Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912531475333120 |
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| author | Xing, Fulin Li, Junjie Tao, Ze Liu, Fujun Tan, Yong |
| author_facet | Xing, Fulin Li, Junjie Tao, Ze Liu, Fujun Tan, Yong |
| contents | Underwater explosion modeling faces a critical challenge of simultaneously resolving shock waves and gas-liquid interfaces, as traditional methods struggle to balance accuracy and computational efficiency. To address this, we develop a physics-informed neural network (PINN) framework featuring a dual-network architecture, that one network learns flow-field variables (pressure, density, velocity) from simulation data, while another network tracks the gas-liquid interface despite lacking direct numerical solutions. Crucially, we introduce an interval-constraint training strategy that penalizes interface deviations beyond grid spacing limits, paired with a physics-preserving linear mapping of 1D spherical Euler equations to ensure consistency. Our results show that this approach accurately reconstructs spatiotemporal fields from coarse-grid data, achieving superior computational efficiency over conventional CFD-enabling rapid, mesh-free blast-load analysis for near/far-field scenarios and extensibility to higher-dimensional problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks Xing, Fulin Li, Junjie Tao, Ze Liu, Fujun Tan, Yong Fluid Dynamics Underwater explosion modeling faces a critical challenge of simultaneously resolving shock waves and gas-liquid interfaces, as traditional methods struggle to balance accuracy and computational efficiency. To address this, we develop a physics-informed neural network (PINN) framework featuring a dual-network architecture, that one network learns flow-field variables (pressure, density, velocity) from simulation data, while another network tracks the gas-liquid interface despite lacking direct numerical solutions. Crucially, we introduce an interval-constraint training strategy that penalizes interface deviations beyond grid spacing limits, paired with a physics-preserving linear mapping of 1D spherical Euler equations to ensure consistency. Our results show that this approach accurately reconstructs spatiotemporal fields from coarse-grid data, achieving superior computational efficiency over conventional CFD-enabling rapid, mesh-free blast-load analysis for near/far-field scenarios and extensibility to higher-dimensional problems. |
| title | Modeling Dynamic Gas-Liquid Interfaces in Underwater Explosions Using Interval-Constrained Physics-Informed Neural Networks |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2508.07633 |