(Invited) Multi-Scale Analysis of Transport in Dry and Partially-Saturated Porous Media
Bibliographic record
Abstract
Improvements in imaging techniques, such as micro- and nano-computer tomography (CT) and focus ion beam scanning electron microscopy (FIB-SEM), have enabled the reconstruction of complex porous media that can then be analyzed by computer simulation to estimate effective transport properties, e.g., ref. [1, 2]. Experimental setups have also been developed to measure dry and partially-saturated gas diffusivity and permeability of gas diffusion layers (GDLs) and catalyst layer (CLs), e.g., ref. [3, 4]. Based on the available literature, a comparison between micro-scale simulation results and experimental data must be performed to assess the accuracy of direct numerical simulation CT image analysis for dry and partially-saturated transport property estimation. In the later case, the validity of computational methods to predict water intrusion, such as full morphology/morphological image opening [1] and watershed segmentation [5], should also be assessed by comparing mercury intrusion experiments to numerical predictions, and by direct comparison of numerical results to CT images. Finally, these transport properties should be integrated into complete fuel cell models where volume-averaging techniques are used to properly estimate channel-porous media interactions. Such interactions however are seldom studied in literature. The proposed contribution aims at first studying the validity of two popular methods for numerical estimation of effective transport properties from three-dimensional reconstructed porous media, i.e., direct numerical simulation (DNS) and pore network modeling (PNM). Two fuel cell gas diffusion media and an electrolyzer porous transport layer are analyzed by CT and characterized by measuring mercury intrusion porosimetry (MIP), and dry permeability and diffusivity. A comparison of numerical and experimental results shows that DNS tools in OpenFCST [6] are capable of accurately predicting intrusion, and transport properties without using any fitting parameters. Accurate predictions are also achieved with the PNM implementation in OpenPNM [7] when the inscribed diameter method is used to estimate the pore size distribution, and the equivalent diameter is used to estimate pore transport properties. These tools open an avenue for computational design of porous materials. Effective transport properties must be integrated into volume-averaged fuel cell models where porous media-channel interactions are of paramount importance. These interactions however, as well as the method used for volume averaging fuel cell equations, are seldom studied in detail. Therefore, the second contribution of the talk aims at analyzing channel-porous media interactions by developing a compressible volume-averaged channel-porous media model in OpenFCST, and validating it with respect to permeability and diffusion bridge experiments in the literature. Comparison of numerical and experimental results show that, depending on the volume-averaging methodology used, experimentally obtained transport properties do not correspond to the input parameters needed in volume-averaged models. Finally, the numerical model is used to estimate flow by-pass in serpentine and inter-digitated channels, as well as compressibility effects [8]. References: [1] M Sabharwal, JT Gostick, M Secanell Virtual liquid water intrusion in fuel cell gas diffusion media, Journal of the Electrochemical Society 165 (7) (2018) F553. [2] I. V. Zenyuk, D. Y. Parkinson, L. G. Connolly, A. Z. Weber, Gas-diffusion-layer structural properties under compression via X-ray tomography, Journal of Power Sources 328 (2016) 364–376. [3] P. Mangal, L. M. Pant, N. Carrigy, M. Dumontier, V. Zingan, S. Mitra, M. Secanell, Experimental study of mass transport in PEMFCs: Through plane permeability and molecular diffusivity in GDLs, Electrochimica Acta 167 (2015) 160–171. [4] T. G. Tranter, P. Stogornyuk, J. T. Gostick, A. D. Burns, W. F. Gale, A method for measuring relative in-plane diffusivity of thin and partially saturated porous media: An application to fuel cell gas diffusion layers, International Journal of Heat and Mass Transfer 110 (2017) 132–141. [5] J. T. Gostick, Versatile and efficient pore network extraction method using marker-based watershed segmentation, Physical Review E 96 (2) (2017) 1–15 [6] M Secanell, A Putz, P Wardlaw, V Zingan, M Bhaiya, M Moore, J Zhou, Chad Balen, Kailyn Domican, Openfcst: An open-source mathematical modelling software for polymer electrolyte fuel cells, ECS Transactions 64 (3), 655, 2014 and www.openfcst.org [7] Gostick et al. OpenPNM: A pore network modeling package. Computing in Science & Engineering. 18(4), p60-74 (2016) and http://openpnm.org/ . [8] A Jarauta, V Zingan, P Minev, M Secanell A Compressible Fluid Flow Model Coupling Channel and Porous Media Flows and Its Application to Fuel Cell Materials, Transport in Porous Media 134 (2) (2020) 351-386.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".