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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.05371 |
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| _version_ | 1866917661864099840 |
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| author | Nohra, Michel Dufour, Steven |
| author_facet | Nohra, Michel Dufour, Steven |
| contents | Multi-fluid flows are found in various industrial processes, including metal injection molding and 3D printing. The accuracy of multi-fluid flow modeling is determined by how well interfaces and capillary forces are represented. In this paper, the multi-fluid flow problem is discretized using a combination of a Physics-Informed Neural Network (PINN) with a finite element discretization. To determine the best PINN formulation, a comparative study is conducted using a manufactured solution. We compare interface reinitialization methods to determine the most suitable approach for our discretization strategy. We devise a neural network architecture that better handles complex free surface topologies. Finally, the coupled numerical strategy is used to model a rising bubble problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem Nohra, Michel Dufour, Steven Numerical Analysis Multi-fluid flows are found in various industrial processes, including metal injection molding and 3D printing. The accuracy of multi-fluid flow modeling is determined by how well interfaces and capillary forces are represented. In this paper, the multi-fluid flow problem is discretized using a combination of a Physics-Informed Neural Network (PINN) with a finite element discretization. To determine the best PINN formulation, a comparative study is conducted using a manufactured solution. We compare interface reinitialization methods to determine the most suitable approach for our discretization strategy. We devise a neural network architecture that better handles complex free surface topologies. Finally, the coupled numerical strategy is used to model a rising bubble problem. |
| title | Coupling of the Finite Element Method with Physics Informed Neural Networks for the Multi-Fluid Flow Problem |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2405.05371 |