Data assimilation for accelerated development of fuel performance codes: Towards automatic implementation of material properties correlations

Developing fuel performance codes requires lengthy technical and legal verification procedures. This work proposes an approach to automate and facilitate these processes by using a data assimilation method. The method considered is Gaussian process regression, which allows for exploiting experimental data to correct/update material property correlations in fuel performance codes. As a demonstration of this method, the correlation for the single-atom xenon diffusivity is updated in the SCIANTIX code with recent lower-length scale results, without the need to modify the SCIANTIX code itself. The goal is to demonstrate the validity of this approach for data-driven developments of physics-based fuel performance codes.