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This presentation covers Oak Ridge National Labatory (ORNL) integral experiment work for fiscal year 2022. The talk highlights the three main Nuclear Criticality Safety Program (NCSP) funded tasks for 2022.
The recent resurgence in advance (non-light water) reactor development has been paralleled by the development of risk-informed performance-based (RIPB) licensing pathways. Specifically, the creation of the RIPB Licensing Modernization Project (LMP) approach and subsequent endorsement by the U.S. Nuclear Regulatory Commission (NRC) now provides advanced reactor vendors with a defined RIPB method to develop an affirmative safety case for licensing. In addition, the Technology Inclusive Content of Applications Project (TICAP) has published guidance on developing a license application based on the LMP approach. To support the utilization of risk information as part of advanced reactor design and licensing efforts, the American Society of Mechanical Engineers (ASME)/American Nuclear Society (ANS) Joint Committee on Nuclear Risk Management (JCNRM) has developed a probabilistic risk assessment (PRA) standard for advanced reactors. The standard, which was formerly approved by the American National Standards Institute (ANSI) in 2021 and recently endorsed by the NRC in trial use Regulatory Guide (RG) 1.247, is an integral standard, covering from initiating events to offsite consequence. A major feature of the standard is that it permits the inclusion of any source of radioactivity material at the site within the plant PRA. Therefore, non-core sources of radioactivity, such as fuel storage, fuel processing, and purification systems, can be included within a single comprehensive plant PRA. For those advanced reactor vendors utilizing a RIPB licensing approach, there is an opportunity to include the non-core sources of radioactivity within the RIPB framework for licensing decision-making, such as the categorization of events, classification of structures, systems, and components (SSCs), and evaluation of the adequacy of defense-in-depth (DID). For advanced reactor designs that contain multiple non-core sources of radioactivity, or for monolithic plant sites that include associated fuel facilities, this approach could potentially simplify licensing applications through the use of a single, uniform, and consistent decision-making framework across all radioactive sources at the site. In addition, a RIPB approach could provide additional insights regarding plant behavior, flexibility regarding licensing decision-making, and potentially allow the use of risk information as part of the plant oversight process. Risk-informing these aspects of advanced reactor licensing would also be consistent with the NRC’s risk policy statement. However, there is diverse regulation and guidance regarding the licensing of non-core sources of radioactivity and generally limited experience using RIPB approaches for the evaluation as part of licensing.
The domestic and international consensus standards for nuclear criticality safety (NCS) have provided guidance for staff performing hands-on work in operations with fissionable materials. These consensus standards have contributed directly to the significant reduction in the rate of criticality accidents in process facilities since the 1940s. The last known criticality accident inside the United States was in 1978 (nearly 43 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (23 years ago). The domestic consensus standards for NCS include the American Nuclear Society (ANS) standards. The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently eighteen standards in the ANS-8 series. Currently, there are six ANS-8 standards in revision mode and eleven in a maintenance mode with one new standard under development. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization (ISO), Technical Committee 85 on Nuclear Energy, Subcommittee 5 on Nuclear Fuel Technology, and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, three standards are in revision mode, and two standards are development. This paper provides the NCS community with an overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.
The domestic and international consensus standards for nuclear criticality safety (NCS) have provided guidance for staff performing hands-on work in operations with fissionable materials. These consensus standards have contributed directly to the significant reduction in the rate of criticality accidents in process facilities since the 1940s. The last known criticality accident inside the United States was in 1978 (nearly 43 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (22 years ago). The domestic consensus standards for NCS include the American Nuclear Society (ANS) standards. The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently eighteen standards in the ANS-8 series. Currently, there are six ANS-8 standards in re- vision mode and eleven in a maintenance mode with one new standard under development. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization, Technical Committee 85 on Nuclear Energy, Subcommittee 5 on Nuclear Fuel Technology, and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, three standards are in revision mode, and two standards are development. This paper provides the NCS community with an overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.
Abstract not provided.
The domestic and international consensus standards for nuclear criticality safety (NCS) were developed based on the lessons-learned from process criticality accidents. These consensus standards were developed to reduce the rate of process criticality accidents in facilities that process, store, handle, or transport fissionable materials by hand. After a significant increase in criticality accidents through the mid-1960s, the rate of criticality accidents decreased as a result of these standards, and the criticality accident rate is extremely low from an industrial safety perspective. The last known criticality accident inside the United States was in 1978 (nearly 47 years ago) at the Idaho Chemical Processing Plant, and outside the United States, an accident occurred at Tokai-mura, Japan, in 1999 (almost 26 years ago). The domestic consensus standards for NCS include the American National Standards (ANS) that are prepared and published by the American Nuclear Society and approved by the American National Standards Institute (ANSI). The ANS Standards Board, the NCS Consensus Committee, and the ANS-8 Subcommittee oversee the development and maintenance of these standards. There are currently 18 standards in the ANS-8 series. Nine ANS-8 standards are either in revision mode or planned for revision. A new standard for the use of nondestructive assay measurements (ANSI/ANS-8.28-2024) for NCS was approved in March of 2024. The international consensus standards for NCS calculations, procedures, and practices are maintained and developed within the International Organization for Standardization, Technical Committee 85 “Nuclear Energy,” Subcommittee 5, “Nuclear Fuel Technology,” and Working Group 8, “Nuclear Criticality Safety.” Eleven standards are currently available, four standards are proposed for revision, and four standards are at various stages of development. This paper provides the NCS community with a high-level overview and status report of domestic and international NCS consensus standards to stimulate interest and to support their continued development.
The agenda for this meeting includes an agenda discussion, ballot report/standard status report, subcommittee projects, and new business.
In 2023, various stakeholders had asked for BEA’s thoughts and recommendations to improve the U.S. Nuclear Regulatory Commission’s (NRC) licensing review and approval process. This included an April 14, 2023 request from the House Committee on Energy and Commerce on “information and recommendations to improve the licensing review and approval process, . . . as well as the siting, licensing, construction, and oversight of advanced nuclear reactor technologies.” In response to these requests, BEA prepared and published INL/RPT-23-72206, Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process (2023 Report). The 2023 Report included 13 recommendations related to streamlining NRC hearings, expediting NRC safety and environmental reviews, otherwise improving NRC licensing, and providing financial benefits to new reactor projects. Many of these earlier recommendations were addressed through various legislative actions or changes made by the NRC. Section 2 of this report addresses the current status of those earlier recommendations. BEA recently received a new request from the House Committee on Energy and Commerce seeking any suggestions for additional areas to examine or potential reforms “that may assist in modernizing the licensing and regulatory process that affects civil nuclear deployment.” Additionally, the new Secretary of Energy has identified initial DOE actions to support unleashing the golden era of American energy dominance, including “Unleash Commercial Nuclear Power in the United States” and “Streamline Permitting and Identify Undue Burdens on American Energy.” Given these developments, BEA has prepared a new set of updated recommendations in this report. The recommendations include updated versions of recommendations from the 2023 Report which have not been fully adopted, as well as entirely new recommendations. This set of recommendations has a slightly broader focus with some recommendations focused on DOE authorizations and some recommendations related to nuclear licensing beyond new reactors. Each recommendation below also identifies whether the recommendation would require legislative action or could be addressed directly by the respective agency.
The COVID-19 pandemic and the isolation measures implemented to contain its spread have exacerbated pre-existing barriers to advancement for women in biomedical informatics. In 2020, the Women in AMIA (WIA) Networking, Mentoring, and Lifecycle Committee administered a survey to identify resources and needs to assist women in informatics. Part of this survey identified several factors impacted by the pandemic: 1) work-life boundaries disappeared and caring for family while working from home became increasingly challenging; 2) isolation, stress and safety of well-being was heightened during the pandemic impacting physical and mental health; 3) diverse working environment and modality brought unforeseen challenges in collaborations and unified communications. These challenges present an opportunity for promoting career development for women professionals in biomedical informatics. This WIA-sponsored panel brings together leaders in the field to highlight their personal experiences and strategies to support women professionals.
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.
The nuclear criticality safety (NCS) consensus standards are developed as using rigorous procedures of the Standards Board of the American Nuclear Society. These procedures have been accredited by the American National Standards Institute, Inc., as meeting the criteria for American National Standards. The Nuclear Criticality Safety Consensus Committee (NCSCC) that approved all 18 NCS consensus standards is balanced to ensure that competent, concerned, and varied interests have had an opportunity to participate. The ANS-8 subcommittee (ANS-8) consists of 17 NCS experts with many years of experience as end users of ANS standards who serve on standard working groups to develop and maintain standards. ANS-8 ensures that the technical content of the standards is adequate for NCS community use. Attempts are made to ensure that ANS-8 consists of NCS professionals with a diverse range of experience such that all standards are applicable to as many sites as possible. ANS-8 is a very active subcommittee, and some active projects in progress are discussed in this paper: basis statement development for all standards, development of a glossary for consistency of definitions across all ANS-8 standards, and Considering the Criticality Safety Support Group (CSSG) Recommendation 2016-04 to the ANS Standards Board for changes in several ANS-8 standards.
A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.