CaltechAUTHORS
  A Caltech Library Service

Fast and scalable turbulent flow simulation with two-way coupling

Li, Wei and Chen, Yixin and Desbrun, Mathieu and Zheng, Changxi and Liu, Xiaopei (2020) Fast and scalable turbulent flow simulation with two-way coupling. ACM Transactions on Graphics, 39 (4). Art. No. 47. ISSN 0730-0301. doi:10.1145/3386569.3392400. https://resolver.caltech.edu/CaltechAUTHORS:20201218-140644519

[img] PDF - Published Version
See Usage Policy.

39MB
[img] Video (MPEG) - Supplemental Material
See Usage Policy.

124MB
[img] Archive (ZIP) - Supplemental Material
See Usage Policy.

550kB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20201218-140644519

Abstract

Despite their cinematic appeal, turbulent flows involving fluid-solid coupling remain a computational challenge in animation. At the root of this current limitation is the numerical dispersion from which most accurate Navier-Stokes solvers suffer: proper coupling between fluid and solid often generates artificial dispersion in the form of local, parasitic trains of velocity oscillations, eventually leading to numerical instability. While successive improvements over the years have led to conservative and detail-preserving fluid integrators, the dispersive nature of these solvers is rarely discussed despite its dramatic impact on fluid-structure interaction. In this paper, we introduce a novel low-dissipation and low-dispersion fluid solver that can simulate two-way coupling in an efficient and scalable manner, even for turbulent flows. In sharp contrast with most current CG approaches, we construct our solver from a kinetic formulation of the flow derived from statistical mechanics. Unlike existing lattice Boltzmann solvers, our approach leverages high-order moment relaxations as a key to controlling both dissipation and dispersion of the resulting scheme. Moreover, we combine our new fluid solver with the immersed boundary method to easily handle fluid-solid coupling through time adaptive simulations. Our kinetic solver is highly parallelizable by nature, making it ideally suited for implementation on single- or multi-GPU computing platforms. Extensive comparisons with existing solvers on synthetic tests and real-life experiments are used to highlight the multiple advantages of our work over traditional and more recent approaches, in terms of accuracy, scalability, and efficiency.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3386569.3392400DOIArticle
ORCID:
AuthorORCID
Desbrun, Mathieu0000-0003-3424-6079
Additional Information:© 2020 Association for Computing Machinery. We thank Qiaodong Cui from UC Santa Barbara, Jonas Zehnder from U. of Montreal, and Ziyin Qu from UPenn for sharing their codes for comparisons. We also thank Cangli Yao, Yihui Ma, Yiran Sun and Chenqi Luo from ShanghaiTech University for helping with rendering and video editing, as well as Christian Lessig for comments. Yixin Chen also received generous support from DGene Digital Technology. The train mesh is from aigei.com; the cow mesh is from free3D.com; and the rocket and car meshes are both from cgtrader.com. This work was supported by a startup funding from ShanghaiTech University, and in part by the US National Science Foundation (1717178, 1816041, 1910839). Finally, Mathieu Desbrun gratefully acknowledges the hospitality of ShanghaiTech University during his sabbatical.
Funders:
Funding AgencyGrant Number
ShanghaiTech UniversityUNSPECIFIED
NSFIIS-1717178
NSFIIS-1816041
NSFIIS-1910839
Subject Keywords:Fluid Simulation, Kinetic Theory, Lattice Boltzmann Method, Fluid-Solid Coupling, Immersed Boundary Method
Issue or Number:4
DOI:10.1145/3386569.3392400
Record Number:CaltechAUTHORS:20201218-140644519
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201218-140644519
Official Citation:Wei Li, Yixin Chen, Mathieu Desbrun, Changxi Zheng, and Xiaopei Liu. 2020. Fast and scalable turbulent flow simulation with two-way coupling. ACM Trans. Graph. 39, 4, Article 47 (July 2020), 20 pages. DOI:https://doi.org/10.1145/3386569.3392400
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:107206
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Dec 2020 23:09
Last Modified:16 Nov 2021 19:00

Repository Staff Only: item control page