually be used to adapt existing nuclear plants for more aggressive load-following on grids of the future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Capability Building Progression of an Insider Threat Mitigation Program at International Nuclear Power Plant

With threats to nuclear facilities continuously evolving, the development and implementation of insider threat mitigation programs is increasingly important. The Office of International Nuclear Security within the U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA’s) developed the “Insider Threat Mitigation Program: Facility Implementation Handbook” to assist organizations to be better positioned to minimize the risks posed by malicious insiders. The handbook identified eight elements that contribute to the development and implementation of insider threat mitigation programs. This report outlines the development of a capability building progression of an insider threat mitigation program at international nuclear power plants. This information can be stand-alone or be accompanied by a technical exchange with subject matter experts to support development and implementation of programs with interested international partners.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Automated Waterbox Inspection for Nuclear Power Plants Using Computer Vision - Based Change Detection

Nuclear power plant waterboxes require regular inspection for leaks, missing components, and structural damage during maintenance outages. Traditional manual inspection is time-consuming and poses safety risks from confined space entry. We developed an automated computer vision system for drone-based waterbox inspection in partnership with Florida Light and Power. Our approach uses feature detection and matching to identify critical changes between baseline and current inspection images, automatically flagging additions (leaks/debris), removals (missing plugs), and translations (displaced components) while compensating for drone movement and environmental variations. We systematically evaluated six feature matching methods, from classical approaches (SIFT+BF) to state-of-the-art neural networks (SuperPoint+SuperGlue), using both standard benchmarks (HPatches) and waterbox-specific validation with real-world augmentations. SuperPoint+SuperGlue achieved superior performance with 7.82 pixels RMSE and 100% success rate—2.8x better accuracy than our baseline. While the pre-trained model has commercial licensing restrictions for nuclear deployment, our findings validate this architecture for custom training. We implemented a real-time GUI demonstrating the SIFT+BF approach for immediate deployment, processing drone feeds at 30 FPS with color-coded change visualization. Future work includes training a custom SuperPoint+SuperGlue model on waterbox data and integrating Vision-Language Models for automated reporting and maintenance guidance.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

One Man’s Pirate is Another Man’s . . . Nuclear Terrorist? Evaluating the Act of Piracy Against Floating Nuclear Power Plants on the High Seas

The lack of international consensus about whether a floating nuclear power plant is a facility or vessel presents an ambiguity about a pirate's status if they board a floating nuclear power plant, As piracy continues to be a concern for maritime security, the deployment of floating nuclear power plants (FNPPs) on the high seas raises questions about laws on piracy.

Fialkoff, Marc [Oak Ridge National Laboratory (ORN↗
DOE OSTI2025

AI for Interpreting Nuclear Power Plant Documents for Power Uprates

To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2024

Evaluating Methods of Software Bill of Materials Generation to Enhance Nuclear Power Plant Cybersecurity

Instrumentation and control (I&C) systems in nuclear power plants (NPPs) are potential targets of cyberattacks and can prove deleterious for the safety of the NPPs. A Software Bill of Materials (SBOM) provides a detailed list of the various components and their dependencies in software, which helps in vulnerability and risk assessment for cyber hygiene and situational awareness. For an NPP, the process of generating an accurate SBOM report can be complex due to the legacy systems and firmware binaries involved. While most current SBOM tools are focused more on modern internet technology software, this research provides insights and guidelines for an NPP to generate an accurate and efficient SBOM. Here, the paper proposes a new methodology to help NPPs categorize software and use appropriate tools to generate SBOMs for their digital I&C systems.

SBOM↗