Kriging to Kolmogorov-Arnold Network model accelerated discovery of oxygen control strategy in lead-based fast reactors

Shiwei Wang, Jiajie Chen, Xiaojing Liu (), Tengfei Zhang, Xiang Chai, Qi Lu, Danhong Shen and Hui He ()
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Shiwei Wang: Shanghai Jiao Tong University
Jiajie Chen: Shanghai Jiao Tong University
Xiaojing Liu: Shanghai Jiao Tong University
Tengfei Zhang: Shanghai Jiao Tong University
Xiang Chai: Shanghai Jiao Tong University
Qi Lu: Nuclear Power Institute of China
Danhong Shen: Nuclear Power Institute of China
Hui He: Shanghai Jiao Tong University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract As a cornerstone of net-zero emission strategies and Gen-IV nuclear technologies, the commercialization of Lead-based Fast Reactor (LFR) is impeded by lead-bismuth eutectic (LBE)-induced cladding corrosion. Although active oxygen control demonstrates promise under laboratory conditions, its long-term effectiveness under realistic conditions remains uncertain due to multiphysics interactions and prohibitive computational costs. To address this challenge, we introduce a high-fidelity and high-accuracy surrogate model, K2K (Kriging to Kolmogorov-Arnold Networks) with a predictor-corrector structure combined with a gradient penalty operator, thereby effectively enhancing model accuracy while mitigating non-physical extrapolations. The application of K2K enables the identification and localization of cladding failure mechanisms. Leveraging these insights, we develop a robust and comprehensive oxygen concentration control strategy, encompassing feasible concentration ranges and optimal values to support the safe, reliable, and long-term operation of LFR. Finally, we explore the potential of K2K model for analyzing multiphysics behaviors in energy systems.

Date: 2025
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DOI: 10.1038/s41467-025-64747-7

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