Dynamic PRA-Based Estimation of PWR Coping Time Using a Surrogate Model for Accident Tolerant Fuel

Christian, Robby; Shah, Asad Ullah Amin; Kang, Hyun Gook

doi:10.1080/00295450.2020.1777035

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…The downside of using ROMs is that full state-space coverage is not achievable, as some accuracy is lost due to the simplified nature of the ROMs. For example, [57] proposes an interpolation method that combines a reduced-order model, a clustering algorithm called dynamic time warping, and a DPRA framework called Risk Analysis Visualization Environment (RAVEN). This approach allows for an approximate estimation of the "response surface" for a system, which relates operation time and response time to the probability of failure.…”

Section: Polynomial Complexitymentioning

confidence: 99%

Supervised dynamic probabilistic risk assessment: Review and comparison of methods

Maidana

Parhizkar²,

Gomola³

et al. 2023

Reliability Engineering & System Safety

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Section: Polynomial Complexitymentioning

confidence: 99%

Supervised dynamic probabilistic risk assessment: Review and comparison of methods

Maidana

Parhizkar²,

Gomola³

et al. 2023

Reliability Engineering & System Safety

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“…Another difference between these ML/AI applications in nuclear safety and risk analysis is that AI/ML techniques are not only directly applied for model development or uncertainty quantification but are embedded in complicated frameworks for different purposes. (Christian et al 2020) developed a data-driven framework for the estimation of pressurized-water reactor (PWR) coping time, wherein the GP, SVM, k-nearest-neighbor classifier and regressor, Shepard's method, and the spline interpolation method can be selected and applied. (Kim et al 2020a) introduced dynamic Bayesian network and clustering methods for the risk assessment of dynamic systems.…”

Section: Ai/ml In Nuclear Safety and Risk Analysismentioning

confidence: 99%

“…• "Several studies leveraged the Risk Analysis and Virtual Environment (RAVEN) computational platform to operationalize machine learning for time-dependent data resulted from simulations that were equipped with sampling and uncertainty analysis (e.g., ADAPT/RELAP/RAVEN (Mandelli et al 2013a))." • Among the PRA-oriented AI/ML studies, most of these efforts used historical event data rather than results of simulation codes, such as (Young et al 2004, Maljovec et al 2015, Siu et al 2016, Christian et al 2020, Ham and Park 2020. "There are limited studies using text mining approaches for PRA.…”

Section: Ai/ml In Nuclear Safety and Risk Analysismentioning

confidence: 99%

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

Bao

Zhang

et al. 2022

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This report presents the project Idaho National Laboratory conducted for the Nuclear Regulatory Commission (NRC) to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. The report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers.The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations, which was published by the NRC as in the Federal Register Notice NRC-2021-0048 in April 2021. A summary of the survey, including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey, is provided in the report.Finally, the report investigates potential applications of using AI/ML in operating nuclear power plants and advanced reactors (both advanced light-water reactors and advanced non-light-water reactors) to improve nuclear plant safety and efficiency. Three main application fields are considered: plant safety and security assessments; plant degradation modeling, fault and accident diagnosis and prognosis; and plant operation and maintenance efficiency improvement. v TABLE OF CONTENTS ABSTRACT .

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“…Nowadays, metamodels are extensively used in several engineering fields to solve industrial issues as it provides a multi-purpose tool [12]: once fitted, the metamodel can be used, possibly in conjunction with the costly computer code, to perform sensitivity analysis, as well as uncertainty propagation, optimization, or calibration studies. Such techniques have been extensively developed for instance in nuclear engineering (see, e.g., [13,14,15,16]). However, to be confident with this approximation-based approach in support of the different uncertainty quantification tasks, it is crucial to develop accurate and reliable metamodels to approximate the computer model.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation

Marrel,

Iooss

2024

Reliability Engineering & System Safety

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Dynamic PRA-Based Estimation of PWR Coping Time Using a Surrogate Model for Accident Tolerant Fuel

Cited by 8 publications

References 13 publications

Supervised dynamic probabilistic risk assessment: Review and comparison of methods

Supervised dynamic probabilistic risk assessment: Review and comparison of methods

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

Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation

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