A data-driven deep learning model of radiative heat transfer in dense granular systems

Abstract

Radiative heat transfer has attracted increasing research interest in various dense granular systems, including large-scale nuclear pebble-bed reactors, fusion breeder blankets, packed beds, metal powder beds and granular lunar regolith simulants. The optimised ray tracing method (ORTM) with a Sobol quasi-random sequence is proposed to calculate the obstructed view factor between spheres, and it converges much faster than that with the traditional pseudorandom number sampling method. Based on discrete particle packing of nuclear pebble bed HTR-PM and random particle generation, a large obstructed view factor dataset of thermal radiation is generated by parallel CUDA computing for training a deep learning regression model to discuss the packing-property linkage for radiative heat transfer in the large-scale particle bed. Compared with other approaches, the deep residual neural network (ResNet) with the swish activation function applied in this paper is found to be the best neural network architecture for view factor prediction without vanishing gradients or model degradation problems during training. The testing error of the trained deep learning model is <1.0 × 10-4 for over 99.96% view-factor cases in nuclear pebble beds, and it has proven to be 7.3 × 105 times faster than the traditional numerical method in view-factor calculations. This makes it feasible to explore the local and macroscopic thermal radiation behaviours in large-scale pebble beds filled with up to 1.0 × 107 spheres.