Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911187959021568 |
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| author | Geshani, Hossein Dehkordi, Mehrdad Raisee Panahi, Masoud Shariat |
| author_facet | Geshani, Hossein Dehkordi, Mehrdad Raisee Panahi, Masoud Shariat |
| contents | We present a data-driven framework for turbulence modeling, applied to flow prediction in the FDA nozzle. In this study, the standard RANS equations have been modified using an implicit-explicit hybrid approach. New variables were introduced, and a solver was developed within the OpenFOAM framework, integrating a machine learning module to estimate these variables. The invariant input features were derived based on Hilbert's basis theorem, and the outputs of the machine learning model were obtained through eigenvalue-vector decomposition of the Reynolds stress tensor. Validation was performed using DNS data for turbulent flow in a square channel at various Reynolds numbers. A baseline MLP was first trained at $Re=2900$ and tested at $Re=3500$ to assess its ability to reproduce turbulence anisotropy and secondary flows. To further enhance generalization, three benchmark DNS datasets were transformed into images via the Deep-Insight method, enabling the use of convolutional neural networks. The trained Deep-Insight network demonstrated improved prediction of turbulence structures in the FDA blood nozzle, highlighting the promise of data-driven augmentation in turbulence modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01091 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle Geshani, Hossein Dehkordi, Mehrdad Raisee Panahi, Masoud Shariat Fluid Dynamics Computational Engineering, Finance, and Science We present a data-driven framework for turbulence modeling, applied to flow prediction in the FDA nozzle. In this study, the standard RANS equations have been modified using an implicit-explicit hybrid approach. New variables were introduced, and a solver was developed within the OpenFOAM framework, integrating a machine learning module to estimate these variables. The invariant input features were derived based on Hilbert's basis theorem, and the outputs of the machine learning model were obtained through eigenvalue-vector decomposition of the Reynolds stress tensor. Validation was performed using DNS data for turbulent flow in a square channel at various Reynolds numbers. A baseline MLP was first trained at $Re=2900$ and tested at $Re=3500$ to assess its ability to reproduce turbulence anisotropy and secondary flows. To further enhance generalization, three benchmark DNS datasets were transformed into images via the Deep-Insight method, enabling the use of convolutional neural networks. The trained Deep-Insight network demonstrated improved prediction of turbulence structures in the FDA blood nozzle, highlighting the promise of data-driven augmentation in turbulence modeling. |
| title | Physics-Informed Machine Learning Approach in Augmenting RANS Models Using DNS Data and DeepInsight Method on FDA Nozzle |
| topic | Fluid Dynamics Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2510.01091 |