Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin

TL;DR

This paper models molten glass flow in a furnace basin, using FEM simulations validated by experiments, and introduces a POD-ANN method for rapid multi-parameter scenario analysis with high accuracy and low computational cost.

Contribution

It presents a new combined FEM and POD-ANN approach for efficient and accurate simulation of molten glass flow in industrial furnace conditions.

Findings

FEM simulations match experimental results well.

POD-ANN achieves rapid solutions with high accuracy.

Method reduces computational time for multiple scenarios.

Abstract

In this work, we present the modelling and numerical simulation of a molten glass fluid flow in a furnace melting basin. We first derive a model for a molten glass fluid flow and present numerical simulations based on the Finite Element Method (FEM). We further discuss and validate the results obtained from the simulations by comparing them with experimental results. Finally, we also present a non-intrusive Proper Orthogonal Decomposition (POD) based on Artificial Neural Networks (ANN) to efficiently handle scenarios which require multiple simulations of the fluid flow upon changing parameters of relevant industrial interest. This approach lets us obtain solutions of a complex 3D model, with good accuracy with respect to the FEM solution, yet with negligible associated computational times.