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Predictive-corrective incompressible SPH


Solenthaler, B; Pajarola, R (2009). Predictive-corrective incompressible SPH. ACM Transactions on Graphics, 28(3):401-406.

Abstract

We present a novel, incompressible fluid simulation method based
on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model.
In our method, incompressibility is enforced by using a predictioncorrection scheme to determine the particle pressures. For this,the information about density fluctuations is actively propagated through the fluid and pressure values are updated until the targeted density is satisfied. With this approach, we avoid the computational expenses of solving a pressure Poisson equation, while still being able to use large time steps in the simulation. The achieved results show that our predictive-corrective incompressible SPH (PCISPH) method clearly outperforms the commonly used weakly compressible SPH (WCSPH) model by more than an order of magnitude while the computations are in good agreement with the WCSPH results.

Abstract

We present a novel, incompressible fluid simulation method based
on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model.
In our method, incompressibility is enforced by using a predictioncorrection scheme to determine the particle pressures. For this,the information about density fluctuations is actively propagated through the fluid and pressure values are updated until the targeted density is satisfied. With this approach, we avoid the computational expenses of solving a pressure Poisson equation, while still being able to use large time steps in the simulation. The achieved results show that our predictive-corrective incompressible SPH (PCISPH) method clearly outperforms the commonly used weakly compressible SPH (WCSPH) model by more than an order of magnitude while the computations are in good agreement with the WCSPH results.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2009
Deposited On:08 Feb 2010 16:49
Last Modified:23 Sep 2018 05:13
Publisher:Association for Computing Machinery
ISSN:0730-0301
Additional Information:© ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Trans. Graph. 28, 3, Article 40 (August 2009), 6 pages. DOI = 10.1145/1531326.1531346
OA Status:Green
Publisher DOI:https://doi.org/10.1145/1531326.1531346
Related URLs:http://tog.acm.org/

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