Modeling of silver transport in cubic SiC: Integrating molecular dynamics, bounds averaging, and uncertainty quantification

TL;DR

This paper develops a physics-informed, multi-scale model for silver diffusion in SiC, combining molecular dynamics, bounds averaging, and uncertainty quantification to better understand and predict silver transport in nuclear fuel materials.

Contribution

It introduces a novel integrated modeling framework that combines MD simulations, bounds averaging, and Bayesian inference to estimate silver diffusivity in SiC with uncertainty quantification.

Findings

Homogenized model captures grain boundary transport mechanisms.

Model overpredicts diffusivity, corrected by reversible trapping concept.

Sensitivity analysis highlights trap desorption energy and specific grain boundary diffusivity.

Abstract

Silver released from TRISO fuel particles can migrate through the SiC layer and deposit on reactor components, posing radiation hazards and operational challenges. Despite numerous proposed mechanisms, the precise pathway of silver transport through intact 3C-SiC remains unresolved. We present a physics-informed model for estimating the effective diffusivity of silver in polycrystalline 3C-SiC. Molecular dynamics (MD) simulations yield diffusivities for {\Sigma 3} and {\Sigma 9} grain boundaries (GBs), while literature values are used for other GB types and the bulk. These are combined using a bounds-averaging approach accounting for distinct GB transport properties. Bayesian inference of experimental data provides credible intervals for effective Arrhenius parameters and reveals a correlation between activation energy and pre-exponential factor. Although the homogenized model captures GB-mediated transport mechanisms, it overpredicts silver diffusivity relative to experiments. To resolve this, a multiplicative correction based on reversible trapping at nano-pores is introduced. It is derived from first principles and is shown to reproduce observed transport behavior. Sensitivity analysis identified trap desorption energy and {\Sigma 9} GB diffusivity as dominant factors influencing Ag transport. The resulting framework provides a mechanistic description of Ag transport suitable for integration into higher-scale fuel performance models.