2017
DOI: 10.1016/j.ijheatmasstransfer.2016.11.097
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Effective gas diffusion coefficient in fibrous materials by mesoscopic modeling

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Cited by 33 publications
(19 citation statements)
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References 42 publications
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“…However, our correlation differs very strongly from the correlation for random orientated overlapping fibers proposed by He et al (2017). For low porosities, the correlation of He et al (2017) proposes effective diffusion factors larger than 1, which is physically unreasonable. It has to be mentioned that our law might slightly underestimate the diffusivity through filamentous fungal networks, because Figure 5 implies that the convergence might not be fully developed for a resolution of 13 voxels.…”
Section: Resultscontrasting
confidence: 99%
“…However, our correlation differs very strongly from the correlation for random orientated overlapping fibers proposed by He et al (2017). For low porosities, the correlation of He et al (2017) proposes effective diffusion factors larger than 1, which is physically unreasonable. It has to be mentioned that our law might slightly underestimate the diffusivity through filamentous fungal networks, because Figure 5 implies that the convergence might not be fully developed for a resolution of 13 voxels.…”
Section: Resultscontrasting
confidence: 99%
“…The correlations for 3D random distributed overlapping fibers (Figure b) fitted our data better than the correlations existing for filamentous microorganisms. Our data fall in between the correlation of Tomadakis and Sotirchos () and He et al (). In both studies, the computation of the effective diffusivity was well validated for other structures like parallel fibers.…”
Section: Resultssupporting
confidence: 88%
“…According to Nam and Kaviany (), the effective diffusivity of isotropic structures is often estimated using a power function of porosity. Thus, He et al () fitted their diffusion results of 3D random distributed overlapping fibers and found keff=αϵn, with α=1.05 and n=3.…”
Section: Resultsmentioning
confidence: 97%
See 1 more Smart Citation
“…Lattice Boltzmann method (LBM) is an efficient numerical method for solving the partial differential equations especially with complicated boundary condition such as porous media and particle suspensions. [38][39][40][41][42][43][44] Different from the traditional computational fluid dynamics (CFD), where the macroscopic governing equations (such as Navier-Stokes equations) are solved directly, LBM solves Boltzmann equation in the finite discrete velocities space, while according to the Chapman-Enskog expansion the Navier-Stokes equations can be recovered. 45 Thus, the basic variables in LBM is the particle distribution function, f i .…”
Section: Lattice Boltzmann Methods (Lbm)mentioning
confidence: 99%