PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911287852662784 |
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| author | Dreisbach, Maximilian Kiyani, Elham Kriegseis, Jochen Karniadakis, George Stroh, Alexander |
| author_facet | Dreisbach, Maximilian Kiyani, Elham Kriegseis, Jochen Karniadakis, George Stroh, Alexander |
| contents | Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conventional optical diagnostics, including shadowgraphy and particle image velocimetry, provide valuable insights but are limited to two-dimensional projections of inherently three-dimensional flows. We employ a specialized optical technique that encodes droplet surface information through color-coded glare points, enabling enhanced reconstruction of gas-liquid interfaces. To interpret these measurements, we introduce video-conditioned physics-informed neural networks (VcPINNs), which integrate experimental observations with governing fluid dynamics equations. This hybrid framework leverages the strengths of both data-driven learning and physical constraints, allowing accurate volumetric flow reconstruction from limited input images. Applied to droplet impingement experiments, our method yields highly resolved and physically consistent 3D interface and flow dynamics. The combined imaging and PINN reconstruction strategy provides a powerful platform for advancing multiphase-flow analysis, with broad potential impact across energy, cooling, and propulsion applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction Dreisbach, Maximilian Kiyani, Elham Kriegseis, Jochen Karniadakis, George Stroh, Alexander Fluid Dynamics Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conventional optical diagnostics, including shadowgraphy and particle image velocimetry, provide valuable insights but are limited to two-dimensional projections of inherently three-dimensional flows. We employ a specialized optical technique that encodes droplet surface information through color-coded glare points, enabling enhanced reconstruction of gas-liquid interfaces. To interpret these measurements, we introduce video-conditioned physics-informed neural networks (VcPINNs), which integrate experimental observations with governing fluid dynamics equations. This hybrid framework leverages the strengths of both data-driven learning and physical constraints, allowing accurate volumetric flow reconstruction from limited input images. Applied to droplet impingement experiments, our method yields highly resolved and physically consistent 3D interface and flow dynamics. The combined imaging and PINN reconstruction strategy provides a powerful platform for advancing multiphase-flow analysis, with broad potential impact across energy, cooling, and propulsion applications. |
| title | PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2411.15949 |