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Pore structures and gas transport properties in porous separators for polymer electrolyte fuel cells are evaluated both experimentally and through simulations. In the experiments, the gas permeabilities of two porous samples, a conventional sample and one with low electrical resistivity, are measured by a capillary flow porometer, and the pore size distributions are evaluated with mercury porosimetry. Local pore structures are directly observed with micro X-ray computed tomography (CT). In the simulations, the effective diffusion coefficients of oxygen and the air permeability in porous samples are calculated using random walk Monte Carlo simulations and computational fluid dynamics (CFD) simulations, respectively, based on the X-ray CT images. The calculated porosities and air permeabilities of the porous samples are in good agreement with the experimental values. The simulation results also show that the in-plane permeability is twice the through-plane permeability in the conventional sample, whereas it is slightly higher in the low-resistivity sample. The results of this study show that CFD simulation based on micro X-ray CT images makes it possible to evaluate anisotropic gas permeabilities in anisotropic porous media.
We present pore-scale simulations of two-phase flows in a reconstructed fibrous porous layer. The three dimensional microstructure of the material, a fuel cell gas diffusion layer, is acquired via X-ray computed tomography and used as input for latti
We show that the permeability of porous media can be reliably predicted from the Minkowski tensors (MTs) describing the pore microstructure geometry. To this end, we consider a large number of simulations of flow through periodic unit cells containin
Radiography with cosmic ray muon scattering has proven to be a successful method of imaging nuclear material through heavy shielding. Of particular interest is monitoring dry storage casks for diversion of plutonium contained in spent reactor fuel. U
A hybrid computational method coupling the lattice-Boltzmann (LB) method and a Langevin-dynamics (LD) method is developed to simulate nanoscale particle and polymer (NPP) suspensions in the presence of both thermal fluctuation and long-range many-bod
Low dose computed tomography (LDCT) is desirable for both diagnostic imaging and image guided interventions. Denoisers are openly used to improve the quality of LDCT. Deep learning (DL)-based denoisers have shown state-of-the-art performance and are