Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Enhancement in Realistic Fluid Re-simulation

Authors
Yahui Song, Hongyan Quan, Yuwen Huang
Corresponding Author
Yahui Song
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.49How to use a DOI?
Keywords
re-simulation; realistic details; fluid; LBMSW; ACE enhancement; velocity
Abstract
Re-simulating a 3D fluid result from video while retaining and rendering details is significant in practice, which still remains a difficult task in spite of rapid advancements in this field during the last two decades. Physically driven models can be easily extended to handle fluid, yet they are unable to preserve surface details like breaking waves without the expense of increasing particle densities. This paper proposes a hybrid particle Lattice Boltzmann Model for Shallow Waters (LBMSW) coupling method to simulate fluid with finer details real-time from video. To preserve re-simulation surface details, we couple the detail particles with the distribution function of the LBMSW model. To improve the time performance, we use only surface to re-simulate. Experiments show that the proposed approach can obtain a realistic re-simulation products from video example in several challenging scenarios and we provide qualitative evaluation to the method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.49How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yahui Song
AU  - Hongyan Quan
AU  - Yuwen Huang
PY  - 2016/01
DA  - 2016/01
TI  - Enhancement in Realistic Fluid Re-simulation
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 197
EP  - 200
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.49
DO  - https://doi.org/10.2991/icaita-16.2016.49
ID  - Song2016/01
ER  -