Model-based time super-sampling of turbulent flow field sequences
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915329900281856 |
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| author | Li-Hu, Qihong Lorena García-Caspueñas, Patricia Ianiro, Andrea Discetti, Stefano |
| author_facet | Li-Hu, Qihong Lorena García-Caspueñas, Patricia Ianiro, Andrea Discetti, Stefano |
| contents | We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of the Navier-Stokes equations on a data-tailored basis. The basis is obtained from a Proper Orthogonal Decomposition (POD) of the measured fields. Time super-sampling is thus achieved by a time-marching integration of the identified dynamical system, taking the original snapshots as initial conditions. Temporal continuity of the reconstructed velocity fields is achieved through a forward-backwards integration between consecutive measured Particle Image Velocimetry measurements of a turbulent jet flow. The results are compared with the interpolation of the POD temporal coefficients and the low-order reconstruction of data measured at a higher sampling rate. In both cases, the results obtained show the ability of the method to reconstruct the dynamics of the flow with small errors during several flow characteristic times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-based time super-sampling of turbulent flow field sequences Li-Hu, Qihong Lorena García-Caspueñas, Patricia Ianiro, Andrea Discetti, Stefano Fluid Dynamics We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of the Navier-Stokes equations on a data-tailored basis. The basis is obtained from a Proper Orthogonal Decomposition (POD) of the measured fields. Time super-sampling is thus achieved by a time-marching integration of the identified dynamical system, taking the original snapshots as initial conditions. Temporal continuity of the reconstructed velocity fields is achieved through a forward-backwards integration between consecutive measured Particle Image Velocimetry measurements of a turbulent jet flow. The results are compared with the interpolation of the POD temporal coefficients and the low-order reconstruction of data measured at a higher sampling rate. In both cases, the results obtained show the ability of the method to reconstruct the dynamics of the flow with small errors during several flow characteristic times. |
| title | Model-based time super-sampling of turbulent flow field sequences |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2502.14722 |