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

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.