FAKE Lab.

논문 초록 (Abstract)

Cloth–liquid interaction involves coupled phenomena such as porous flow, absorption, emission, and diffusion, which are difficult to model in a stable and efficient manner in particle-based simulations. In this paper, we present a GPU-based cloth–liquid coupling framework that integrates these processes within a unified Smoothed Particle Hydrodynamics (SPH) pipeline. To improve directional robustness in porous flow near boundaries, we use a virtual-pressure formulation inspired by Darcy’s law, while absorption and emission are handled through saturation-based mass exchange and diffusion is applied as a sequential redistribution stage. For efficient neighbor search, the framework employs a Bitonic sort–based GPU hashing structure. We evaluate the proposed method using qualitative wet-cloth scenarios and quantitative analyses including frame-time measurement, sorting-cost comparison, scalability with increasing particle counts, mass conservation error, and module-wise timing breakdown. The results show that the proposed hashing scheme reduces sorting overhead compared with the baseline GPU implementation, while the full coupling pipeline maintains stable wetting behavior and bounded mass variation across the tested scenes. These results indicate that the proposed framework provides an efficient and numerically stable approach for GPU-based porous cloth–liquid simulation.