A new method for modeling radiative heat transfer based on Bayesian artificial neural networks and Monte Carlo method in participating media

Abstract

A new method based on predictive capacity of Feedforward Artificial Neural Networks (FANN) is proposed to estimate the divergence of the radiative flux in an axisymmetric domain. Training and validation databases have been built thanks to results given by the SNB-CK model and computed accordingly with a null collision Monte Carlo algorithm. The major aim of this work is to combine advantages of spectral models in terms of accuracy and the computational efficiency of neural networks in order to make possible the accurate modeling of radiative heat transfer. As a result, ANNs are able to model the radiative flux divergence on the basis of training data and some keys to avoid the pitfalls related to ANNs are provided.