Application of Machine Learning Algorithms to Identify Problematic Nuclear Data

Grechanuk, Pavel Aleksandrovi; Rising, Michael Evan; Palmer, Todd S.

doi:10.1080/00295639.2021.1935102

Cited by 6 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Overall, the growth and utilization of ML is promising in the nuclear industry. Grechanuk et al [95] recognized the problematic nuclear data in transport simulation using RF to predict the bias of MCNP6 calculation. The benchmark selected in this study was from whisper library with major emphasis being on 233 U solution cases.…”

Section: Application Of Artificial Intelligence In Nuclear Power Plantsmentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

View full text Add to dashboard Cite

The field of machine learning has proven to be a powerful approach in smart manufacturing and processing in the chemical and process industries. This review provides a systematic overview of current state of artificial intelligence and machine learning and their applications in textile, nuclear power plant, fertilizer, water treatment, and oil and gas industries. Moreover, this study reveals the current dominant machine learning methods, pre and post processing of models, increased utilization of machine learning in terms of fault detection, prediction, optimization, quality control, and maintenance in these sectors. In addition, this review gives the insight into the actual benefits and impact of each method, and complications in their extensive deployment. Finally in the current impressive state, challenges, future development in terms of algorithm and infrastructure aspects are highlighted.

show abstract

Section: Application Of Artificial Intelligence In Nuclear Power Plantsmentioning

confidence: 99%

Machine Learning an Intelligent Approach in Process Industries: A Perspective and Overview

Khan

Taqvi

2022

ChemBioEng Reviews

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…A summary of the current status of using AI/ML for existing nuclear power plants related to nuclear reactor heath, monitoring, and radiation detection is provided by Gomez-Fernandex et al [12]. AI techniques are also being applied to numerous nuclear data problems including nuclear data uncertainty [13], identification of problems in nuclear data [14,15], and aiding in performing nuclear data evaluations [16][17][18]. AI/ML techniques have also gain popularity for nuclear material security applications, the current status of which is summarized by Alamaniotis and Heifetz [19].…”

Section: Artificial Intelligence For Nuclear Engineeringmentioning

confidence: 99%