Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin
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 obtai...
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| Published in | Computational & applied mathematics Vol. 43; no. 7 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.10.2024
Elsevier |
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| Online Access | Get full text |
| ISSN | 2238-3603 0377-0427 1807-0302 |
| DOI | 10.1007/s40314-024-02921-1 |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 407 |
| Author | Ballarin, Francesco Delgado Ávila, Enrique Mola, Andrea Rozza, Gianluigi |
| Author_xml | – sequence: 1 givenname: Francesco surname: Ballarin fullname: Ballarin, Francesco organization: Department of Mathematics and Physics, Università Cattolica del Sacro Cuore – sequence: 2 givenname: Enrique orcidid: 0000-0001-7881-4045 surname: Delgado Ávila fullname: Delgado Ávila, Enrique email: edelgado1@us.es organization: Departamento de Ecuaciones Diferenciales y Análisis Numérico, Universidad de Sevilla – sequence: 3 givenname: Andrea surname: Mola fullname: Mola, Andrea organization: MUSAM Lab, Scuola IMT Alti Studi Lucca – sequence: 4 givenname: Gianluigi surname: Rozza fullname: Rozza, Gianluigi organization: mathLab, Mathematics area, SISSA, International School for Advances Studies |
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| Keywords | Finite element method Molten glass flow Proper orthogonal decomposition Artificial neural networks |
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| Title | Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin |
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