An efficient adaptive vortex particle method for real-time smoke simulation

• Main Authors: HE, Shengfeng, WONG, Hon-Cheng, WONG, Un-Hong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Computer animation; GPU; Grid-based; Grid-based simulation; Helicities; Hybrid method; Initial conditions; Smoke simulation; Spatial vorticity confinement; Turbulence generation; Velocity field; Vortex particle method; Vortex particles; Vorticity confinement; Zero velocity; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8421; https://ink.library.smu.edu.sg/context/sis_research/article/9424/viewcontent/An_Efficient_Adaptive_Vortex_Particle_Method_for_Real_Time_Smoke_Simulation.pdf
• Institution: Singapore Management University

Smoke simulation is one of the interesting topics in computer animation and it usually involves turbulence generation. Efficient generation of realistic turbulent flows becomes one of the challenges in smoke simulation. Vortex particle method, which is a hybrid method that combines grid-based and particle-based approaches, is often used for generating turbulent details. However, it may cause irrational artifacts due to its initial condition and vorticity forcing approach used. In this paper, a new vorticity forcing approach based on the spatial adaptive vorticity confinement is proposed to address this problem. In this approach, the spatial adaptive vorticity confinement force varies with helicity, leading to the fact that the grid-based simulation driven by the vortex particle is now based on the velocity field. Furthermore, we introduce an adaptive vortex particle approach to improve the computational efficiency of the simulation by making the influencing region adapt with the velocity and eliminating those particles with zero velocity in the vorticity forcing method. A parallel smoke simulator integrating our approaches has been implemented using GPUswith CUDA. Experimental results demonstrate that our proposed methods are efficient and effective for real-time smoke simulation.