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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 OSTI2020

The Floating Chameleon: Floating Nuclear Power Plants and the Nexus of Maritime and Nuclear Security Law

During the 63rd regular session of the International Atomic Energy Agency (IAEA) General Conference, as part of Norway's statement to the General Conference, Mr. Auden Halvorsen, State Secretary for Norway, made the following statement about transportable nuclear power plants (TNPPs): "Deployment of transportable nuclear power plants—TNPPs—demands our attention. The Agency must intensify its conversations of all aspects of the safety and security of such facilities. . . . The scope and applicability of existing requirements and instruments need to be clarified and developed including dialogue with the International Maritime Organization [IMO]." Ensuring the security of TNPPs is essential for their deployment and implementation. TNPPs may be floating nuclear power plants (FNPPs), which bring together elements of nuclear security related to the nuclear power plant with elements of maritime security. Insofar as an FNPP weds the principles of nuclear and maritime security, it also weds the two domains, the ancient traditions and contemporary practices of maritime law with the contemporary practices of nuclear law. Fundamentally, this raises the question, what are the legal requirements for the security of a FNPP? Although both maritime law and nuclear law have established regimes for security through various legal instruments, does an FNPP expose gaps between the current international legal instruments? The previous question of whether international legal regimes define requirements for the security of FNPPs, but a threshold question remains: what is an FNPP? For purposes of nuclear and maritime security, is an FNPP a facility, is an FNPP a vessel and therefore a transport, or does an FNPP change its status based upon temporal and spatial considerations such as when it is docked at a port generating power or in transit to its destination? This set of questions, and others addressed later in this article, highlights that the issue is not merely a question about security but intersects elements of safety whether an FNPP is in uncharted waters of the international regime for both nuclear and maritime law. Here, this paper addresses many of the questions posed above related to the security regulations for an FNPP.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

HAZARD ANALYSIS OF DIGITAL ENGINEERED SAFETY FEATURES ACTUATION SYSTEM IN ADVANCED NUCLEAR POWER PLANTS USING A REDUNDANCY-GUIDED APPROACH

Replacing the existing aging analog instrumentation and control (I&C) systems with modern safety control and protection digital technology offers one of the foremost means of performance improvements and cost reductions for the existing nuclear power plants (NPPs). However, the qualification of digital I&C systems remains a challenge, especially considering the issue of software common-cause failures (CCFs), which are difficult to address. With the application and upgrades of advanced digital I&C systems, software CCFs have become a potential threat to plant safety because most redundant designs use similar digital platforms or software in the operating and application systems. With complex designs of multilayer redundancy to meet the single-failure criterion, digital I&C safety systems (e.g., engineered safety-features actuation system [ESFAS]) are of a particular concern in the U.S. Nuclear Regulatory Commission (NRC) licensing procedures. This paper applies a modularized approach to conduct redundancy-guided systems-theoretic hazard analysis for an advanced digital ESFAS with multilevel redundancy designs. Systematic methods and risk-informed tools are incorporated to address both hardware and software CCFs, which provide guidance to eliminate the triggers of potential single points of failure in the design of digital safety systems in advanced plant designs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
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

Coupling of CTF and RELAP5-3D Within an Enhanced Fidelity Nuclear Power Plant Simulator

A robust and accurate multiphysics engineering simulator is being developed to model the core behavior and system response of pressurized water reactors. This simulator relies on the NESTLE and CTF computer codes to model the neutronics and thermal hydraulics (TH), respectively, inside the core on a nodal scale and on the Reactor Excursion and Leak Analysis Program—Three Dimensional (RELAP5-3D) to model the entire nuclear steam supply system. The RELAP5-3D model includes highly detailed nodalization and multidimensional flow modeling throughout the vessel. Previously, pin-resolved data generated via the Virtual Environment for Reactor Analysis core simulator were used to improve the accuracy of the NESTLE core predictions. The engineering simulator being developed as part of this work uses the 3KEYMASTER platform to couple the enhanced NESTLE model to a nodal-fidelity CTF model to balance run time with accuracy; NESTLE provides node-dependent powers to CTF, and CTF provides node-dependent coolant densities and fuel temperatures to NESTLE.An overlapping domain approach is used for the core TH in which RELAP5-3D provides core boundary conditions based on the system response and CTF provides a node-dependent coolant heating rate to the RELAP5-3D core solution. In the preliminary TH demonstration discussed in this paper, CTF and RELAP5-3D provided similar steady-state core predictions, indicating the hydraulic compatibility between the codes, as well as reasonable and expected behavior under hypothetical transient conditions. This provides an initial step in ongoing efforts toward a robust, multiscale TH/neutronics engineering simulator capability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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 OSTI2020

A Tool for Performing Link Analysis, Operational Sequence Analysis, and Workload Analysis to Support Nuclear Power Plant Control Room Modernization

Nuclear power continues to have an imperative role for the U.S.’s electricity generation. However, for these nuclear power plants (NPPs) to remain economically viable, new strategies for reducing operations and maintenance costs must be explored. The U.S. Department of Energy Light Water Reactor Sustainability Program is researching how to digitally transform existing analog and hybrid main control rooms into a fully integrated control room that addresses this challenge. The transformation will fundamentally change the conduct of operations for these U.S. NPPs. Human factors engineering has a vital role in this effort, where traditional task analysis methods are important in informing the design of advanced human-system interface displays. This work describes a preliminary tool to support task analysis for the development of these advanced displays. Details on the use of this tool, including the specific task analysis methods offered, are presented in this paper.

99 GENERAL AND MISCELLANEOUS↗
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 OSTI2021

ENABLING BUSINESS-DRIVEN INNOVATION THROUGH HUMAN FACTORS ENGINEERING IN NUCLEAR POWER PLANTS

For existing United States nuclear power plant fleet to remain economically viable, the nuclear industry needs to fundamentally change the way in which these plants are operated, maintained, and supported. A digital transformation is a key strategy to address this challenge. Though, guidance in this area is a continued effort. One framework to support innovation in the nuclear industry has taken a broader perspective by focusing on how technology can be used to meet specific business needs and work for the people and processes at hand. This work discusses the role and value of human factors engineering within this nuclear innovation framework. Human factors methods are presented here regarding how they address the phases of nuclear innovation. This work seeks to describe how human factors can be applied in nuclear innovation by strengthening the alignment of technology, people, processes, and regulations such that the needs of the business is addressed.

99 GENERAL AND MISCELLANEOUS↗
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↗