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Computational simulation data using the Lattice Boltzmann method to generate correlations for gas diffusion layer parameters.

ABSTRACT: Analyzing the fluid behavior in complex porous media like gas diffusion layers (GDLs) in polymer electrolyte fuel cells (PEFCs) can be accurately done using the lattice Boltzmann method (LBM). This article shows the data obtained from a study in which diffusion parameters such as porosity, gas phase tortuosity and diffusibility are computed considering simulated porous media [1]. The data were computed when a water drop obstacle is placed inside the GDL domain and the size of the water-drop is varied. Additionally, figures showing the evolution of the flow velocity field are presented alongside graphics that presents the change in local and bulk porosity for each obstacle size. Finally, there is a detailed method explanation concerning the implementation of the lattice Boltzmann method and a general description of computational codes for the domain and obstacle generation as well as the boundary conditions simulation. Data and processes in this article can be exploited in new attempts to solve real case problems in complex mesoscale media.

SUBMITTER: Espinoza-Andaluz M

PROVIDER: S-EPMC6838375 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Computational simulation data using the Lattice Boltzmann method to generate correlations for gas diffusion layer parameters.

Espinoza-Andaluz Mayken M Reyna Raul R Qi Yuanxin Y Li Tingshuai T Andersson Martin M

Data in brief 20191019

Analyzing the fluid behavior in complex porous media like gas diffusion layers (GDLs) in polymer electrolyte fuel cells (PEFCs) can be accurately done using the lattice Boltzmann method (LBM). This article shows the data obtained from a study in which diffusion parameters such as porosity, gas phase tortuosity and diffusibility are computed considering simulated porous media [1]. The data were computed when a water drop obstacle is placed inside the GDL domain and the size of the water-drop is v ...[more]

PMID: 31720338

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Project description:Ionic transport in solid electrolytes can often be approximated as ions performing a sequence of hops between distinct lattice sites. If these hops are uncorrelated, quantitative relationships can be derived that connect microscopic hopping rates to macroscopic transport coefficients; i.e. tracer diffusion coefficients and ionic conductivities. In real materials, hops are uncorrelated only in the dilute limit. At non-dilute concentrations, the relationships between hopping frequency, diffusion coefficient and ionic conductivity deviate from the random walk case, with this deviation quantified by single-particle and collective correlation factors, f and fI, respectively. These factors vary between materials, and depend on the concentration of mobile particles, the nature of the interactions, and the host lattice geometry. Here, we study these correlation effects for the garnet lattice using lattice-gas Monte Carlo simulations. We find that, for non-interacting particles (volume exclusion only), single-particle correlation effects are more significant than for any previously studied three-dimensional lattice. This is attributed to the presence of two-coordinate lattice sites, which causes correlation effects intermediate between typical three-dimensional and one-dimensional lattices. Including nearest-neighbour repulsion and on-site energies produces more complex single-particle correlations and introduces collective correlations. We predict particularly strong correlation effects at xLi=3 (from site energies) and xLi=6 (from nearest-neighbour repulsion), where xLi=9 corresponds to a fully occupied lithium sublattice. Both effects are consequences of ordering of the mobile particles. Using these simulation data, we consider tuning the mobile-ion stoichiometry to maximize the ionic conductivity, and show that the 'optimal' composition is highly sensitive to the precise nature and strength of the microscopic interactions. Finally, we discuss the practical implications of these results in the context of lithium garnets and other solid electrolytes.

Dataset Information

Computational simulation data using the Lattice Boltzmann method to generate correlations for gas diffusion layer parameters.

Publications

Computational simulation data using the Lattice Boltzmann method to generate correlations for gas diffusion layer parameters.

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