Effective conductivity in random porous media with convex and non-convex porosity

Chueh, Chih‐Che; Bertei, Antonio; Pharoah, Jon G.; Nicolella, Cristiano

doi:10.1016/j.ijheatmasstransfer.2013.12.041

Cited by 44 publications

(18 citation statements)

References 39 publications

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“…k eff represents the effective (or relative) conductivity factor of the ion-conducting phase within the composite domain. k eff is defined as the ratio between the effective conductivity of the ionic phase and its bulk conductivity, 9,46 that is, k eff = σ O,eff /σ O . Sometimes k eff is defined as the ratio between the volume fraction and the tortuosity factor of the ion-conducting phase in the composite domain, 47,48 which is a definition equivalent to that adopted in this study.…”

Section: Modellingmentioning

confidence: 99%

Guidelines for the Rational Design and Engineering of 3D Manufactured Solid Oxide Fuel Cell Composite Electrodes

Bertei

Tariq

Yufit

et al. 2016

J. Electrochem. Soc.

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The growth of 3D printing has opened the scope for designing microstructures for solid oxide fuel cells (SOFCs) with improved power density and lifetime. This technique can introduce structural modifications at a scale larger than particle size but smaller than cell size, such as by inserting electrolyte pillars of ∼5-100 μm. This study sets the minimum requirements for the rational design of 3D printed electrodes based on an electrochemical model and analytical solutions for functional layers with negligible electronic resistance and no mixed conduction. Results show that this structural modification enhances the power density when the ratio k eff between effective conductivity and bulk conductivity of the ionic phase is smaller than 0.5. The maximum performance improvement is predicted as a function of k eff . A design study on a wide range of pillar shapes indicates that improvements are achieved by any structural modification which provides ionic conduction up to a characteristic thickness ∼10-40 μm without removing active volume at the electrolyte interface. The best performance is reached for thin (<∼2 μm) and long (>∼80 μm) pillars when the composite electrode is optimised for maximum three-phase boundary density, pointing toward the design of scaffolds with well-defined geometry and fractal structures. Many studies in the literature have recognised the importance of the electrode microstructure in affecting the power density and the lifetime of solid oxide fuel cells (SOFCs).1-7 Significant enhancements in power density have been achieved through the statistical control of the microstructural properties by tuning porosity, 8,9 volume fractions, 2,8,10 particle size 11-13 and even tortuosity of the phases.14 However, nowadays the design of the electrode microstructure can be optimised with unprecedented precision through the advent of 3D printing and additive manufacturing techniques. 15-17Additive manufacturing allows for the development of electrodes with 3D architectures, thus going beyond what conventional fabrication techniques, such as tape casting and screen printing, can produce. The exploitation of 3D additive manufacturing has been successfully applied by the battery community [18][19][20] and the implementation of this approach to SOFCs is currently under investigation.15,21 One of the most promising applications consists of the insertion of ionconducting pillars connected to the electrolyte 15,[22][23][24][25] as reported in Figure 1. Such a modification can be regarded as the mesoscale structural control of electrode microstructure, where the term mesoscale here refers to a dimension larger than the typical constituent particle size (<1 μm) but smaller than the characteristic length of the cell, that is, falling in the order of 5-100 μm.26 Ideally, 3D printing offers the opportunity to controllably manufacture pillars of any shape, with the desired geometric and spacing requirements, in order to provide a preferential pathway for ionic conduction. 22,25 It is important to note that this mesos...

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Section: Modellingmentioning

confidence: 99%

Guidelines for the Rational Design and Engineering of 3D Manufactured Solid Oxide Fuel Cell Composite Electrodes

Bertei

Tariq

Yufit

et al. 2016

J. Electrochem. Soc.

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Section: Supplementary Information Appendix a Derivation Of Performanmentioning

confidence: 99%

“…(a): A 2D sketch of the considered system model, where L is the distance between the TX and RX. (b): A 3D sample of a PM[13]. (c): Illustration of molecular transport through a PM with heterogeneous advection[10], where the red lines represent streamlines of the laminar flow; the shading of the background denotes the flow velocity which decreases from light to dark; the horizontal arrow denotes transport of molecules over the length of a pore in streamwise direction; and the vertical arrow indicates transport of molecules across streamlines into low velocity zones in the wake of the solid grains.…”

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confidence: 99%

Molecular Information Delivery in Porous Media

Fang

Guo

Icardi

et al. 2018

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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Information delivery via molecular signals is abundant in nature and potentially useful for industry sensing. Many propagation channels (e.g., tissue membranes and catalyst beds) contain porous medium materials and the impact this has on communication performance is not well understood. Here, communication through realistic porous channels is investigated for the first time via statistical breakthrough curves. Assuming that the number of arrived molecules can be approximated as a Gaussian random variable and using fully resolved computational fluid dynamics results for the breakthrough curves, the numerical results for the throughput, mutual information, error probability, and information diversity gain are presented. Using these numerical results, the unique characteristics of the porous medium channel are revealed.

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“…The effective thermal conductivity (k eff ) mentioned here is the property to conduct heat through the multi-compositions within the porous media, which can be calculated based on Fourier's law. Along with theoretical solutions, numerical simulations-by taking into account pore distribution by using computational heat transfer methods-have also been employed to study the heat transfer characteristics of porous material, such as lattice Boltzmann method [10], mean-square displacement method [11], finite volume method [12,13], the volume averaging theory [14], and the shooting method [15]. The determination of effective thermal conductivity is crucial as input data for a number of approaches employed in numerical simulation of heat and fluid flow in porous media, as for instance when the generalized model is employed.…”

Section: Introductionmentioning

confidence: 99%

Heat Conduction in Porous Media Characterized by Fractal Geometry

et al. 2017

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Fractal geometry (fractional Brownian motion-FBM) is introduced to characterize the pore distribution of porous material. Based on this fractal characterization, a mathematical model of heat conduction is presented to study heat conduction behaviors in porous material with a focus on effective thermal conductivity. The role of pore structure on temperature distribution and heat flux is examined and investigated for fractal porous material. In addition, the effects of fractal dimension, porosity, and the ratio of solid-matrix-to-fluid-phase thermal conductivity (k s /k f ) on effective thermal conductivity are evaluated. The results indicate that pore structure has an important effect on heat conduction inside porous material. Increasing porosity lowers thermal conductivity. Even when porosity remains constant, effective thermal conductivity is affected by the fractal dimensions of the porous material. For porous material, the heat conduction capability weakens with increased fractal dimension. Additionally, fluid-phase thermal conduction across pores is effective in porous material only when k s /k f < 50. Otherwise, effective thermal conductivity for porous material with a given pore structure depends primarily on the thermal conductivity of the solid matrix.

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Effective conductivity in random porous media with convex and non-convex porosity

Cited by 44 publications

References 39 publications

Guidelines for the Rational Design and Engineering of 3D Manufactured Solid Oxide Fuel Cell Composite Electrodes

Guidelines for the Rational Design and Engineering of 3D Manufactured Solid Oxide Fuel Cell Composite Electrodes

Molecular Information Delivery in Porous Media

Heat Conduction in Porous Media Characterized by Fractal Geometry

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