Deep learning of interfacial curvature: A symmetry-preserving approach for the volume of fluid method

Asim Önder, Philip L.F. Liu

研究成果: Article同行評審

5 引文 斯高帕斯(Scopus)

摘要

Estimation of interface curvature in surface-tension dominated flows is a remaining challenge in Volume of Fluid (VOF) methods. Data-driven methods are recently emerging as a promising alternative in this domain. They outperform conventional methods on coarser grids but diverge with grid refinement. Furthermore, unlike conventional methods, data-driven methods are sensitive to coordinate system and sign conventions, thus often fail to capture basic symmetry patterns in interfaces. The present work proposes a new data-driven strategy which conserves the symmetries in a cost-effective way and delivers consistent results over a wide range of grids. The method is based on artificial neural networks with deep multilayer perceptron (MLP) architecture which read volume fraction fields on regular grids. The anti-symmetries are preserved with no additional cost by employing a neural network model with input normalization, odd-symmetric activation functions and bias-free neurons. The symmetries are further conserved by height-function inspired rotations and averaging over several different orientations. The new symmetry-preserving MLP model is implemented into a flow solver (OpenFOAM) and tested against conventional schemes in the literature. It shows superior performance compared to its standard counterpart and has similar accuracy and convergence properties with the state-of-the-art conventional method despite using smaller stencil.

原文English
文章編號112110
期刊Journal of Computational Physics
485
DOIs
出版狀態Published - 2023 7月 15

All Science Journal Classification (ASJC) codes

  • 數值分析
  • 建模與模擬
  • 物理與天文學(雜項)
  • 一般物理與天文學
  • 電腦科學應用
  • 計算數學
  • 應用數學

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