Finite element based Model Order Reduction for parametrized one-way coupled steady state linear thermo-mechanical problems

Abstract

This contribution focuses on the development of Model Order Reduction (MOR) for one-way coupled steady state linear thermo-mechanical problems in a finite element setting. We apply Proper Orthogonal Decomposition (POD) for the computation of reduced basis space. On the other hand, for the evaluation of the modal coefficients, we use two different methodologies: the one based on the Galerkin projection (G) and the other one based on Artificial Neural Network (ANN). We aim to compare POD-G and POD-ANN in terms of relevant features including errors and computational efficiency. In this context, both physical and geometrical parametrization are considered. We also carry out a validation of the Full Order Model (FOM) based on customized benchmarks in order to provide a complete computational pipeline. The framework proposed is applied to a relevant industrial problem related to the investigation of thermo-mechanical phenomena arising in blast furnace hearth walls. • Computational pipeline for fast and accurate thermo-mechanical FEM simulations. • Comparison between POD-Galerkin and POD-ANN model order reduction. • Parametric setting involving both physical properties and geometrical features. • Assessment of MOR approach with an industrial problem related to ironmaking process.