scholarly journals Hyperbolic model for bacterial movement through an orthotropic two-dimensional porous medium

2015 ◽  
Vol 39 (3-4) ◽  
pp. 1050-1062
Author(s):  
R.M. Hernández ◽  
E. Rincón ◽  
R. Herrera ◽  
A.E. Chávez
Author(s):  
Atul Kumar ◽  
◽  
Lav Kush Kumar ◽  
Shireen Shireen ◽  
◽  
...  

1983 ◽  
Vol 17 (5) ◽  
pp. 704-710
Author(s):  
E. G. Basanskii ◽  
V. M. Kolobashkin ◽  
N. A. Kudryashov

2010 ◽  
Vol 44 (6) ◽  
pp. 1319-1348 ◽  
Author(s):  
Laëtitia Girault ◽  
Jean-Marc Hérard

1996 ◽  
Author(s):  
J. Cruz-Hernandez ◽  
C. Perez-Rosales ◽  
F. Samaniego-V.

2009 ◽  
Vol 20 (2) ◽  
pp. 122-143
Author(s):  
V. I. Dmitriev ◽  
A A. Kantsel’ ◽  
E. S. Kurkina ◽  
N. V. Peskov

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Krzysztof M. Graczyk ◽  
Maciej Matyka

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.


2002 ◽  
Vol 66 (5) ◽  
Author(s):  
Yves Méheust ◽  
Grunde Løvoll ◽  
Knut Jørgen Måløy ◽  
Jean Schmittbuhl

2016 ◽  
Vol 18 (3) ◽  
pp. 355-391
Author(s):  
Maria Calle ◽  
Carlota Maria Cuesta ◽  
Juan Velázquez

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