MLS pressure boundaries for divergence-free and viscous SPH fluids

Band, Stefan; Gissler, Christoph; Peer, Andreas; Teschner, Matthias

doi:10.1016/j.cag.2018.08.001

Cited by 27 publications

(38 citation statements)

References 44 publications

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“…SPH setting : We have integrated our novel CLL variant and the compressed neighbour representation into a DFSPH framework [BK17, BGPT18]. If not stated otherwise, we use the Wendland

C^{6}

kernel [Wen95] with a support ℏ of two times the particle spacing h for all SPH interpolations.…”

Section: Resultsmentioning

confidence: 99%

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Compressed Neighbour Lists for SPH

Band

Gissler

Teschner

2019

Computer Graphics Forum

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We propose a novel compression scheme to store neighbour lists for iterative solvers that employ Smoothed Particle Hydrodynamics (SPH). The compression scheme is inspired by Stream VByte, but uses a non‐linear mapping from data to data bytes, yielding memory savings of up to 87%. It is part of a novel variant of the Cell‐Linked‐List (CLL) concept that is inspired by compact hashing with an improved processing of the cell‐particle relations. We show that the resulting neighbour search outperforms compact hashing in terms of speed and memory consumption. Divergence‐Free SPH (DFSPH) scenarios with up to 1.3 billion SPH particles can be processed on a 24‐core PC using 172 GB of memory. Scenes with more than 7 billion SPH particles can be processed in a Message Passing Interface (MPI) environment with 112 cores and 880 GB of RAM. The neighbour search is also useful for interactive applications. A DFSPH simulation step for up to 0.2 million particles can be computed in less than 40 ms on a 12‐core PC.

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“…SPH setting : We have integrated our novel CLL variant and the compressed neighbour representation into a DFSPH framework [BK17, BGPT18]. If not stated otherwise, we use the Wendland

C^{6}

kernel [Wen95] with a support ℏ of two times the particle spacing h for all SPH interpolations.…”

Section: Resultsmentioning

confidence: 99%

“…Solid boundaries are represented with a single particle layer of non‐uniform size [AIA*12]. Pressure forces at solid boundaries are computed with extrapolated pressure according to [BGPT18].…”

Section: Resultsmentioning

confidence: 99%

Compressed Neighbour Lists for SPH

Band

Gissler

Teschner

2019

Computer Graphics Forum

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“…(52) is to use an additional particle discretization for the boundary, e.g. , [IAGT10, AIA*12, BGPT18, BGI*18, GPB*19]. The boundary particles serve as additional sampling points and typically have the same radius as the fluid particles.…”

Section: Boundary Handlingmentioning

confidence: 99%

“…Such variants can be referred to as local pressure solvers, e.g. , [HLL*12,BGPT18]. In contrast, variants of the second option conceptually solve a system in the pressure computation.…”

Section: Incompressibilitymentioning

confidence: 99%

A Survey on SPH Methods in Computer Graphics

Koschier¹,

Bender

Solenthaler

et al. 2022

Computer Graphics Forum

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Throughout the past decades, the graphics community has spent major resources on the research and development of physics simulators on the mission to computer-generate behaviors achieving outstanding visual effects or to make the virtual world indistinguishable from reality. The variety and impact of recent research based on Smoothed Particle Hydrodynamics (SPH) demonstrates the concept's importance as one of the most versatile tools for the simulation of fluids and solids. With this survey, we offer an overview of the developments and still-active research on physics simulation methodologies based on SPH that has not been addressed in previous SPH surveys. Following an introduction about typical SPH discretization techniques, we provide an overview over the most used incompressibility solvers and present novel insights regarding their relation and conditional equivalence. The survey further covers recent advances in implicit and particle-based boundary handling and sampling techniques. While SPH is best known in the context of fluid simulation we discuss modern concepts to augment the range of simulatable physical characteristics including turbulence, highly viscous matter, deformable solids, as well as rigid body contact handling. Besides the purely numerical approaches, simulation techniques aided by machine learning are on the rise. Thus, the survey discusses recent data-driven approaches and the impact of differentiable solvers on artist control. Finally, we provide context for discussion by outlining existing problems and opportunities to open up new research directions.

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“…The simplicity of the force formulation allows coupling Eulerian fluid simulations with particlebased solid simulations [26] and particle-based fluid simulations with deformable solids [27]- [29] and, using continuous collision detection, sheet-based cloth simulations [30], [31]. Recent work on handling collisions between fluid particles and rigid boundaries include density maps [32], volume maps [33], liquid boundaries [34] and pressure boundaries [35], [36]. Density maps can also be augmented to handle frictional contact [37], and strong fluid-rigid body coupling simulation is achieved by interlinked pressure solvers [38].…”

Section: Related Workmentioning

confidence: 99%

Particle Merging-and-Splitting

Truong

Yuksel

Watcharopas

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Robustly handling collisions between individual particles in a large particle-based simulation has been a challenging problem. We introduce particle merging-and-splitting, a simple scheme for robustly handling collisions between particles that prevents inter-penetrations of separate objects without introducing numerical instabilities. This scheme merges colliding particles at the beginning of the time-step and then splits them at the end of the time-step. Thus, collisions last for the duration of a time-step, allowing neighboring particles of the colliding particles to influence each other. We show that our merging-and-splitting method is effective in robustly handling collisions and avoiding penetrations in particle-based simulations. We also show how our merging-and-splitting approach can be used for coupling different simulation systems using different and otherwise incompatible integrators. We present simulation tests involving complex solid-fluid interactions, including solid fractures generated by fluid interactions.

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MLS pressure boundaries for divergence-free and viscous SPH fluids

Cited by 27 publications

References 44 publications

Compressed Neighbour Lists for SPH

Compressed Neighbour Lists for SPH

A Survey on SPH Methods in Computer Graphics

Particle Merging-and-Splitting

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