A new Surrogate Microstructure Generator for Porous Materials with Applications to the Buffer Layer of TRISO Nuclear Fuel Particles

TL;DR

This paper introduces a surrogate microstructure generator based on Gaussian random fields for porous materials, specifically applied to the buffer layer of TRISO nuclear fuel particles, enabling realistic microstructure replication for mechanical analysis.

Contribution

The paper presents a novel microstructure generator using Gaussian random fields and a clustering algorithm, tailored for porous nuclear materials, with validated application to TRISO fuel buffer layers.

Findings

Generator accurately reproduces microstructure morphology.

Good agreement in mechanical properties between real and surrogate microstructures.

Effective for a wide range of porosities.

Abstract

We present a surrogate material model for generating microstructure samples reproducing the morphology of the real material. The generator is based on Gaussian random fields, with a Mat\'ern kernel and a topological support field defined through ellipsoidal inclusions clustered by a random walk algorithm. We identify the surrogate model parameters by minimizing misfits in a list of statistical and geometrical descriptors of the material microstructure. To demonstrate the effectiveness of the method for porous nuclear materials, we apply the generator to the buffer layer of Tristructural Isotropic Nuclear Fuel (TRISO) particles. This part has been shown to be failure sensitive part of TRISO nuclear fuel and our generator is optimized with respect to a dataset of FIB-SEM tomography across the buffer layer thickness. We evaluate the performance by applying mechanical modeling with problems of linear elastic homogenization and linear elastic brittle fracture material properties and comparing the behaviour of the dataset microstructure and the surrogate microstructure. This shows good agreement between the dataset microstructure and the generated microstructures over a large range of porosities.