Version 1
: Received: 29 May 2024 / Approved: 30 May 2024 / Online: 30 May 2024 (10:58:09 CEST)
How to cite:
Cervone, A.; Manservisi, S.; Scardovelli, R.; Sirotti, L. Computing Interface Curvature from Height Functions using Machine Learning with a Symmetry-preserving Approach for Two-phase Simulations. Preprints2024, 2024052007. https://doi.org/10.20944/preprints202405.2007.v1
Cervone, A.; Manservisi, S.; Scardovelli, R.; Sirotti, L. Computing Interface Curvature from Height Functions using Machine Learning with a Symmetry-preserving Approach for Two-phase Simulations. Preprints 2024, 2024052007. https://doi.org/10.20944/preprints202405.2007.v1
Cervone, A.; Manservisi, S.; Scardovelli, R.; Sirotti, L. Computing Interface Curvature from Height Functions using Machine Learning with a Symmetry-preserving Approach for Two-phase Simulations. Preprints2024, 2024052007. https://doi.org/10.20944/preprints202405.2007.v1
APA Style
Cervone, A., Manservisi, S., Scardovelli, R., & Sirotti, L. (2024). Computing Interface Curvature from Height Functions using Machine Learning with a Symmetry-preserving Approach for Two-phase Simulations. Preprints. https://doi.org/10.20944/preprints202405.2007.v1
Chicago/Turabian Style
Cervone, A., Ruben Scardovelli and Lucia Sirotti. 2024 "Computing Interface Curvature from Height Functions using Machine Learning with a Symmetry-preserving Approach for Two-phase Simulations" Preprints. https://doi.org/10.20944/preprints202405.2007.v1
Abstract
The volume of fluid (VOF) method is a popular technique for the direct numerical simulations of flows involving immiscible fluids. A discrete volume fraction field evolving in time represents the interface, in particular, to compute its geometric properties. The height function method (HF) is based on the volume fraction field, and its estimate of the interface curvature converges with second-order accuracy with grid refinement. Data-driven methods have been recently proposed as an alternative to computing the curvature, with particular consideration for a well-balanced input data set generation and symmetry preservation. In the present work, a two-layer feed-forward neural network is trained on an input data set generated from the height function data instead of the volume fraction field. The symmetries for rotations, reflections, and the anti-symmetry for the phase swapping have been considered to reduce the number of input parameters. The neural network, establishing a correlation between curvature and height function values, can efficiently predict the local interface curvature. We compare the trained neural network to the standard Height Function method to assess its performance and robustness.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
