A Future with Machine Learning: Review of Condition Assessment of Structures and Mechanical Systems in Nuclear Facilities

Harleen Kaur Sandhu, Saran Srikanth Bodda and Abhinav Gupta ()
Additional contact information
Harleen Kaur Sandhu: Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA
Saran Srikanth Bodda: Department of CCEE, North Carolina State University, Raleigh, NC 27695, USA
Abhinav Gupta: Center for Nuclear Energy Facilities and Structures, North Carolina State University, Raleigh, NC 27695, USA

Energies, 2023, vol. 16, issue 6, 1-23

Abstract: 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–silica 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: condition assessment; artificial intelligence; deep learning; damage detection; signal processing; data management; nuclear piping; concrete; advanced reactors; digital twin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/6/2628/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/6/2628/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:6:p:2628-:d:1093827

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().