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Machine Learning design of Volume of Fluid schemes for compressible flows

Abstract : Our aim is to establish the feasibility of Machine-Learning-designed Volume of Fluid algorithms for compressible flows. We detail the incremental steps of the construction of a new family of Volume of Fluid-Machine Learning (VOF-ML) schemes adapted to bi-material compressible Euler calculations on Cartesian grids. An additivity principle is formulated for the Machine Learning datasets. We explain a key feature of this approach which is how to adapt the compressible solver to the preservation of natural symmetries. The VOF-ML schemes show good accuracy for advection of a variety of interfaces, including regular interfaces (straight lines and arcs of circle), Lipschitz interfaces (corners) and non Lipschitz triple point (the Trifolium test problem). Basic comparisons with a SLIC/Downwind scheme are presented together with elementary bi-material calculations with shocks.
Keywords : ML VOF CFD
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Contributor : Bruno Després Connect in order to contact the contributor
Submitted on : Wednesday, January 22, 2020 - 6:47:30 PM
Last modification on : Wednesday, December 9, 2020 - 3:17:12 PM
Long-term archiving on: : Thursday, April 23, 2020 - 5:11:44 PM


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  • HAL Id : hal-02447631, version 2


Bruno Després, Hervé Jourdren. Machine Learning design of Volume of Fluid schemes for compressible flows. 2020. ⟨hal-02447631v2⟩



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