Parameter Analysis in Continuous Data Assimilation for Various Turbulence Models
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929487212445696 |
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| author | Albanez, Debora A. F. Benvenutti, Maicon Jose Little, Samuel Tian, Jing |
| author_facet | Albanez, Debora A. F. Benvenutti, Maicon Jose Little, Samuel Tian, Jing |
| contents | In this study, we conduct parameter estimation analysis on a data assimilation algorithm for two turbulence models: the simplified Bardina model and the Navier-Stokes-α model. Our approach involves creating an approximate solution for the turbulence models by employing an interpolant operator based on the observational data of the systems. The estimation depends on the parameter alpha in the models. Additionally, numerical simulations are presented to validate our theoretical results |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Parameter Analysis in Continuous Data Assimilation for Various Turbulence Models Albanez, Debora A. F. Benvenutti, Maicon Jose Little, Samuel Tian, Jing Fluid Dynamics Numerical Analysis Mathematical Physics In this study, we conduct parameter estimation analysis on a data assimilation algorithm for two turbulence models: the simplified Bardina model and the Navier-Stokes-α model. Our approach involves creating an approximate solution for the turbulence models by employing an interpolant operator based on the observational data of the systems. The estimation depends on the parameter alpha in the models. Additionally, numerical simulations are presented to validate our theoretical results |
| title | Parameter Analysis in Continuous Data Assimilation for Various Turbulence Models |
| topic | Fluid Dynamics Numerical Analysis Mathematical Physics |
| url | https://arxiv.org/abs/2409.03042 |