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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 OSTI2023

Risk Analysis of a 100 MW Hydrogen Generation Facility near a Nuclear Power Plant

Nuclear power plants (NPPs) are considering flexible plant operations to take advantage of excess thermal and electrical energy. One option for NPPs is to pursue hydrogen production through high temperature electrolysis as an alternate revenue stream to remain economically viable. The intent of this study is to investigate the risk of a 100 MW hydrogen production facility in close proximity to an NPP. Previous analyses have evaluated preliminary designs of a hydrogen production facility in a conservative manner to determine if it is feasible to co-locate the facility within 1 km of an NPP. This analysis specifically evaluates the risk components of a 100 MW hydrogen production facility design, including the likelihood of a leak within the system and the associated consequence to critical NPP targets. This analysis shows that although the likelihood of a leak in an HTEF is not negligible, the consequence to critical NPP targets is not expected to lead to a failure given adequate distance from the plant.

08 HYDROGEN↗
DOE OSTI2023

Risk Analysis of a Hydrogen Generation Facility near a Nuclear Power Plant

Nuclear power plants (NPPs) are considering flexible plant operations to take advantage of excess thermal and electrical energy. One option for NPPs is to pursue hydrogen production through high temperature electrolysis as an alternate revenue stream to remain economically viable. The intent of this study is to investigate the risk of a hydrogen production facility in close proximity to an NPP. A 100 MW, 500 MW, and 1,000 MW facility are evaluated herein. Previous analyses have evaluated preliminary designs of a hydrogen production facility in a conservative manner to determine if it is feasible to co-locate the facility within 1 km of an NPP. This analysis specifically evaluates the risk components of different hydrogen production facility designs, including the likelihood of a leak within the system and the associated consequence to critical NPP targets. This analysis shows that although the likelihood of a leak in an HTEF is not negligible, the consequence to critical NPP targets is not expected to lead to a failure given adequate distance from the plant.

08 HYDROGEN↗
DOE OSTI2023

A New Offering for the Seaman Status Labyrinth - Seaman Status for Nuclear Reactor Operators on Floating Nuclear Power Plants

Floating nuclear power plants present a unique operating environment for land-based nuclear reactor operators. Traditionally located in the control room of a nuclear power plant on land, development of floating nuclear power plants exposes the traditional land-based employees to the marine environment. With the extension of nuclear power generation facilities into the maritime domain, do nuclear reactor operators working on a floating nuclear power plant qualify as seaman under maritime law? Applying existing maritime law, the answer is no, a nuclear reactor operator who operates the nuclear reactor on a floating nuclear power plant does not qualify as a seaman because their work is not in support of the mission of the vessel and the reactor is not connected to a vessel because a floating nuclear power plant is not a vessel. Applying the analysis developed by the Supreme Court in Chandris v. Latsis and the recent Sanchez v. Smart Fabricators of Texas, L.L.C. en banc decision by the Fifth Circuit, a nuclear reactor operator on a floating nuclear power plant does not qualify for seaman status under the Jones Act because their function supports the operation of the reactor and the structure on which the reactor resides does not meet the reasonable person standard established in Lozman v. City of Riviera Beach. Further, existing case law highlights that rendering a structure practically impossible to move eliminates the structure from consideration as a vessel. Because a floating nuclear power plant may be anchored at a seaport or anchored offshore but connected via transmission cables and protected by physical protection barriers, a floating nuclear power plant, with no current means of propulsion is rendered a power plant on water, which is its true function. Recognizing that technological change may alter the conclusion presented in this Article, current designs and structures that exist illustrate the intersection between nuclear and maritime law and the ever-evolving concepts that underpin seamen status in maritime law.

Fialkoff, Marc↗
DOE OSTI2023

Extending Data-Driven Anomaly Detection Methods to Transient Power Conditions in Nuclear Power Plants

Historically, nuclear power plants have operated predominantly at or near full power, meaning that data driven anomaly detection methods can likely perform well at full power operations. This presents a challenge when the power drops (referred to as a transient) and may result in false alarms due to the lack of historical data at those new power levels. The current approach to handling this challenge is to turn detectors off during transients, which makes it impossible to use the algorithms to detect anomalies during these periods, i.e., causing missed detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A full-scope, high-fidelity simulator-based hardware-in-the-loop testbed for comprehensive nuclear power plant cybersecurity research

Nuclear power plant (NPP) cybersecurity research often relies on hardware-in-the-loop (HIL) testbeds that integrate real hardware components into simulated environments. These testbeds allow researchers to identify vulnerabilities, evaluate attack impacts, and test security measures in a controlled setting. Furthermore, previous HIL testbeds lacked fidelity to accurately represent real nuclear systems, limiting the scope of cybersecurity analysis. This study presents the creation of a HIL testbed, devised upon a full-scope, high-fidelity NPP simulator, to facilitate realistic and comprehensive cybersecurity research. To demonstrate its capabilities, the control logic for the steam generator water level was migrated from the simulator to an external programmable logic controller. As a practical application of the developed testbed, supply chain attack scenarios were simulated by injecting malicious code into the controller logic, and the effects of manipulating sensor inputs and control commands were observed. While this HIL testbed provides more detailed simulations, enhanced realism, and wider applicability compared to other options utilizing a less complex simulator, it is also more intricate and costly. For this reason, we include a detailed comparison with some alternative architectures to aid fellow researchers and practitioners in the selection of a suitable HIL architecture based on specific research objectives.

47 OTHER INSTRUMENTATION↗
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 OSTI2023

Quantifying Uncertainty of Deep Reinforcement Learning Based Decision Making for Operations and Maintenance of Nuclear Power Plant

This paper summarizes research that integrates condition monitoring and prognostics with decision making for nuclear power plant operations and maintenance. As part of this research, we have developed an online asset management tool to help reduce life-cycle maintenance and repair costs. Using the latest advancements in condition monitoring, supply chain analytics, and deep reinforcement learning, we have created a predictive maintenance tool that can optimize the maintenance and spare-part management of a repairable nuclear system. To demonstrate these methods, preliminary studies were conducted on a simple, representative maintenance system undergoing a stochastic degradation process that requires repairs or replacement to continue operation. Through Monte Carlo simulations, we were able to reduce maintenance spending by approximately 50% compared to optimized, time-based maintenance strategies. Not only does the decision maker reduce the average life-cycle costs, it also minimizes the chance of high cost scenarios, lowering the variance of the expected cost distributions, and reducing overall financial risk. Furthermore, this work also studies the ability of the decision maker to handle various levels of noise from observation uncertainty. By introducing uncertainty into the decision-making process, we have quantified the robustness and resiliency of the decision maker, as well as identified necessary levels of observability to demonstrate cost effectiveness.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Analysis of Nuclear Fuel Cycle Data

Electricity generated using nuclear power accounted for 18.9% of all electricity consumed in the United States in 2021, putting it in third place behind natural gas (38%) and coal (22%) power plants. Nuclear power plants boast a significantly higher uptime or capacity factor—90% and above—compared to 49.1% for coal fired power plants and 56.6% for natural gas power plants. Renewable energy sources, such as solar photovoltaic (PV) and wind electricity, have lower capacity factors: 24.9% and 36.3%, respectively. In addition, nuclear power is cleaner than both coal and natural gas fired power plants. With the passing of the 2022 Inflation Reduction Act, significant tax credits will be claimed by producers of hydrogen with well-to-gate greenhouse gas (GHG) emissions below 0.45 kg CO 2e /kg H 2 . This has sparked interest in using clean sources of electricity, including nuclear power, to generate H 2 via water electrolysis. As uranium is a primary fuel for modern nuclear power plants, the upstream emissions from nuclear fuel production greatly impact the GHG emissions related to all nuclear power end use. Therefore, it is important to accurately determine the upstream emissions associated with the nuclear fuel cycle of nuclear power production in the United States. In this analysis, the nuclear fuel cycle was separated into distinct steps to allow better understanding of the chemical and energy inputs at each step of the fuel cycle. This also provides details of the GHG emissions at each step in the nuclear fuel cycle. The transportation distance for each step of the fuel cycle was updated to account for the locations of uranium processing facilities along the supply chain of the current U.S. nuclear power plants. Finally, all the updated values were incorporated into Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Research to Develop Flood Barrier Testing Strategies for Nuclear Power Plants

The U.S. Nuclear Regulatory Commission has developed regulations regarding the siting and design of nuclear power plants (NPPs) that are aimed at addressing various natural hazards, including flooding. Flood barriers are designed to prevent water from entering NPP areas containing structures, systems, and components (SSCs) important to safety. The barriers are used at NPPs along with drains, sumps, pumps, valves, plugs, and site grading as part of the plant flood protection features that protect SSCs from experiencing external or internal flooding and mitigate the effects of flooding on NPP operations. The performance of flood protection features, including flood barriers at NPPs, has been an ongoing concern. Domestic and international operational experience provides clear indications that flood barrier performance has significant safety implications, especially for aging NPPs. The observed deficiencies show that flood barriers should be designed and installed properly, then adequately tested, inspected, and maintained in order to ensure that they perform their intended functions during flooding events. Here, this paper reviews available information related to flood barriers employed at U.S. NPPs and provides an overview and categorization of NPP flood barriers. It identifies potential domestic and international flood barrier testing facilities, including operating and decommissioned U.S. NPPs. Finally, this paper presents the technical and logistical considerations that should be made when developing specific testing strategies and protocols for flood barriers, such as the selection of flood barriers, test locations, testing approach, performance criteria, and testing parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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 OSTI2023

Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

In equipment fault diagnosis in nuclear power plants, there may be far more samples in one class (e.g., a health state) than in another class (e.g., a fault state). The distribution of data in each class is highly skewed. Most machine learning algorithms are suitable for balanced training datasets. When faced with imbalanced samples, these algorithms tend to provide good identification for the majority classes and bias for the minority classes. However, the misclassification of minority classes can lead to high costs. To address the above problem, this paper develops a deep reinforcement learning-based diagnosis method that models fault diagnosis as a sequential decision-making process. At each time step, the agent receives the state of the environment represented by the training samples and then takes a diagnosis action guided by a policy. If the action is correct/incorrect, the agent receives a positive/negative reward. The reward for minority classes is higher than that for majority classes. The agent’s goal is to obtain as many cumulative rewards as possible in the process, i.e., to identify the sample as correctly as possible. Six demonstration scenarios are constructed, depending on the selected fault datasets and the designed model structures. Experiments show that the proposed method achieves a higher weighted-averaged F1 score than the classical supervised learning method in most cases of class imbalance. Finally, the proposed method has potential applications in the field of class imbalance fault diagnosis of equipment in nuclear power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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↗
DOE OSTI2023

A Review of Sociotechnical Approaches for Nuclear Power Plant Modernization

The current United States nuclear power plant fleet is in need of transforming the way work is performed to remain competitive with other electricity-generating sources. The use of new digital technologies can be applied to significantly reduce operating and maintenance costs. Recent research by the United States Department of Energy Light Water Reactor Sustainability Program has identified new opportunities to leverage advanced digital technologies to transform the way work is performed at existing plants. However, to ensure that the capabilities of people and advanced digital technologies are jointly optimized, a sociotechnical approach should be considered. This work explores recently introduced sociotechnical approaches to address the function allocation and data visualization considerations in the integration of new digital technologies to ensure safety, reliability, and maximizing the capabilities of proposed technological solutions that ensure the economic viability of the existing United States nuclear power plant fleet.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel

This report provides evaluations of the NPP site infrastructure and near-site transportation infrastructure for removing SNF from 19 NPP sites and the Morris Independent Spent Fuel Storage Installation (ISFSI). The material to be removed from the NPP sites includes both the SNF and the greater-than-Class C low-level radioactive waste (GTCC waste)3 that is stored, or will be stored, at the sites. This report is an update of the report Nuclear Power Plant Infrastructure Evaluations for Removal of Spent Nuclear Fuel (Maheras et al. 2021) and includes expansion of the site evaluations to include operating nuclear power plant (NPP) sites and to incorporate updated site inventory data. Figures that include the number of spent nuclear fuel (SNF) assemblies and metric tons heavy metal (MTHM) in a single figure have also been added to the report.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗