Dynamic data-driven multiscale modeling for predicting the degradation of a 316L stainless steel nuclear cladding material

Journal Article · 29 September 2024 · Journal of Nuclear Materials

DOI: https://doi.org/10.1016/j.jnucmat.2024.155429 · OSTI ID:2513871

Frazier, William E. ^[1]; Fu, Yucheng ^[1]; Li, Lei  ^[1]; Devanathan, Ram  ^[1]

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1. Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Here, we have developed a long short-term memory stacked ensemble (LSTM-SE) surrogate modeling approach that can provide rapid predictions of microstructural evolution and the resultant mechanical properties of American Iron and Steel Institute (AISI) 316L series stainless steel (316LSS) fuel cladding under conditions of varying temperature and radiation dose rate. To acquire training data, we developed and implemented a kinetic Monte Carlo (KMC) model to simulate precipitation kinetics of M₂₃C₆, γ', and G phases within SS316L cladding. Experimentally reported precipitation kinetics of SS316L in literature were linked to the kinetic parameters of the simulated precipitation in our KMC model. The model was then used to simulate microstructure evolution under synthetically generated treatments of varying temperature and radiation dose rate, for periods of up to 3000 hours. Changes in volume fraction, number density, and particle size of precipitates were recorded, and particle area fractions were correlated using statistical methods to develop the surrogate model. Simultaneously, the mechanical properties of the simulated microstructures were evaluated using microstructure-based finite element method (FEM) analysis to determine the elastic modulus, yield stress, ultimate tensile strength, and elongation to failure of the aged microstructures. Using this approach, our surrogate model can predict precipitation behavior within 0.25% volume fraction and mechanical properties within 6% relative error from the values predicted by the KMC and FEM models using 50 training simulations as input. The trained recurrent neural network-based model can return estimations of precipitation kinetics and mechanical properties ~1000 times faster than the physics-based codes. This work demonstrates, as a proof of concept, that reactor material service lifetimes under variable service conditions can be predicted for a statistics-based model from a practicably obtainable dataset.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: AC05-76RL01830
• OSTI ID: 2513871
• Report Number(s): PNNL-SA--178017
• Journal Information: Journal of Nuclear Materials, Journal Name: Journal of Nuclear Materials Vol. 603; ISSN 0022-3115
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English