Selection of representative volume elements for pore-scale analysis of transport in fuel cell materials

Wargo, Eric A.; Hanna, Anne C.; Cecen, Ahmet; Kalidindi, Surya R.; Kumbur, E. Caglan

doi:10.1016/j.jpowsour.2011.09.035

Cited by 79 publications

(56 citation statements)

References 56 publications

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“…Niezgoda et al [28] showed that an RVE generated with this method can result in minimum deviation in the effective modulus compared to the original microstructure modulus. Similar results have also been presented by Qidwai et al [32] and Wargo et al [33] in very different application domains. Here, the same technique is applied without a reference to the material properties, and to distinguish between both approaches, the ROH approach is used to reflect the weighted representative microstructure without calculating any Figure 8a, 100 windows were sampled from the image and the median and variance within each sample were quantitatively measured in the two-point statistics domain in which the differences can be measured via a Euclidean distance (D) in Figure 8b.…”

Section: Representative Descriptions Of Microstructure Regions Of Homsupporting

confidence: 89%

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

Salem

Shaffer

Satko

et al. 2014

Integr Mater Manuf Innov

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In this paper, a generalized workflow is outlined for the necessary integration of multimodal measurements and multiphysics models at multiple hierarchical length scales demanded by an Integrated Computational Materials Engineering (ICME) approach to accelerated materials development. Recognizing that multiple choices or techniques are typically available in each of the main steps, several exemplary analyses are detailed utilizing mainly the alpha/beta titanium alloys as an illustrative case. It is anticipated that the use and further refinement of these workflows will promote transparency and engender intimate collaborations between materials experts and manufacturing/design specialists by providing an understanding of the various mesoscale heterogeneities that develop naturally in the workpiece as a direct consequence of the inherent heterogeneity imposed by the manufacturing history (i.e., different thermomechanical histories at different locations in the sample). More specifically, this article focuses on three main areas: (i) data science protocols for efficient analysis of large microstructure datasets (e.g., cluster analysis), (ii) protocols for extracting reduced descriptions of salient microstructure features for insertion into simulations (e.g., regions of homogeneity), and (iii) protocols for direct and efficient linking of materials models/databases into process/performance simulation codes (e.g., crystal plasticity finite element method).

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Section: Representative Descriptions Of Microstructure Regions Of Homsupporting

confidence: 89%

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

Salem

Shaffer

Satko

et al. 2014

Integr Mater Manuf Innov

View full text Add to dashboard Cite

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“…In prior work, the protocols described above have been successfully applied to multiphase composite systems [13,14,19,43,44,57,58], atomistic datasets [59,60], and polycrystalline microstructures [42,61]. However, in all of these applications, the microstructure domains had a simple overall shape (rectangles in 2-D and rectangular parallelepipeds in 3-D), periodicity was generally imposed to take advantage of FFT algorithms, and the studies used relatively small domains.…”

Section: Methods: Discretized Microstructure Function and Spatial Cormentioning

confidence: 99%

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Cecen

Fast

Kalidindi

2016

Integr Mater Manuf Innov

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This paper presents a generalized framework along with the associated computational strategies for a rigorous quantification of the material structure in a range of different applications using the framework of 2-point spatial correlations. In particular, we focus on applications requiring different assumptions about the periodicity and/or involving irregular domain shapes and potentially extremely large datasets. Important details of the computational algorithms needed to address these challenges are developed and illustrated with example case studies. Algorithms developed and presented in this work are available at http://dx.doi.org/10.5281/zenodo.31329. Background

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“…For statistics, cubes with varying cube edge lengths from 200 up to 1500 μm were implemented into the RoI, similar to Ref. 31, without overlapping as summarized in Supplementary Table 2. The target criterion was set to a relative error, i.e.…”

mentioning

confidence: 99%

Influence of Operating Conditions and Material Properties on the Mass Transport Losses of Polymer Electrolyte Water Electrolysis

Suermann

Takanohashi

Lamibrac

et al. 2017

J. Electrochem. Soc.

110

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Three different porous transport layer (PTL) structures, based on titanium sintered powders, were characterized using X-ray tomographic microscopy to determine key geometric properties such as porosity, pore and particle size distributions as well as effective transport properties. The mass transport through the PTL contributes to the voltage losses in the polymer electrolyte water electrolysis cell. Therefore, influence of the PTL structure on the mass transport overpotential is investigated as function of current densities (≤ 4 A · cm −2 ), operating pressures (1-100 bar) and temperatures (40-60 • C), respectively. A decrease of transport losses was observed with increasing pressure and temperature for all investigated PTLs. At around 100 bar balanced pressure, the transport losses for all PTLs converge to about 40 mV per applied A · cm −2 , suggesting that other parts of the cell such as the catalyst layer or their interface contribute to these remaining losses. The performance loss, induced by the different PTL structures, shows a stronger correlation with geometric parameters such as pore and particle size distributions than transport properties like effective diffusivity and permeability. The finest materials with d 50 pore and particle diameters of 40-48 and 68 μm, respectively, are performing better than the coarsest material with diameters roughly twice the sizes. Polymer electrolyte water electrolysis (PEWE) is a promising technology to convert (surplus) electric energy into chemical energy in form of hydrogen to avoid curtailment of the fluctuating renewable electricity sources 1 and thus reducing the need of fossil fuels. 2 Depending on the use of hydrogen, the gas has to be provided at pressure levels up to 1000 bar, i.e. for the use in mobility applications.3 Commercial electrolyzers are operated at pressures in the order of 30 bar (hydrogen and optionally oxygen) 4 to reduce downstream compression and drying efforts. 5For energy applications, high efficiency of the electrolysis conversion process is a key property. As in any electrochemical cell, also with PEWE, losses/overpotentials incur when a current is passed. Ohmic and kinetic overpotentials, related to the movement of protons in the polymer electrolyte and the finite rates of the electrochemical reactions are reasonably well understood for PEWE. [6][7][8] In most cases, at least at current densities above about 1 A · cm −2 , losses in addition to ohmic and kinetic sources are observed and related to mass transport resistances in the porous structures of the catalyst and porous transport layers (PTLs), though their nature and origin in PEWE are not well understood.6 Nevertheless, as long as a sufficient water supply is guaranteed, no transport limitations occur in the form of a turning point in the current/voltage characteristics (i/E-curves), as demonstrated by Lewinski et al. 9 up to 19 A · cm −2 . In polymer electrolyte fuel cells (PEFC), a technology similar to PEWE, the effect of mass transport in the micro-porous structures of the el...

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Selection of representative volume elements for pore-scale analysis of transport in fuel cell materials

Cited by 79 publications

References 56 publications

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

Workflow for integrating mesoscale heterogeneities in materials structure with process simulation of titanium alloys

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Influence of Operating Conditions and Material Properties on the Mass Transport Losses of Polymer Electrolyte Water Electrolysis

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