Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries

• URL: http://arxiv.org/abs/2401.06755v2
• Date: Sun, 3 Mar 2024 17:46:05 GMT
• Title: Solving the Discretised Multiphase Flow Equations with Interface Capturing on Structured Grids Using Machine Learning Libraries
• Authors: Boyang Chen, Claire E. Heaney, Jefferson L. M. A. Gomes, Omar K. Matar, Christopher C. Pain
• Abstract summary: This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. For the first time, finite element discretisations of multiphase flows can be solved using an approach based on (untrained) convolutional neural networks.
• Score: 0.6299766708197884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. The idea comes from the observation that convolutional layers can be used to express a discretisation as a neural network whose weights are determined by the numerical method, rather than by training, and hence, we refer to this approach as Neural Networks for PDEs (NN4PDEs). To solve the discretised multiphase flow equations, a multigrid solver is implemented through a convolutional neural network with a U-Net architecture. Immiscible two-phase flow is modelled by the 3D incompressible Navier-Stokes equations with surface tension and advection of a volume fraction field, which describes the interface between the fluids. A new compressive algebraic volume-of-fluids method is introduced, based on a residual formulation using Petrov-Galerkin for accuracy and designed with NN4PDEs in mind. High-order finite-element based schemes are chosen to model a collapsing water column and a rising bubble. Results compare well with experimental data and other numerical results from the literature, demonstrating that, for the first time, finite element discretisations of multiphase flows can be solved using an approach based on (untrained) convolutional neural networks. A benefit of expressing numerical discretisations as neural networks is that the code can run, without modification, on CPUs, GPUs or the latest accelerators designed especially to run AI codes.

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