Physics-informed data-driven closure relation for dilute short fiber suspensions
Abstract Tensor based formulations for modeling fiber orientation require the use of a closure approximation to solve the macro-descriptor evolution equation. This originates from the hydrodynamic component of motion and has received some attention in literature. In this work, we use the concept of...
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SpringerOpen
2025-03-01
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| Series: | Advanced Modeling and Simulation in Engineering Sciences |
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| Online Access: | https://doi.org/10.1186/s40323-025-00290-w |
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| author | Bruno Ramoa Chady Ghnatios João Miguel Nóbrega Francisco Chinesta |
| author_facet | Bruno Ramoa Chady Ghnatios João Miguel Nóbrega Francisco Chinesta |
| author_sort | Bruno Ramoa |
| collection | DOAJ |
| description | Abstract Tensor based formulations for modeling fiber orientation require the use of a closure approximation to solve the macro-descriptor evolution equation. This originates from the hydrodynamic component of motion and has received some attention in literature. In this work, we use the concept of universal ordinary differential equations to infer a data-driven closure for fibers in the diluted regime using simple homogeneous flows. The closure can be understood as an orthogonal correction to the linear closure in eigenspace. To simplify and accelerate the training process, empirically determined bounds are used to restrain the machine learning output to physically admissible values. Three sets of tests are used to assess the performance of the model with simple homogeneous flows not included in the training pool, sequential combination of simple flows, and a non-homogeneous flow in a center gated disk. The results show that the trained model is able to adequately replicate the true dynamics of orientation, even for unseen flow regimes. |
| format | Article |
| id | doaj-art-46ef0ca0a84346e8aff7c81582bc65fd |
| institution | Kabale University |
| issn | 2213-7467 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Advanced Modeling and Simulation in Engineering Sciences |
| spelling | doaj-art-46ef0ca0a84346e8aff7c81582bc65fd2025-08-20T03:40:47ZengSpringerOpenAdvanced Modeling and Simulation in Engineering Sciences2213-74672025-03-0112112210.1186/s40323-025-00290-wPhysics-informed data-driven closure relation for dilute short fiber suspensionsBruno Ramoa0Chady Ghnatios1João Miguel Nóbrega2Francisco Chinesta3Institute for Polymers and Composites, University of MinhoDepartment of Mechanical engineering, University of North FloridaInstitute for Polymers and Composites, University of MinhoPIMM Lab & ESI Chair, CNRS@CREATE, Arts et Metiers Institute of TechnologyAbstract Tensor based formulations for modeling fiber orientation require the use of a closure approximation to solve the macro-descriptor evolution equation. This originates from the hydrodynamic component of motion and has received some attention in literature. In this work, we use the concept of universal ordinary differential equations to infer a data-driven closure for fibers in the diluted regime using simple homogeneous flows. The closure can be understood as an orthogonal correction to the linear closure in eigenspace. To simplify and accelerate the training process, empirically determined bounds are used to restrain the machine learning output to physically admissible values. Three sets of tests are used to assess the performance of the model with simple homogeneous flows not included in the training pool, sequential combination of simple flows, and a non-homogeneous flow in a center gated disk. The results show that the trained model is able to adequately replicate the true dynamics of orientation, even for unseen flow regimes.https://doi.org/10.1186/s40323-025-00290-wMachine learningPhysics informed machine learningFiber orientation modeling |
| spellingShingle | Bruno Ramoa Chady Ghnatios João Miguel Nóbrega Francisco Chinesta Physics-informed data-driven closure relation for dilute short fiber suspensions Advanced Modeling and Simulation in Engineering Sciences Machine learning Physics informed machine learning Fiber orientation modeling |
| title | Physics-informed data-driven closure relation for dilute short fiber suspensions |
| title_full | Physics-informed data-driven closure relation for dilute short fiber suspensions |
| title_fullStr | Physics-informed data-driven closure relation for dilute short fiber suspensions |
| title_full_unstemmed | Physics-informed data-driven closure relation for dilute short fiber suspensions |
| title_short | Physics-informed data-driven closure relation for dilute short fiber suspensions |
| title_sort | physics informed data driven closure relation for dilute short fiber suspensions |
| topic | Machine learning Physics informed machine learning Fiber orientation modeling |
| url | https://doi.org/10.1186/s40323-025-00290-w |
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