Turbulence model augmented physics informed neural networks for mean flow reconstruction
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866914692473028608 |
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| author | Patel, Yusuf Mons, Vincent Marquet, Olivier Rigas, Georgios |
| author_facet | Patel, Yusuf Mons, Vincent Marquet, Olivier Rigas, Georgios |
| contents | Experimental measurements and numerical simulations of turbulent flows are characterised by a trade-off between accuracy and resolution. In this study, we combine accurate sparse pointwise mean velocity measurements with the Reynolds-Averaged Navier-Stokes (RANS) equations using data assimilation methods. Importantly, we bridge the gap between data assimilation (DA) using Physics-Informed Neural Networks (PINNs) and variational methods based on classical spatial discretisation of the flow equations, by comparing both approaches on the same turbulent flow case. Firstly, by constraining the PINN with sparse data and the under-determined RANS equations without closure, we show that the mean flow is reconstructed to a higher accuracy than a RANS solver using the Spalart-Allmaras (SA) turbulence model. Secondly, we propose the SA turbulence model augmented PINN (PINN-DA-SA), which outperforms the former approach by up to 73% reduction in mean velocity reconstruction error with coarse measurements. The additional SA physics constraints improve flow reconstructions in regions with high velocity and pressure gradients and separation. Thirdly, we compare the PINN-DA-SA approach to a variational data assimilation using the same sparse velocity measurements and physics constraints. The PINN-DA-SA achieves lower reconstruction error across a range of data resolutions. This is attributed to discretisation errors in the variational methodology that are avoided by PINNs. We demonstrate the method using high fidelity measurements from direct numerical simulation of the turbulent periodic hill at Re = 5600. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_01065 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Turbulence model augmented physics informed neural networks for mean flow reconstruction Patel, Yusuf Mons, Vincent Marquet, Olivier Rigas, Georgios Fluid Dynamics Computational Physics Experimental measurements and numerical simulations of turbulent flows are characterised by a trade-off between accuracy and resolution. In this study, we combine accurate sparse pointwise mean velocity measurements with the Reynolds-Averaged Navier-Stokes (RANS) equations using data assimilation methods. Importantly, we bridge the gap between data assimilation (DA) using Physics-Informed Neural Networks (PINNs) and variational methods based on classical spatial discretisation of the flow equations, by comparing both approaches on the same turbulent flow case. Firstly, by constraining the PINN with sparse data and the under-determined RANS equations without closure, we show that the mean flow is reconstructed to a higher accuracy than a RANS solver using the Spalart-Allmaras (SA) turbulence model. Secondly, we propose the SA turbulence model augmented PINN (PINN-DA-SA), which outperforms the former approach by up to 73% reduction in mean velocity reconstruction error with coarse measurements. The additional SA physics constraints improve flow reconstructions in regions with high velocity and pressure gradients and separation. Thirdly, we compare the PINN-DA-SA approach to a variational data assimilation using the same sparse velocity measurements and physics constraints. The PINN-DA-SA achieves lower reconstruction error across a range of data resolutions. This is attributed to discretisation errors in the variational methodology that are avoided by PINNs. We demonstrate the method using high fidelity measurements from direct numerical simulation of the turbulent periodic hill at Re = 5600. |
| title | Turbulence model augmented physics informed neural networks for mean flow reconstruction |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2306.01065 |