Liouville models of particle-laden flow
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929268516192256 |
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| author | Domínguez-Vázquez, Daniel Jacobs, Gustaaf B. Tartakovsky, Daniel M. |
| author_facet | Domínguez-Vázquez, Daniel Jacobs, Gustaaf B. Tartakovsky, Daniel M. |
| contents | Langevin (stochastic differential) equations are routinely used to describe particle-laden flows. They predict Gaussian probability density functions (PDFs) of a particle's trajectory and velocity, even though experimentally observed dynamics might be highly non-Gaussian. Our Liouville approach overcomes this dichotomy by replacing the Wiener process in the Langevin models with a (small) set of random variables, whose distributions are tuned to match the observed statistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Liouville models of particle-laden flow Domínguez-Vázquez, Daniel Jacobs, Gustaaf B. Tartakovsky, Daniel M. Mathematical Physics Langevin (stochastic differential) equations are routinely used to describe particle-laden flows. They predict Gaussian probability density functions (PDFs) of a particle's trajectory and velocity, even though experimentally observed dynamics might be highly non-Gaussian. Our Liouville approach overcomes this dichotomy by replacing the Wiener process in the Langevin models with a (small) set of random variables, whose distributions are tuned to match the observed statistics. |
| title | Liouville models of particle-laden flow |
| topic | Mathematical Physics |
| url | https://arxiv.org/abs/2403.04913 |