“Auto-Adaptivity: An Optimization-Based Approach to Spatial Adaptivity for Smoke Simulations” by Nielsen, Stamatelos, Bojsen-Hansen and Bridson
Conference:
Type(s):
Entry Number: 01
Title:
- Auto-Adaptivity: An Optimization-Based Approach to Spatial Adaptivity for Smoke Simulations
Presenter(s)/Author(s):
Abstract:
High resolution fluid simulations are commonly used in the visual effects industry to convincingly animate smoke, steam, and explosions. Traditional volumetric fluid solvers operate on dense grids and often spend a lot of time working on empty regions with no visible smoke. We present an efficient sparse fluid solver that effectively skips the inactive space. At the core of this solver is our sparse pressure projection method based on unsmooth aggreggation multigrid that treats the internal boundaries as open, allowing the smoke to freely move into previously inactive regions. We model small-scale motion in the air around the smoke with a noise field, justifying the absence of reliable velocities in the inactive areas.
References:
Ryoichi Ando, Nils Thürey, and Chris Wojtan. 2013. Highly Adaptive Liquid Simulations on Tetrahedral Meshes. ACM Trans. Graph. 32, 4, Article Article 103 (July 2013), 10 pages. https://doi.org/10.1145/2461912.2461982
Frank Losasso, Frédéric Gibou, and Ron Fedkiw. 2004. Simulating Water and Smoke with an Octree Data Structure. In ACM SIGGRAPH 2004 Papers (SIGGRAPH ’04). Association for Computing Machinery, New York, NY, USA, 457–462. https://doi. org/10.1145/1186562.1015745
Michael B. Nielsen, Konstantinos Stamatelos, Morten Bojsen-Hansen, Duncan Brins- mead, Yannick Pomerleau, Marcus Nordenstam, and Robert Bridson. 2018. A Collocated Spatially Adaptive Approach to Smoke Simulation in Bifrost. In ACM SIGGRAPH 2018 Talks (SIGGRAPH ’18). ACM, New York, NY, USA, Article 77, 2 pages. https://doi.org/10.1145/3214745.3214749
Rajsekhar Setaluri, Mridul Aanjaneya, Sean Bauer, and Eftychios Sifakis. 2014. SPGrid: A Sparse Paged Grid Structure Applied to Adaptive Smoke Simulation. ACM Trans. Graph. 33, 6, Article Article 205 (Nov. 2014), 12 pages. https://doi.org/10.1145/ 2661229.2661269
