LAPSE:2023.31162
Published Article
LAPSE:2023.31162
A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities
April 18, 2023
The nuclear industry is exploring applications of Artificial Intelligence (AI), including autonomous control and management of reactors and components. A condition assessment framework that utilizes AI and sensor data is an important part of such an autonomous control system. A nuclear power plant has various structures, systems, and components (SSCs) such as piping-equipment that carries coolant to the reactor. Piping systems can degrade over time because of flow-accelerated corrosion and erosion. Any cracks and leakages can cause loss of coolant accident (LOCA). The current industry standards for conducting maintenance of vital SSCs can be time and cost-intensive. AI can play a greater role in the condition assessment and can be extended to recognize concrete degradation (chloride-induced damage and alkali‚ąísilica reaction) before cracks develop. This paper reviews developments in condition assessment and AI applications of structural and mechanical systems. The applicability of existing techniques to nuclear systems is somewhat limited because its response requires characterization of high and low-frequency vibration modes, whereas previous studies focus on systems where a single vibration mode can define the degraded state. Data assimilation and storage is another challenging aspect of autonomous control. Advances in AI and data mining world can help to address these challenges.
Keywords
advanced reactors, Artificial Intelligence, concrete, condition assessment, damage detection, data management, deep learning, digital twin, nuclear piping, signal processing
Suggested Citation
Sandhu HK, Bodda SS, Gupta A. A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities. (2023). LAPSE:2023.31162
Author Affiliations
Sandhu HK: Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA [ORCID]
Bodda SS: Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA [ORCID]
Gupta A: Center for Nuclear Energy Facilities and Structures, North Carolina State University, Raleigh, NC 27695, USA [ORCID]
Journal Name
Energies
Volume
16
Issue
6
First Page
2628
Year
2023
Publication Date
2023-03-10
Published Version
ISSN
1996-1073
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Original Submission
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PII: en16062628, Publication Type: Review
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LAPSE:2023.31162
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doi:10.3390/en16062628
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Apr 18, 2023
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