A Deep Neural Network Model of Particle Thermal Radiation in Packed Bed

TL;DR

This paper introduces a deep neural network model that predicts particle view factors for thermal radiation in packed beds, enabling real-time simulations and efficient analysis of radiative heat transfer in large granular systems.

Contribution

It presents a novel DNN-based predictor for view factors, significantly improving computational speed and enabling practical real-time thermal radiation simulations in granular systems.

Findings

DNN model achieves high accuracy in predicting view factors.

Model enables real-time thermal radiation simulation in large pebble beds.

Coupling with DEM allows efficient analysis of radiative properties.

Abstract

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.