Simulation of turbulent bubbly pipe flow with high density ratio and high reynolds number by using the lattice boltzmann method and a multi-phase field model

2021 ◽  
Vol 134 ◽  
pp. 103505
Author(s):  
Yos Panagaman Sitompul ◽  
Takayuki Aoki ◽  
Tomohiro Takaki
Keyword(s):  
Pipe Flow ◽  
Lattice Boltzmann ◽  
High Reynolds Number ◽  
Field Model ◽  
Density Ratio ◽  
Phase Field Model ◽  
High Density Ratio ◽  
High Reynolds ◽  
Multi Phase ◽  
Boltzmann Method

scholarly journals Lattice Boltzmann Method Simulations of High Reynolds Number Flows Past Porous Obstacles

2019 ◽  
Vol 11 (03) ◽  
pp. 1950028 ◽  
Author(s):  
N. M. Sangtani Lakhwani ◽  
F. C. G. A. Nicolleau ◽  
W. Brevis
Keyword(s):  
Reynolds Number ◽  
Lattice Boltzmann Method ◽  
Turbulent Flows ◽  
Lattice Boltzmann ◽  
High Reynolds Number ◽  
Reynolds Numbers ◽  
Navier Stokes Equation ◽  
High Reynolds ◽  
Boltzmann Method ◽  
Reynolds Number Flows

Lattice Boltzmann Method (LBM) simulations for turbulent flows over fractal and non-fractal obstacles are presented. The wake hydrodynamics are compared and discussed in terms of flow relaxation, Strouhal numbers and wake length for different Reynolds numbers. Three obstacle topologies are studied, Solid (SS), Porous Regular (PR) and Porous Fractal (FR). In particular, we observe that the oscillation present in the case of the solid square can be annihilated or only pushed downstream depending on the topology of the porous obstacle. The LBM is implemented over a range of four Reynolds numbers from 12,352 to 49,410. The suitability of LBM for these high Reynolds number cases is studied. Its results are compared to available experimental data and published literature. Compelling agreements between all three tested obstacles show a significant validation of LBM as a tool to investigate high Reynolds number flows in complex geometries. This is particularly important as the LBM method is much less time consuming than a classical Navier–Stokes equation-based computing method and high Reynolds numbers need to be achieved with enough details (i.e., resolution) to predict for example canopy flows.

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