期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
A Multiscale Method for Two-Component,Two-Phase Flow with a Neural Network Surrogate
1
作者 Jim Magiera Christian Rohde 《Communications on Applied Mathematics and Computation》 2024年第4期2265-2294,共30页
Understanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface.We consider the sharp-interface motion of the compressible two-component flow an... Understanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface.We consider the sharp-interface motion of the compressible two-component flow and propose a heterogeneous multiscale method(HMM)to describe the flow fields accurately.The multiscale approach combines a hyperbolic system of balance laws on the continuum scale with molecular-dynamics(MD)simulations on the microscale level.Notably,the multiscale approach is necessary to compute the interface dynamics because there is—at present—no closed continuum-scale model.The basic HMM relies on a moving-mesh finite-volume method and has been introduced recently for the compressible one-component flow with phase transitions by Magiera and Rohde in(J Comput Phys 469:111551,2022).To overcome the numerical complexity of the MD microscale model,a deep neural network is employed as an efficient surrogate model.The entire approach is finally applied to simulate droplet dynamics for argon-methane mixtures in several space dimensions.To our knowledge,such compressible two-phase dynamics accounting for microscale phase-change transfer rates have not yet been computed. 展开更多
关键词 Phase transition Hyperbolic balance laws for multi-component fluids Multiscale modeling moving-mesh methods Deep neural networks
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部