Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Ma, Zhegang; Bao, Han; Zhang, Sai; Xian, Min; Mack, Andrea

doi:10.2172/1847070

Cited by 6 publications

(15 citation statements)

References 85 publications

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“…Although not without delays and growing pains, recent developments are encouraging. Updates central to digitalization efforts have included smart sensors and gauges, data tracking devices, EWPs, and DIs (Ma et al, 2022;Oxstrand, 2022). These efforts now provide information that was either previously manually recorded or not recorded at all.…”

Section: Approachmentioning

confidence: 99%

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

Hall,

Miyake,

Joe

et al. 2023

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The commercial U.S. light-water reactor fleet has been operating at historical efficiency, reliability, and safety over the last decade. Nuclear power has the highest capacity factor of any other power generation technology while also serving as the largest baseload source for carbon-free energy. Despite this remarkable achievement, continued operations for many plants are threatened due to fierce electricity market competition and rising operations and maintenance costs of which continued maintenance of obsolete analog equipment is a contributor.

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Section: Approachmentioning

confidence: 99%

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

Hall,

Miyake,

Joe

et al. 2023

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“…As a result, however, the licensees have also opted to discontinue use of the NRC-endorsed approach in NUMARC 93-01 (NEI 2011) for meeting requirements in 10 CFR 50.65. As such, NRC resident inspectors are tasked with understanding the underlying technologies employed in these new approaches (e.g., AI, ML, and data analytical tools) to ensure the adequate inspection of the licensee's ability to meet the requirements in 10 CFR 50.65 [20].…”

Section: Monitormentioning

confidence: 99%

“…ML is also being used to support nuclear data [68]. For more examples, see the comprehensive report INL prepared for the NRC, which consists of an industry survey and overview of AI and ML for nuclear applications [20].…”

Section: Figure 5 Relative Value and Complexity Of Different Types Of...mentioning

confidence: 99%

Status Report on Regulatory Criteria Applicable to the Use of Digital Twins

Muhlheim

Ramuhalli

Huning

et al. 2022

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Although the interest in the use of Digital Twins (DTs) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing DT technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable the automation and autonomy opportunities that can drastically reduce the fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using DTs for construction and in the nuclear arena DTs can provide great benefit in decommission activities. Their ability to operate in real time vastly increases their potential impact.It has been recognized that before a DT can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of DTs need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations may vary if the DT is used to create a virtual reference for plant operations, be used for training, optimization of maintenance intervals, prioritize of maintenance activities, etc. Different still is if the DT is to be used for design. A DT that is integrated into the design or design process can facilitate improved decision making and greater operational flexibilities and its use to support the selection of technical specifications will introduce other requirements. This report describes the research results to date to identify regulatory implications of DT technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of predictive maintenance DTs, artificial intelligence (AI), and automation. The focus of this review included determination of constraints on the application of DT technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. The research included review of topical reports and other submissions to the US Nuclear Regulatory Commission (NRC) on technologies relevant to using DTs for predictive maintenance, NRC safety evaluation (SE) reports, and other relevant literature to identify specific regulatory concerns. v CONTENTS ABSTRACT.

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“…These two problems are even more significant than any technical development issues of predictive models. In NUREG/CR-7294 [106], the NRC has explored existing state of technology of AI/ML models. Repeated throughout the report is the issue of data quality, quantity, applicability, and uncertainty.…”

Section: Challenges Associated With Ai/ml Deployment and Risk Analysismentioning

confidence: 99%

“…Particularly, the "black box" nature of ML/AI brings challenges with respect to the trustworthiness and transparency of the results in nuclear industry. This challenge makes the deployment of ML/AI-guided applications difficult to satisfy the regulatory requirements of NRC [106]. This leads to the first proposed problem that existing regulation has not yet addressed.…”

Section: Challenges Associated With Ai/ml Deployment and Risk Analysismentioning

confidence: 99%

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

Bao

Svetlana

Park

et al. 2022

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This report documents the activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a technical basis to support effective and secure DI&C technologies for digital upgrades/designs. A framework was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the risk impact of plant modernization, considering the introduction of high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant.

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Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

Cited by 6 publications

References 85 publications

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

Status Report on Regulatory Criteria Applicable to the Use of Digital Twins

An Integrated Framework for Risk Assessment of High Safety Significant Safety-related Digital Instrumentation and Control Systems in Nuclear Power Plants: Methodology and Demonstration

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