A Neural Particle Method with Interface Tracking and Adaptive Particle Refinement for Free Surface Flows (Communications in Computational Physics, 2024)
This paper is devoted to a new neural particle method (NPM) based onphysics-informed neural networks (PINNs) for modeling free surface flows. Utilizing interface tracking techniques and machine learning (ML) modeling, the new NPMapproach with interface tracking and adaptive particle refinement (NPM-LA) is suggested. This method encompasses properties of tracking the interface particles and ensuring the preservation of the designated distribution pattern for interior fluid (computational) particles. The determination of the corresponding physical quantities atthese particles is accomplished through the process of inference, a distinctive featurefacilitated by ML. The proposed NPM-LA effectively provides solutions for both appropriately tracking the morphology of complex flow surfaces and enhancing the accuracy by dynamically redistributing particles into desired patterns within the computational domain. Two testing cases (the 2D Poiseuille flow problem and a rotatingsquare patch of inviscid fluid) are adopted to examine the performance of the proposedNPM-LA method. The applications to experiments of dam break and wave breakingproblems are explored for demonstrating the capability of capturing the complex deforming flow surface.
Keywords: Neural particle method (NPM); Lagrangian approach; adaptive particle refinement; interface tracking; physics-informed neural networks (PINNs).