Lattice-Boltzmann Method for Macroscopic Porous Media Modeling

1998 ◽  
Vol 09 (08) ◽  
pp. 1491-1503 ◽  
Author(s):  
David M. Freed
Keyword(s):  
Porous Medium ◽  
Porous Media ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Inertial Effects ◽  
Simulation Region ◽  
Flow Through ◽  
Previous Algorithm ◽  
Validation Tests ◽  
Boltzmann Method

An extension to the basic lattice-BGK algorithm is presented for modeling a simulation region as a porous medium. The method recovers flow through a resistance field with arbitrary values of the resistance tensor components. Corrections to a previous algorithm are identified. Simple validation tests are performed which verify the accuracy of the method, and demonstrate that inertial effects give a deviation from Darcy's law for nominal simulation velocities.

scholarly journals Predicting porosity, permeability, and tortuosity of porous media from images by deep learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Krzysztof M. Graczyk ◽  
Maciej Matyka
Keyword(s):  
Neural Networks ◽  
Porous Medium ◽  
Porous Media ◽  
Deep Learning ◽  
Lattice Boltzmann ◽  
Good Accuracy ◽  
Two Dimensional ◽  
Empirical Estimate ◽  
Flow Through ◽  
Boltzmann Method

AbstractConvolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($$\varphi$$ φ ), permeability (k), and tortuosity (T). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. The analysis has been performed for the systems with $$\varphi \in (0.37,0.99)$$ φ ∈ ( 0.37 , 0.99 ) which covers five orders of magnitude a span for permeability $$k \in (0.78, 2.1\times 10^5)$$ k ∈ ( 0.78 , 2.1 × 10 5 ) and tortuosity $$T \in (1.03,2.74)$$ T ∈ ( 1.03 , 2.74 ) . It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and $$\varphi$$ φ has been obtained and compared with the empirical estimate.

Implementation of Multi-GPU Based Lattice Boltzmann Method for Flow Through Porous Media

2015 ◽  
Vol 7 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Changsheng Huang ◽  
Baochang Shi ◽  
Nanzhong He ◽  
Zhenhua Chai
Keyword(s):  
Porous Media ◽  
Lattice Boltzmann Method ◽  
Lattice Boltzmann ◽  
Message Passing Interface ◽  
Gpu Computing ◽  
Processing Unit ◽  
Flow Through Porous Media ◽  
Communication Time ◽  
Flow Through ◽  
Boltzmann Method

AbstractThe lattice Boltzmann method (LBM) can gain a great amount of performance benefit by taking advantage of graphics processing unit (GPU) computing, and thus, the GPU, or multi-GPU based LBM can be considered as a promising and competent candidate in the study of large-scale fluid flows. However, the multi-GPU based lattice Boltzmann algorithm has not been studied extensively, especially for simulations of flow in complex geometries. In this paper, through coupling with the message passing interface (MPI) technique, we present an implementation of multi-GPU based LBM for fluid flow through porous media as well as some optimization strategies based on the data structure and layout, which can apparently reduce memory access and completely hide the communication time consumption. Then the performance of the algorithm is tested on a one-node cluster equipped with four Tesla C1060 GPU cards where up to 1732 MFLUPS is achieved for the Poiseuille flow and a nearly linear speedup with the number of GPUs is also observed.

scholarly journals Lattice Boltzmann method investigation of a reactive electro-kinetic flow in porous media: towards a phenomenological model

2021 ◽  
Vol 379 (2208) ◽  
pp. 20200398 ◽  
Author(s):  
Haijing Li ◽  
Herman J. H. Clercx ◽  
Federico Toschi
Keyword(s):  
Porous Medium ◽  
Porous Media ◽  
Lattice Boltzmann Method ◽  
Conversion Efficiency ◽  
Phenomenological Model ◽  
Electric Potential ◽  
Lattice Boltzmann ◽  
Fluid Velocity ◽  
Reaction Conversion ◽  
Boltzmann Method

A model based on the Lattice Boltzmann method is developed to study the flow of reactive electro-kinetic fluids in porous media. The momentum, concentration and electric/potential fields are simulated via the Navier–Stokes, advection–diffusion/Nernst–Planck and Poisson equations, respectively. With this model, the total density and velocity fields, the concentration of reactants and reaction products, including neutral and ionized species, the electric potential and the interaction forces between the fields can be studied, and thus we provide an insight into the interplay between chemistry, flow and the geometry of the porous medium. The results show that the conversion efficiency of the reaction can be strongly influenced by the fluid velocity, reactant concentration and by porosity of the porous medium. The fluid velocity determines how long the reactants stay in the reaction areas, the reactant concentration controls the amount of the reaction material and with different dielectric constant, the porous medium can distort the electric field differently. All these factors make the reaction conversion efficiency display a non-trivial and non-monotonic behaviour as a function of the flow and reaction parameters. To better illustrate the dependence of the reaction conversion efficiency on the control parameters, based on the input from a number of numerical investigations, we developed a phenomenological model of the reactor. This model is capable of capturing the main features of the causal relationship between the performance of the reactor and the main test parameters. Using this model, one could optimize the choice of reaction and flow parameters in order to improve the performance of the reactor and achieve higher production rates. This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.

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