An advanced hybrid smoothed particle hydrodynamics–fluid implicit particle method on adaptive grid for condensation simulation

Abstract

In this article, we propose a novel hybrid framework by combining smoothed particle hydrodynamics and adaptive narrow band fluid implicit particle method (NB-FLIP) to faithfully model the multiphysical processes involving heat transfer and phase transition, and to precisely simulate the dynamics of condensed droplets moving along intricate objects. We first formulate a governing physical model built upon an improved phase transition model and an augmented on-surface drop analysis method to achieve realistic condensation effects over intricate hydrophilic/hydrophobic interface. To achieve both high-fidelity interactions and high-resolution visual effects, we further develop an adaptive NB-FLIP solver with octree-dictated background grid in order to further enhance the performance of our framework. Experimental results have shown that our approach can be used to efficiently and realistically simulate the small-scale interaction details between condensed drops and complex objects with arbitrary geometry.

Learning frequency‐aware convolutional neural network for spatio‐temporal super‐resolution water surface waves

“…Animation of water surface waves . Navier–Stokes equations based methods have been certificated in solving a variety of volumetric fluids such as condensation 6 and lava 7 but suffer from fine discretization while tracking subtle wave motions that only occur at water surface, which can be alleviated by employing FFT‐based methods. FFT‐based spectrum methods for wave animation date back to References 8 and 9, that describe the waves as a sum of cosine waves.…”