Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence
Abstract The coalescence of liquid droplets and lenses is of great practical and theoretical importance in fluid dynamics and the statistical mechanics of multiphase flows. During such coalescence, there is an interesting and intricate interplay between the shapes of the interfaces, separating two p...
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| Format: | Article |
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Nature Portfolio
2025-04-01
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| Series: | Communications Physics |
| Online Access: | https://doi.org/10.1038/s42005-025-02097-y |
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| author | Vasanth Kumar Babu Nadia Bihari Padhan Rahul Pandit |
| author_facet | Vasanth Kumar Babu Nadia Bihari Padhan Rahul Pandit |
| author_sort | Vasanth Kumar Babu |
| collection | DOAJ |
| description | Abstract The coalescence of liquid droplets and lenses is of great practical and theoretical importance in fluid dynamics and the statistical mechanics of multiphase flows. During such coalescence, there is an interesting and intricate interplay between the shapes of the interfaces, separating two phases, and the background flow field. In experiments, it is easier to visualize concentration fields than to obtain the flow field. We demonstrate that two-dimensional (2D) encoder-decoder CNNs, 2D U-Nets, and three-dimensional (3D) U-Nets can be used to obtain flow fields from concentration fields here. To train these networks, we use concentration and flow fields from our numerical simulations of the Cahn-Hilliard-Navier-Stokes equations. We show that, given test images of concentration fields, our trained models accurately predict the flow fields. Finally, we use data from recent experiments on droplet coalescence to show how our method can be used to obtain the flow field from measurements of the concentration field. |
| format | Article |
| id | doaj-art-46867b9d59cb45fbbebbf70f50c08bdb |
| institution | DOAJ |
| issn | 2399-3650 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Physics |
| spelling | doaj-art-46867b9d59cb45fbbebbf70f50c08bdb2025-08-20T03:15:12ZengNature PortfolioCommunications Physics2399-36502025-04-018111310.1038/s42005-025-02097-yConvolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescenceVasanth Kumar Babu0Nadia Bihari Padhan1Rahul Pandit2Department of Physics, Centre for Condensed Matter Theory, Indian Institute of ScienceInstitute of Scientific Computing, TU DresdenDepartment of Physics, Centre for Condensed Matter Theory, Indian Institute of ScienceAbstract The coalescence of liquid droplets and lenses is of great practical and theoretical importance in fluid dynamics and the statistical mechanics of multiphase flows. During such coalescence, there is an interesting and intricate interplay between the shapes of the interfaces, separating two phases, and the background flow field. In experiments, it is easier to visualize concentration fields than to obtain the flow field. We demonstrate that two-dimensional (2D) encoder-decoder CNNs, 2D U-Nets, and three-dimensional (3D) U-Nets can be used to obtain flow fields from concentration fields here. To train these networks, we use concentration and flow fields from our numerical simulations of the Cahn-Hilliard-Navier-Stokes equations. We show that, given test images of concentration fields, our trained models accurately predict the flow fields. Finally, we use data from recent experiments on droplet coalescence to show how our method can be used to obtain the flow field from measurements of the concentration field.https://doi.org/10.1038/s42005-025-02097-y |
| spellingShingle | Vasanth Kumar Babu Nadia Bihari Padhan Rahul Pandit Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence Communications Physics |
| title | Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence |
| title_full | Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence |
| title_fullStr | Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence |
| title_full_unstemmed | Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence |
| title_short | Convolutional neural network based reconstruction of flow-fields from concentration fields for liquid-droplet coalescence |
| title_sort | convolutional neural network based reconstruction of flow fields from concentration fields for liquid droplet coalescence |
| url | https://doi.org/10.1038/s42005-025-02097-y |
| work_keys_str_mv | AT vasanthkumarbabu convolutionalneuralnetworkbasedreconstructionofflowfieldsfromconcentrationfieldsforliquiddropletcoalescence AT nadiabiharipadhan convolutionalneuralnetworkbasedreconstructionofflowfieldsfromconcentrationfieldsforliquiddropletcoalescence AT rahulpandit convolutionalneuralnetworkbasedreconstructionofflowfieldsfromconcentrationfieldsforliquiddropletcoalescence |