首页|A Multiscale Method for Two-Component,Two-Phase Flow with a Neural Network Surrogate
A Multiscale Method for Two-Component,Two-Phase Flow with a Neural Network Surrogate
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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 hyper-bolic system of balance laws on the continuum scale with molecular-dynamics(MD)simu-lations 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.
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