RT Journal Article
T1 Finite element based Model Order Reduction for parametrized one-way coupled steady state linear thermo-mechanical problems
A1 Shah, Nirav Vasant
A1 Girfoglio, Michele
A1 Quintela Estévez, Peregrina
A1 Rozza, Gianluigi
A1 Lengomín, Alejandro
A1 Ballarín, Francesco
A1 Barral Rodiño, Patricia
K1 Thermo-mechanical problems
K1 Finite element method
K1 Geometric and physical parametrization
K1 Proper orthogonal decomposition
K1 Galerkin projection
K1 Artificial neural network
K1 Blast furnace
AB 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.
PB Elsevier
SN 0168-874X
YR 2022
FD 2022
LK http://hdl.handle.net/10347/32419
UL http://hdl.handle.net/10347/32419
LA eng
NO Shah, N. V., Girfoglio, M., Quintela, P., Rozza, G., Lengomin, A., Ballarin, F., & Barral, P. (2022). Finite element based Model Order Reduction for parametrized one-way coupled steady state linear thermo-mechanical problems. Finite Elements in Analysis and Design, 212. https://doi.org/10.1016/J.FINEL.2022.103837
NO We are grateful to Dr. Federico Pichi (SISSA mathLab) for insights and crucial support in the numerical implementation of artificial neural network.We would like to acknowledge the financial support of the European Union under the Marie Sklodowska-Curie Grant Agreement No. 765374. We also acknowledge the partial support by the European Union Funding for Research and Innovation - Horizon 2020 Program - in the framework of European Research Council Executive Agency: Consolidator Grant H2020 ERC CoG 2015 AROMA-CFD project 681447 “Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics” and INDAM-GNCS project “Advanced intrusive and non-intrusive model order reduction techniques and applications”, 2019. This work was also partially supported by FEDER and Xunta de Galicia, Spain [grant numbers ED431C 2017/60, ED431C 2021/15], and the Agencia Estatal de Investigación, Spain [PID2019-105615RB-I00/AEI/10.13039/501100011033]. This work has focused exclusively on civil applications. It is not to be used for any illegal, deceptive, misleading or unethical purpose or in any military applications. This includes any application where the use of this work may result in death, personal injury or severe physical or environmental damage.
DS Minerva
RD 24 abr 2026