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DTSTAMP:20260114T163641Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T160000
DTEND;TZID=Australia/Melbourne:20231212T161000
UID:siggraphasia_SIGGRAPH Asia 2023_sess121_papers_671@linklings.com
SUMMARY:An Implicitly Stable Mixture Model for Dynamic Multi-fluid Simulat
 ions
DESCRIPTION:Yanrui Xu (University of Groningen, University of Science and 
 Technology Beijing); Xiaokun Wang (University of Science and Technology Be
 ijing, Bournemouth University); Jiamin Wang, Chongming Song, Tiancheng Wan
 g, and Yanlan Zhang (University of Science and Technology Beijing); Jian C
 hang and Jianjun Zhang (Bournemouth University); Jiri Kosinka (University 
 of Groningen); Alexandru Telea (Utrecht University); and Xiaojuan Ban (Uni
 versity of Science and Technology Beijing)\n\nParticle-based simulation ha
 s become increasingly popular in real-time applications due to its efficie
 ncy and adaptability, especially in generating highly dynamic fluid effect
 s. Nevertheless, the swift and stable simulation of interactions between d
 istinct fluids continues to pose challenges for current mixture model tech
 niques. When using a single mixture flow field to represent all fluid phas
 es, numerical discontinuities in phase fields can result in significant lo
 sses of dynamic effects and unstable conservation of mass and momentum.\nT
 o tackle these issues, we present an advanced implicit mixture model for s
 moothed particle hydrodynamics. Instead of relying on an explicit mixture 
 field for all dynamic computations and phase transfers between particles, 
 our approach calculates phase momentum sources from the mixture model to d
 erive explicit, continuous velocity phase fields. We then implicitly obtai
 n the mixture field using our proposed phase-mixture momentum mapping mech
 anism, ensuring the conservation of incompressibility, mass, and momentum.
  In addition, we propose a mixture viscosity model and establish viscous e
 ffects between the mixture and individual fluid phases to avoid instabilit
 y under extreme inertia conditions.\nThrough a series of experiments, we s
 how that our method effectively improves dynamic effects compared to exist
 ing mixture models while reducing critical instability factors. This makes
  our approach particularly well-suited for long-duration, efficiency-orien
 ted virtual reality scenarios.\n\nRegistration Category: Full Access\n\nSe
 ssion Chair: Christopher Batty (University of Waterloo)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_671&sess=sess121
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