A Multi‐Fidelity Modeling Framework for Predicting the Fracture Toughness of Steel in Hydrogen Environments

Hydrogen embrittlement is a major failure mode of steel structures in hydrogen‐rich environments. Accurate and rapid assessment of material parameters, such as the fracture toughness of steels in hydrogen environments, is crucial for ensuring the safe operation of hydrogen energy infrastructure. In this study, a computational multi‐fidelity modeling framework is proposed to estimate the hydrogen‐assisted fracture toughness of steels within a defined phase‐field parameter space. A finite element model based on the fracture phase field principle is constructed to obtain high‐fidelity data. A low‐fidelity analytical expression that considers hydrogen‐induced fracture energy reduction and stress‐driven accumulation is developed to obtain a large number of low‐fidelity samples. The proposed multi‐fidelity model achieves higher prediction accuracy than popular models designed for small sample sizes, namely, support vector regression, kernel ridge regression, and Gaussian process regression. Notably, the constructed multi‐fidelity model maintains good predictive performance under the investigated loading‐rate cases. Furthermore, the influence of modeling variables on is thoroughly discussed. In summary, the proposed multi‐fidelity modeling framework provides a powerful tool for accurate, high‐throughput hydrogen embrittlement performance assessment.