[2M15-21] 大規模シミュレーション

[2M20] Physics-Informed Neural Networks for Two-Phase Flow Simulations: An Integrated Approach with Advanced Interface Tracking Methods

*Wen Zhou¹, Shuichiro Miwa¹, Koji Okamoto¹ (1. UTokyo)

キーワード：physics-informed neural networks, Navier-Stokes equations, interface tracking methods, two-phase flow

This study aims to develop a robust and generic physics-informed neural networks (PINNs) for two phase flows by incorporating the Navier-Stokes equations and three advanced interface tracking methods—specifically, the Volume of Fluid (VOF), Level Set (LS), and Phase-Field (PF)—into an improved PINNs framework. The case of single rising bubble in two-phase flows is simulated to validate the accuracy of the improved PINNs. The accuracy of the simulations is compared with the velocity, pressure, and phase field against CFD solutions. The results indicate that the improved PINNs coupled with these interface tracking methods offers a satisfactory consistency in simulating rising bubble.