Digital twin challenges and opportunities for nuclear fuel manufacturing applications

Manuel Bandala; Patrick Chard; Neil Cockbain; David Dunphy; David Eaves; Daniel Hutchinson; Darren Lee; Xiandong Ma; Stephen Marshall; Paul Murray; Andrew Parker; Paul Stirzaker; C. James Taylor; Jaime Zabalza; Malcolm J. Joyce

doi:10.1016/j.nucengdes.2024.113013

Digital twin challenges and opportunities for nuclear fuel manufacturing applications

Manuel Bandala, Patrick Chard, Neil Cockbain, David Dunphy, David Eaves, Daniel Hutchinson, Darren Lee, Xiandong Ma^*, Stephen Marshall, Paul Murray, Andrew Parker, Paul Stirzaker, C. James Taylor, Jaime Zabalza, Malcolm J. Joyce

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

137 Downloads (Pure)

Abstract

There have been a number of digital twin (DT) frameworks proposed for multiple disciplines in recent years. However, there is a need to develop systematic methodologies to improve our ability to produce DT solutions for the nuclear fuel industry considering specific requirements and conditions exclusive to the nuclear fuel manufacturing cycle. A methodology tailored for nuclear fuel production is presented in this paper. Due to the nature of the chemical processes involved in fuel manufacturing, we highlight the importance of using a combination of physics-based and data-driven modelling. We introduce key technologies for DT construction and the technical challenges for DT are discussed. Furthermore, we depict typical application scenarios, such as key stages of the nuclear manufacturing cycle. Finally, a number of technology issues and research questions related to DT and nuclear fuel manufacturing are identified.

Original language	English
Article number	113013
Number of pages	13
Journal	Nuclear Engineering and Design
Volume	420
Early online date	12 Feb 2024
DOIs	https://doi.org/10.1016/j.nucengdes.2024.113013
Publication status	Published - 15 Apr 2024

Funding

The work reported in this paper has been undertaken as part of the project: Autonomous Inspection for Responsive and Sustainable Nuclear Fuel Manufacture (AIRS-NFM). This work was funded by the Engineering and Physical Sciences Research Council (EPSRC), UK , Grant reference EP/V051059/1 .

Keywords

digital twin
physics-based modelling
data-driven modelling
manufacturing
nuclear fuel

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.nucengdes.2024.113013Licence: CC BY 4.0

Bandala-etal-NED-2024-Digital-twin-challenges-and-opportunities-for-nuclearFinal published version, 1.61 MBLicence: CC BY 4.0

Autonomous Inspection for Responsive and Sustainable Nuclear Fuel Manufacture (Responsive Manufacturing) (AIRS-NFM)
Murray, P. (Principal Investigator), Marshall, S. (Co-investigator) & Zabalza, J. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/09/21 → 5/10/25
Project: Research

Cite this

Bandala, M., Chard, P., Cockbain, N., Dunphy, D., Eaves, D., Hutchinson, D., Lee, D., Ma, X., Marshall, S., Murray, P., Parker, A., Stirzaker, P., Taylor, C. J., Zabalza, J., & Joyce, M. J. (2024). Digital twin challenges and opportunities for nuclear fuel manufacturing applications. Nuclear Engineering and Design, 420, Article 113013. https://doi.org/10.1016/j.nucengdes.2024.113013

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AU - Lee, Darren

AU - Ma, Xiandong

AU - Marshall, Stephen

AU - Murray, Paul

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AB - There have been a number of digital twin (DT) frameworks proposed for multiple disciplines in recent years. However, there is a need to develop systematic methodologies to improve our ability to produce DT solutions for the nuclear fuel industry considering specific requirements and conditions exclusive to the nuclear fuel manufacturing cycle. A methodology tailored for nuclear fuel production is presented in this paper. Due to the nature of the chemical processes involved in fuel manufacturing, we highlight the importance of using a combination of physics-based and data-driven modelling. We introduce key technologies for DT construction and the technical challenges for DT are discussed. Furthermore, we depict typical application scenarios, such as key stages of the nuclear manufacturing cycle. Finally, a number of technology issues and research questions related to DT and nuclear fuel manufacturing are identified.

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KW - physics-based modelling

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KW - nuclear fuel

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Digital twin challenges and opportunities for nuclear fuel manufacturing applications

Abstract

Funding

Keywords

UN SDGs

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Projects

Autonomous Inspection for Responsive and Sustainable Nuclear Fuel Manufacture (Responsive Manufacturing) (AIRS-NFM)

Cite this