Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases

TL;DR

This study explores the use of CNNs and MLPs as surrogate models to efficiently approximate radiative heat transfer in 2-D furnaces with spectrally participating gases, aiming to reduce computational costs.

Contribution

It introduces a novel adaptation of CNN inputs for radiative transfer problems and compares CNN and MLP architectures in terms of speed, accuracy, and robustness.

Findings

CNN achieves higher accuracy than MLP.

Both models significantly speed up computations.

CNN is more robust to hyper-parameter variations.

Abstract

Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have been created with the classical solver, ICARUS2D, that uses the discrete transfer radiation method with the statistical narrow bands model. The performance of the CNN architecture is compared to a more classical MLP architecture in terms of speed and accuracy. Thanks to Optuna, all results are obtained using the optimized hyper parameters networks. The results show a significant speedup with industrially acceptable relative errors compared to the classical solver for both architectures. Additionally, the CNN outperforms the MLP in terms of precision and is more robust and stable to changes in hyper-parameters. A performance analysis on the dataset size of the samples have also been carried out to gain a deeper understanding of the model behavior.