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

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 Matérn 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 a failure sensitive part of TRISO nuclear fuel and our generator is optimized with respect to a publicly available dataset of the buffer layer FIB-SEM tomography measured by a team of researchers from University of Wisconsin at Madison and Oak Ridge National Laboratory. 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.     «

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 Matérn 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 metho...     »