Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids
September 15, 2020 Β· Declared Dead Β· π Eurographics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dan Koschier, Jan Bender, Barbara Solenthaler, Matthias Teschner
arXiv ID
2009.06944
Category
cs.GR: Graphics
Citations
94
Venue
Eurographics
Last Checked
4 months ago
Abstract
Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Graphics
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Deep Bilateral Learning for Real-Time Image Enhancement
R.I.P.
π»
Ghosted
Animating Human Athletics
R.I.P.
π»
Ghosted
BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration
R.I.P.
π»
Ghosted
Shape Transformation Using Variational Implicit Functions
R.I.P.
π»
Ghosted
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted