MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910921368010752 |
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| author | Shadkhah, Mehdi Tali, Ronak Rabeh, Ali Yang, Cheng-Hau Herron, Ethan Upadhyaya, Abhisek Krishnamurthy, Adarsh Hegde, Chinmay Balu, Aditya Ganapathysubramanian, Baskar |
| author_facet | Shadkhah, Mehdi Tali, Ronak Rabeh, Ali Yang, Cheng-Hau Herron, Ethan Upadhyaya, Abhisek Krishnamurthy, Adarsh Hegde, Chinmay Balu, Aditya Ganapathysubramanian, Baskar |
| contents | Multiphase fluid dynamics, such as falling droplets and rising bubbles, are critical to many industrial applications. However, simulating these phenomena efficiently is challenging due to the complexity of instabilities, wave patterns, and bubble breakup. This paper investigates the potential of scientific machine learning (SciML) to model these dynamics using neural operators and foundation models. We apply sequence-to-sequence techniques on a comprehensive dataset generated from 11,000 simulations, comprising 1 million time snapshots, produced with a well-validated Lattice Boltzmann method (LBM) framework. The results demonstrate the ability of machine learning models to capture transient dynamics and intricate fluid interactions, paving the way for more accurate and computationally efficient SciML-based solvers for multiphase applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07080 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics Shadkhah, Mehdi Tali, Ronak Rabeh, Ali Yang, Cheng-Hau Herron, Ethan Upadhyaya, Abhisek Krishnamurthy, Adarsh Hegde, Chinmay Balu, Aditya Ganapathysubramanian, Baskar Fluid Dynamics Multiphase fluid dynamics, such as falling droplets and rising bubbles, are critical to many industrial applications. However, simulating these phenomena efficiently is challenging due to the complexity of instabilities, wave patterns, and bubble breakup. This paper investigates the potential of scientific machine learning (SciML) to model these dynamics using neural operators and foundation models. We apply sequence-to-sequence techniques on a comprehensive dataset generated from 11,000 simulations, comprising 1 million time snapshots, produced with a well-validated Lattice Boltzmann method (LBM) framework. The results demonstrate the ability of machine learning models to capture transient dynamics and intricate fluid interactions, paving the way for more accurate and computationally efficient SciML-based solvers for multiphase applications. |
| title | MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2502.07080 |