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.

A ceramic–ceramic (CERCER) fuel with minor actinide-enriched ceramic fuel particles dispersed in a MgO ceramic matrix is chosen as a promising composite target for accelerator-driven systems (ADS). Fission swelling is a complex irradiation-induced phenomenon that involves recrystallization, resolution, and hydrostatic pressure under extreme conditions of high temperature and significant fission flux. In this study, a multiscale computational framework was developed to integrate simulations of continuum-scale thermo-mechanical behavior in the CERCER composite with a grain-scale hydrostatic pressure-dependent fission gas swelling model. Hydrostatic pressure-dependent fission welling is taken into account in the stress update algorithms for UO2 particles. Accordingly, we programmed the user subroutines to define the thermo-mechanical constitutive relations in the finite element simulations. The obtained results indicate that (1) the proposed method accurately predicts the swelling deformation at various burnup levels while taking into account hydrostatic pressure and (2) prior to recrystallization, the particle swelling is primarily influenced by temperature variation, whereas after recrystallization, the presence of hydrostatic pressure favorably suppresses the swelling deformation. This work effectively captures the swelling behavior influenced by hydrostatic pressure within the dispersed-type CERCER composite fuel in ADSs.

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