An Application of ASP in Nuclear Engineering: Explaining the Three Mile Island Nuclear Accident Scenario

Hanna, Botros N.; Trieu, Ly Ly; Son, Tran Cao; Dinh, Truc-Nam

doi:10.1017/s1471068420000241

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…A means of automating the plant procedural system is to implement the rules through decision tables, which associate conditions with actions to perform. In a recent paper by Hanna et al (2020), an answer set programming representation of an NPP was presented, which included rules encoding the plant behavior for fast procedure lookup.…”

Section: Rule-based Decision-makingmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Section: Rule-based Decision-makingmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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“…developed an autonomous management and control system for advanced reactors to mitigate plant anomalies and accidents by using a feedforward neural network (FNN) and RNN. Similarly, other ML/AI techniques or advanced statistical methods, such as Bayesian network(Cetiner and Ramuhalli 2019), Answer Set Programming(Hanna et al 2020a), and LSTM…”

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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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Witnesses for Answer Sets of Logic Programs

Wang

Eiter

Zhang

et al. 2023

ACM Trans. Comput. Logic

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In this paper, we consider Answer Set Programming (ASP). It is a declarative problem solving paradigm that can be used to encode a problem as a logic program whose answer sets correspond to the solutions of the problem. It has been widely applied in various domains in AI and beyond. Given that answer sets are supposed to yield solutions to the original problem, the question of “why a set of atoms is an answer set” becomes important for both semantics understanding and program debugging. It has been well investigated for normal logic programs. However, for the class of disjunctive logic programs, which is a substantial extension of that of normal logic programs, this question has not been addressed much. In this paper, we propose a notion of reduct for disjunctive logic programs and show how it can provide answers to the aforementioned question. First of all, we show that for each answer set, its reduct provides a resolution proof for each atom in it. We then further consider minimal sets of rules that will be sufficient to provide resolution proofs for sets of atoms. Such sets of rules will be called witnesses and are the focus of this paper. We study complexity issues of computing various witnesses and provide algorithms for computing them. In particular, we show that the problem is tractable for normal and headcycle-free disjunctive logic programs, but intractable for general disjunctive logic programs. We also conducted some experiments and found that for many well-known ASP and SAT benchmarks, computing a minimal witness for an atom of an answer set is often feasible.

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An Application of ASP in Nuclear Engineering: Explaining the Three Mile Island Nuclear Accident Scenario

Cited by 4 publications

References 13 publications

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

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

Witnesses for Answer Sets of Logic Programs

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