A personal view on pore network models in drying technology

Metzger, Thomas

doi:10.1080/07373937.2018.1512502

Cited by 20 publications

(13 citation statements)

References 39 publications

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“…14, the drying curve tends toward the curve obtained from the desorption isotherm [Eq. (21)] when the external diffusive layer thickness d is increased to very large values compared to H. This is expected on the ground that the diffusive transfer resistance within the network must gradually become negligible compared to the external transfer resistance when d is increased. However, it can be seen that the convergence is quite slow.…”

Section: G Drying For D ) Hmentioning

confidence: 99%

Pore network model of drying with Kelvin effect

2021

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A pore network model of isothermal drying is presented. The model takes into account the capillary effects, the transport of vapor by diffusion, including Knudsen effect, in the gas phase, and the Kelvin effect. The model is seen as a first step toward the simulation of drying in mesoscopic porous materials involving pore sizes between 4 nm and 50 nm. The major issue addressed with the present model is the computation of the menisci mean curvature radius at the boundary of each liquid cluster in conjunction with the Kelvin effect. The impact of Kelvin effect on the drying process is investigated, varying the relative humidity in the ambient air outside the medium. The simulations indicate that the Kelvin effect has a significant impact on the liquid distribution during drying. The evaporation rate is found to fluctuate due to the menisci curvature variations during drying. The simulations also highlight a noticeable non-local equilibrium effect.

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Section: G Drying For D ) Hmentioning

confidence: 99%

Pore network model of drying with Kelvin effect

2021

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“…Different types of PNMs are generally distinguished. These are (i) PNMs for quasi-static drainage invasion processes [1][2][3][4], (ii) PNMs for quasi-static imbibition invasion processes [1,[3][4][5], (iii) PNMs of drainage with phase transition and diffusion of the vapor (especially applied in drying research) [6][7][8], and (iv) PNMs for the computation of dynamic pore scale fluid flow [9][10][11][12][13]. While the first three approaches usually assume quasi-static invasion of the pore space, the fourth approach considers viscous flow of the liquid phase and dynamic invasion of the pore space.…”

Section: Introductionmentioning

confidence: 99%

Pore Network Simulation of Gas-Liquid Distribution in Porous Transport Layers

et al. 2019

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Pore network models are powerful tools to simulate invasion and transport processes in porous media. They are widely applied in the field of geology and the drying of porous media, and have recently also received attention in fuel cell applications. Here we want to describe and discuss how pore network models can be used as a prescriptive tool for future water electrolysis technologies. In detail, we suggest in a first approach a pore network model of drainage for the prediction of the oxygen and water invasion process inside the anodic porous transport layer at high current densities. We neglect wetting liquid films and show that, in this situation, numerous isolated liquid clusters develop when oxygen invades the pore network. In the simulation with narrow pore size distribution, the volumetric ratio of the liquid transporting clusters connected between the catalyst layer and the water supply channel is only around 3% of the total liquid volume contained inside the pore network at the moment when the water supply route through the pore network is interrupted; whereas around 40% of the volume is occupied by the continuous gas phase. The majority of liquid clusters are disconnected from the water supply routes through the pore network if liquid films along the walls of the porous transport layer are disregarded. Moreover, these clusters hinder the countercurrent oxygen transport. A higher ratio of liquid transporting clusters was obtained for greater pore size distribution. Based on the results of pore network drainage simulations, we sketch a new route for the extraction of transport parameters from Monte Carlo simulations, incorporating pore scale flow computations and Darcy flow.

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“…[ 8 ] Modeling has evolved far from that point on, with the current pore network modeling being able to describe even complex pore structures of different pore sizes. [ 9–15 ] Although modeling is well advanced, it still requires a lot of computing power and information on the actual film morphology to predict the drying behavior of realistic pore networks.…”

Section: Introductionmentioning

confidence: 99%

Influence of Layer Thickness on the Drying of Lithium‐Ion Battery Electrodes—Simulation and Experimental Validation

et al. 2021

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In this work, a detailed study of the drying of battery electrodes of different thicknesses is presented. A mathematical model to calculate the solvent loading and film temperature over the drying time is experimentally validated. The model is based on a first study presenting a simulation model to predict the drying course when linear drying kinetics prevail and no resistance exists for solvent transport within the film. To shed some light on the drying behavior of electrode films with different thicknesses, the start of capillary pore emptying is observed using a digital microscope. In the experiments, an onset of capillary transport even before the end of film shrinkage is observed for the electrode films with thicknesses above state‐of‐the‐art‐thickness. A clusterwise drying behavior becomes more distinct for thicker electrodes, with large areas of dry and wet capillaries next to each other, compared to a more homogenous drying of the thin electrodes. Based on these findings, the linear model is extended to consider transport limitations within the porous electrode film in the form of a moving drying front. The experiments show an increasing deviation from the linear model with increasing electrode thickness and the extended simulation, which considers transport resistances within the film, shows good agreement.

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A personal view on pore network models in drying technology

Cited by 20 publications

References 39 publications

Pore network model of drying with Kelvin effect

Pore network model of drying with Kelvin effect

Pore Network Simulation of Gas-Liquid Distribution in Porous Transport Layers

Influence of Layer Thickness on the Drying of Lithium‐Ion Battery Electrodes—Simulation and Experimental Validation

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