Digital twin challenges and opportunities for nuclear fuel manufacturing applications

被引:2
|
作者
Bandala, Manuel [1 ]
Chard, Patrick [5 ]
Cockbain, Neil [4 ]
Dunphy, David [2 ]
Eaves, David [6 ]
Hutchinson, Daniel [6 ]
Lee, Darren [3 ]
Ma, Xiandong [1 ]
Marshall, Stephen [2 ]
Murray, Paul [2 ]
Parker, Andrew [1 ]
Stirzaker, Paul [6 ]
Taylor, C. James [1 ]
Zabalza, Jaime [2 ]
Joyce, Malcolm J. [1 ]
机构
[1] Univ Lancaster, Sch Engn, Lancaster LA1 4YW, England
[2] Univ Strathclyde, Dept Elect & Elect Engn, Glasgow G1 1XW, Scotland
[3] Springfields, Natl Nucl Lab, Preston PR4 0XJ, England
[4] Natl Nucl Lab, Cent Lab, Seascale CA20 1PG, England
[5] Mirion Technol, Warrington OX11 0RL, England
[6] Springfields, Westinghouse Nucl Fuel, Preston PR4 0XJ, England
基金
英国工程与自然科学研究理事会;
关键词
Digital twin; Physics-based modelling; Data-driven modelling; Manufacturing; Nuclear fuel; MODEL PREDICTIVE CONTROL; BIG DATA; ENERGY-POLICY; ENRICHMENT; COSTS; METHODOLOGY; SYSTEMS;
D O I
10.1016/j.nucengdes.2024.113013
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
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.
引用
收藏
页数:13
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