Application of LSTM Approach for Predicting the Fission Swelling Behavior within a CERCER Composite Fuel

Jian Zhao, Zhenyue Chen, Jingqi Tu, Yunmei Zhao () and Yiqun Dong ()
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Jian Zhao: School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
Zhenyue Chen: School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
Jingqi Tu: School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
Yunmei Zhao: School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200092, China
Yiqun Dong: Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China

Energies, 2022, vol. 15, issue 23, 1-14

Abstract: Irradiation-induced swelling plays a key role in determining fuel performance. Due to their high cost and time demands, experimental research methods are ineffective. Knowledge-based multiscale simulations are also constrained by the loss of trustworthy theoretical underpinnings. This work presents a new trial of integrating knowledge-based finite element analysis (FEA) with a data-driven deep learning framework, to predict the hydrostatic-pressure–temperature dependent fission swelling behavior within a CERCER composite fuel. We employed the long short-term memory (LSTM) deep learning network to mimic the history-dependent behaviors. Training of the LSTM is achieved by processing the sequential order of the inputs to do the forecasting; the input features are fission rate, fission density, temperature, and hydrostatic pressure. We performed the model training based on a leveraged dataset of 8000 combinations of a wide range of input states and state evaluations that were generated by high-fidelity simulations. When replicating the swelling plots, the trained LSTM deep learning model exhibits outstanding prediction effectiveness. For various input variables, the model successfully pinpoints when recrystallization first occurs. The preliminary study for model interpretation suggests providing quantified insights into how those features affect solid and gaseous portions of swelling. The study demonstrates the efficacy of combining data-driven and knowledge-based modeling techniques to assess irradiation-induced fuel performance and enhance future design.

Keywords: fission swelling; data-driven; LSTM deep learning; finite element analysis; multiscale modeling (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: 2022
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