Predicting the particle-agglomeration effect on the equivalent mechanical properties of dispersion nuclear fuel by machine learning

The optimal design of dispersion nuclear fuel necessitates precise correlations between the particle distribution and mechanical properties, which can be established by combining the numerical method, data-driven technique, and machine learning model. In this study, we developed an automated high-throughput workflow to rapidly generate massive number of dispersion nuclear fuel meat models with different particle distributions, and further built the machine learning database for predicting the crucial mechanical properties. Effects of the particle-agglomeration behavior on mechanical properties of the dispersion fuel meat were analyzed in length. The automated workflow includes the entire parametric modeling, parallel computation and post-processing of simulated results, through which we can calculate the equivalent elastic modulus and the maximum Mises stress of the dispersed microstructure with randomly distributed fuel particles. The Fourier distribution function is utilized to characterize the random particle distribution configuration. The analysis suggests that the particle distribution configuration, the volume fraction and the number of agglomerate particles are the crucial factors determining the performance of dispersion nuclear fuel. Furthermore, through utilizing the database constructed by high-throughput computing, the Gaussian process regression algorithm was successfully applied to accurately forecast the mechanical properties in the dispersion fuel meat. This work lays a foundation for optimizing the design of high-performance dispersion nuclear fuel, quickly estimating mechanical properties of composite structures containing randomly dispersed particles, and further extending to analogous systems.