@inproceedings{0f601937294b4cbf917f6f859edd176b,
title = "Fast grid-based fluid dynamics simulation with conservation of momentum and kinetic energy on GPU",
abstract = "Since the computation of fluid animation is often too heavy to run in real-time simulation, we propose a fast grid-based method with parallel acceleration. In order to reduce the cost of computation keeping a balance between fluid stability and diversity, we consider the Navier-Stokes equation on the grid structure with momentum conservation, and introduce the kinetic energy for collision handling and boundary condition. Our algorithm avoids the mass loss during the energy transfer, and can be applied to the two-way coupling with a solid body. Importantly, we propose to use the forward-tracing-based motion and design for parallel computing on Graphics Processing Unit (GPU). In particular, these experiments illustrate the benefits of our method, both in conserving fluid density and momentum. They show that our method is suitable to solve the energy transfer when object interaction is considered during fluid simulation.",
keywords = "Computational fluid dynamics, Kinetic energy, Momentum conservation",
author = "Chan, {Ka Hou} and Im, {Sio Kei}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th International Conference on Image and Graphics, ICIG 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2017",
doi = "10.1007/978-3-319-71598-8_27",
language = "English",
isbn = "9783319715971",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "299--310",
editor = "Yao Zhao and Xiangwei Kong and David Taubman",
booktitle = "Image and Graphics - 9th International Conference, ICIG 2017, Revised Selected Papers",
address = "Germany",
}