A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate Article Swipe
Jim Magiera
,
Christian Rohde
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.00876
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.00876
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 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 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 compressible one-component flow with phase transitions in [Magiera and Rohde, JCP. 469 (2022)]. To overcome the numerical complexity of the molecular-dynamics 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. Up to our knowledge such compressible two-phase dynamics accounting for microscale phase-change transfer rates have not yet been computed.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.00876
- https://arxiv.org/pdf/2309.00876
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386552513
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https://openalex.org/W4386552513Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.00876Digital Object Identifier
- Title
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A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network SurrogateWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
- Publication date
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2023-09-02Full publication date if available
- Authors
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Jim Magiera, Christian RohdeList of authors in order
- Landing page
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https://arxiv.org/abs/2309.00876Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2309.00876Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2309.00876Direct OA link when available
- Concepts
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Microscale chemistry, Component (thermodynamics), Computer science, Compressibility, Statistical physics, Compressible flow, Flow (mathematics), Artificial neural network, Two-phase flow, Mechanics, Physics, Artificial intelligence, Mathematics, Thermodynamics, Mathematics educationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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