Volume Maps: An Implicit Boundary Representation for SPH

Bender, Jan; Kugelstadt, Tassilo; Weiler, Marcel; Koschier, Dan

doi:10.1145/3359566.3360077

Cited by 27 publications

(21 citation statements)

References 32 publications

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“…Several other methods for SPH obstacle handling have been presented in recent years. For example, Bender et al [34] have proposed volume maps: a grid in which the obstacle contribution to SPH kernels is precomputed per grid cell. This solves the problems of accuracy and parameter tuning, but it still uses a separate obstacle representation that we prefer to avoid.…”

Section: Obstacle Handlingmentioning

confidence: 99%

“…We now present an obstacle-handling approach that is based directly on the 2D polygonal obstacles in the environment. Our technique borrows several concepts from volume maps [34], but without requiring a grid representation.…”

Section: Sph Obstacle Handlingmentioning

confidence: 99%

“…To compute the contribution of O k to ρ i , we need to approximate an integral over all points in P k . We employ two approximations inspired by volume maps [34]. First, we assume that the density inside P k is equal to the agent's current rest density…”

Section: Density Contribution Of Obstaclesmentioning

confidence: 99%

“…To define the pressure force f wall k exerted by a wall O k , we condense the obstacle region P k into a representative point r p k , and we further assume that the pressure inside P k is equal to p i . This 'pressure mirroring' is used in volume maps as well [34]. This leads to the following equation for f wall k :…”

Section: Pressure Contribution Of Obstaclesmentioning

confidence: 99%

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SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics

Toll

Chatagnon

Braga

et al. 2021

Computers & Graphics

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In highly dense crowds of humans, collisions between people occur often. It is common to simulate such a crowd as one fluid-like entity (macroscopic), and not as a set of individuals (microscopic, agent-based). Agent-based simulations are preferred for lower densities because they preserve the properties of individual people. However, their collision handling is too simplistic for extreme-density crowds. Therefore, neither paradigm is ideal for all possible densities.In this paper, we combine agent-based crowd simulation with Smoothed Particle Hydrodynamics (SPH), a particle-based method that is popular for fluid simulation. We integrate SPH into the crowd simulation loop by treating each agent as a fluid particle. The forces of SPH (for pressure and viscosity) then augment the usual navigation behavior and contact forces per agent. We extend the standard SPH model with a dynamic rest density per particle, which intuitively controls the crowd density that an agent is willing to accept. We also present a simple way to let agents blend between individual navigation and fluid-like interactions depending on the SPH density.Experiments show that SPH improves agent-based simulation in several ways: better stability at high densities, more intuitive control over the crowd density, and easier replication of wave-propagation effects. Also, density-based blending between collision avoidance and SPH improves the simulation of mixed-density scenarios. Our implementation can simulate tens of thousands of agents in real-time. As such, this work successfully prepares the agent-based paradigm for crowd simulation at all densities.

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Section: Obstacle Handlingmentioning

confidence: 99%

Section: Sph Obstacle Handlingmentioning

confidence: 99%

Section: Density Contribution Of Obstaclesmentioning

confidence: 99%

Section: Pressure Contribution Of Obstaclesmentioning

confidence: 99%

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SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics

Toll

Chatagnon

Braga

et al. 2021

Computers & Graphics

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“…Koschier and Bender [KB17] proposed to extend the fluid's density field into the boundary geometry and discretize the function using cubic polynomials on a sparse grid without any dependence on particle sampling. To further improve the performance, Bender et al [BKWK19] suggested not to precompute the density field on the grid, but instead determine the intersection volume between a particle's support domain and the boundary on a coarse grid. Compared to particle-based boundary handling techniques, the grid-based methods shows better performance of neighborhood searches.…”

Section: Related Workmentioning

confidence: 99%

Semi‐analytical Solid Boundary Conditions for Free Surface Flows

Chang

Liu

et al. 2020

Computer Graphics Forum

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Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

Song

Wang

et al. 2023

Computer Animation & Virtual

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Fluid simulation is well-known for being visually stunning while computationally expensive. Spatial adaptivity can effectively ease the computational cost by discretizing the simulation space with varying resolutions. Adaptive methods nowadays mainly focus on the mechanism of refining the fluid surfaces to obtain more vivid splashes and wave effects. But such techniques hinder further performance gain under the condition where most of the vast fluid surface is tranquil. Moreover, energetic flow beneath the surface cannot be adequately captured with the interior of the fluid still being simulated under coarse discretization. This article proposes a novel boundary-distance based adaptive method for smoothed particle hydrodynamics fluid simulation. The signed-distance field constructed with respect to the coupling boundary is introduced to determine particle resolution in different spatial positions. The resolution is maximal within a specific distance to the boundary and decreases smoothly as the distance increases until a threshold is reached. The sizes of the particles are then adjusted towards the resolution via splitting and merging. Additionally, a wake flow preservation mechanism is introduced to keep the particle resolution at a high level for a period of time after a particle flows through the boundary object to prevent the loss of flow details. Experiments show that our method can refine fluid-solid coupling details more efficiently and effectively capture dynamic effects beneath the surface.

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Volume Maps: An Implicit Boundary Representation for SPH

Cited by 27 publications

References 32 publications

SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics

SPH crowds: Agent-based crowd simulation up to extreme densities using fluid dynamics

Semi‐analytical Solid Boundary Conditions for Free Surface Flows

Spatial adaptivity with boundary refinement for smoothed particle hydrodynamics fluid simulation

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