Alsulami, Anwar, Yasuda, Yuki, Onishi, Ryo, Lai, Yukun  ORCID: https://orcid.org/0000-0002-2094-5680 and Yokoi, Kensuke ORCID: https://orcid.org/0000-0001-7914-6050
2026.
Acceleration of three-dimensional free-surface flow simulations using super-resolution convolutional neural network.
Physics of Fluids
38
(4)
, 042106.
10.1063/5.0321435
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Abstract
We propose a super-resolution framework for accelerating three-dimensional free-surface flow simulations, termed the free-surface flow super-resolution convolutional neural network (FSFlow-SRCNN), by integrating a 3D U-Net-based CNN with an advanced free-surface flow solver. The free-surface flow solver is based on the coupled level-set and volume-of-fluid (CLSVOF) method and the full-variable Cartesian grid (FVCG) method. Numerical experiments show substantial acceleration while maintaining close agreement with high-resolution reference solutions and satisfying mass conservation to a comparatively high standard; a speed-up factor of approximately 139 is achieved in one computational environment. FSFlow-SRCNN also demonstrates generalization to nearby unseen conditions, remaining stable and maintaining robust super-resolution performance. Among the three tested loss functions, Loss3 combining an L1 term with divergence and gradient terms provides the best overall balance: the divergence term improves mass conservation, whereas the gradient term enhances the reconstruction of the density field across gas–liquid density discontinuities. Finally, training on mixed one- and two-droplet datasets yields reliable performance in both cases, indicating the potential of FSFlow-SRCNN as a versatile model for a range of free-surface flows.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Engineering Schools > Computer Science & Informatics |
| Publisher: | American Institute of Physics |
| ISSN: | 1070-6631 |
| Date of First Compliant Deposit: | 8 April 2026 |
| Date of Acceptance: | 23 March 2026 |
| Last Modified: | 09 Apr 2026 08:45 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/186278 |
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