FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations
Lattice Boltzmann Method (LBM) has shown great potential in fluid simulations, but performance issues and difficulties to manage complex boundary conditions have hindered a wider application. The upcoming of Graphic Processing Units (GPU) Computing offered a possible solution for the performance iss...
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doaj-1e5e66f9315546dc8927898854f3c2722020-11-24T21:20:04ZengMulti-Science PublishingInternational Journal of Multiphysics1750-95482048-39612017-03-0111110.21152/1750-9548.11.1.1321FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid SimulationsG Boroni0J Dottori1P Rinaldi2CONICET, PLADEMA UNCPBACICPBA, PLADEMA UNCPBACICPBA, PLADEMA UNCPBALattice Boltzmann Method (LBM) has shown great potential in fluid simulations, but performance issues and difficulties to manage complex boundary conditions have hindered a wider application. The upcoming of Graphic Processing Units (GPU) Computing offered a possible solution for the performance issue, and methods like the Immersed Boundary (IB) algorithm proved to be a flexible solution to boundaries. Unfortunately, the implicit IB algorithm makes the LBM implementation in GPU a non-trivial task. This work presents a fully parallel GPU implementation of LBM in combination with IB. The fluid-boundary interaction is implemented via GPU kernels, using execution configurations and data structures specifically designed to accelerate each code execution. Simulations were validated against experimental and analytical data showing good agreement and improving the computational time. Substantial reductions of calculation rates were achieved, lowering down the required time to execute the same model in a CPU to about two magnitude orders.http://journal.multiphysics.org/index.php/IJM/article/view/310 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
G Boroni J Dottori P Rinaldi |
spellingShingle |
G Boroni J Dottori P Rinaldi FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations International Journal of Multiphysics |
author_facet |
G Boroni J Dottori P Rinaldi |
author_sort |
G Boroni |
title |
FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations |
title_short |
FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations |
title_full |
FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations |
title_fullStr |
FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations |
title_full_unstemmed |
FULL GPU Implementation of Lattice-Boltzmann Methods with Immersed Boundary Conditions for Fast Fluid Simulations |
title_sort |
full gpu implementation of lattice-boltzmann methods with immersed boundary conditions for fast fluid simulations |
publisher |
Multi-Science Publishing |
series |
International Journal of Multiphysics |
issn |
1750-9548 2048-3961 |
publishDate |
2017-03-01 |
description |
Lattice Boltzmann Method (LBM) has shown great potential in fluid simulations, but performance issues and difficulties to manage complex boundary conditions have hindered a wider application. The upcoming of Graphic Processing Units (GPU) Computing offered a possible solution for the performance issue, and methods like the Immersed Boundary (IB) algorithm proved to be a flexible solution to boundaries. Unfortunately, the implicit IB algorithm makes the LBM implementation in GPU a non-trivial task. This work presents a fully parallel GPU implementation of LBM in combination with IB. The fluid-boundary interaction is implemented via GPU kernels, using execution configurations and data structures specifically designed to accelerate each code execution. Simulations were validated against experimental and analytical data showing good agreement and improving the computational time. Substantial reductions of calculation rates were achieved, lowering down the required time to execute the same model in a CPU to about two magnitude orders. |
url |
http://journal.multiphysics.org/index.php/IJM/article/view/310 |
work_keys_str_mv |
AT gboroni fullgpuimplementationoflatticeboltzmannmethodswithimmersedboundaryconditionsforfastfluidsimulations AT jdottori fullgpuimplementationoflatticeboltzmannmethodswithimmersedboundaryconditionsforfastfluidsimulations AT prinaldi fullgpuimplementationoflatticeboltzmannmethodswithimmersedboundaryconditionsforfastfluidsimulations |
_version_ |
1726004075155161088 |