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Enregistrement W4237953042 · doi:10.1149/ma2015-02/37/1288

Stochastic Reconstruction and Transport Simulation of PEFC Catalyst Layers

2015· article· en· W4237953042 sur OpenAlexaff
L. M. Pant, Mayank Sabharwal, Sushanta K. Mitra, Marc Secanell

Notice bibliographique

RevueECS Meeting Abstracts · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensYork UniversityUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésPorous mediumPorosityMaterials scienceThermal diffusivityTortuosityTransport phenomenaComputer scienceMechanicsPhysicsComposite materialThermodynamics

Résumé

récupéré en direct d'OpenAlex

Understanding mass transport in polymer electrolyte fuel cells (PEFCs) is critical to improve reactant and water transport in catalyst layers. Numerical simulation techniques can be used to gain a detailed understanding of transport within the catalyst layer structure. The accuracy of these simulations is dependent on the accuracy of the physical domain and the mathematical models. While detailed mathematical models of mass, charge and energy transport have been presented in literature, there has been limited work in understanding the catalyst layer structure. Reconstructions of PEFC catalyst layers are therefore crucial to understand its microstructure and to obtain transport properties via simulation. Imaging techniques such as X-ray tomography (micro-CT) or scanning electron microscopy (SEM), even though informative, cannot easily be used to understand how different porous media parameters (e.g., pore sizes, connectivity) control its transport properties (e.g., effective diffusivity) as the structure is not mathematically characterized. The method of stochastic reconstruction on the other hand, uses geometry based statistical descriptors in order to create reconstructions. The statistical descriptors are related to the geometric properties of the porous media, e.g., the two point correlation function is related to the interface area and the chord length function is related to pore size. The method of stochastic reconstruction is cost and time efficient, can account for the stochastic nature of porous media, and provides a way to characterize the porous media by its statistical descriptors. Most of the fuel cell literature is composed of imaging based reconstructions [1,2]. A few catalyst layer stochastic reconstructions use either simple statistical descriptors, such as two-point correlation functions, or heuristic approach, e.g., creating the structure based on a sphere packing algorithm [3,4]. Due to the limited stochastic information and idealized building blocks, these reconstructions might not truly represent the complex microstructure of PEFC porous media, which consists of non-spherical fractal carbon particles [5]. The use of multiple correlation functions together with simulated annealing [6] could provide an improved method to reconstruct the complex PEFC porous media, and to analyze the relation between porous media structure and transport properties. In this work, a modified simulated annealing method based on one proposed by Yeong and Torquato [6] is used to reconstruct a three-dimensional and multi-phase microstructure representative of a catalyst layer (CL). A two dimensional version of the program was used to reconstruct a PEFC CL earlier [7]. The new improved method uses a multigrid hierarchical annealing technique to significantly reduce the reconstruction time and ascertains long range connectivity. The multigrid method performs reconstructions in sequential refinement stages and has been found to result in reconstruction time reduction by 2-3 orders of magnitude. The new method uses a biased pixel swapping method compared to conventional random swapping for faster and more accurate reconstruction. The biased pixel swapping has reduced the reconstruction time by almost an order of magnitude, while also improving the accuracy of the reconstructions by an order of magnitude. Multiple statistical descriptors are used in combination to generate a structure, which provides a better representation of the PEFC CL structure. An algorithm for automatically generating a computational mesh and a mathematical model of the transport process in the catalyst layer has also been developed. Mass and electron transport are simulated on the reconstructions using the open source package OpenFCST [8]. The effective transport properties of the reconstructions are close to the reference structure value, e.g., the ratio of difusibility values of the reconstructions to diffusibility values of reference was found to be in the range of 0.9-0.96. The new method allows us to reconstruct detailed high-resolution structures with high accuracy in practical amount of time, e.g., a reconstruction of size 300x300x300 can now be reconstructed in 24-30 hours instead of several weeks. The effect of different stochastic descriptors on the transport properties will also be studied in future. Overall, the current work provides a novel methodology to characterize and reconstruct PEFC porous media from statistical descriptors in a time and cost effective manner. [1] K. Lange et al. Electrochim. Acta. 2012, 85, 322-331 [2] W. Epting et al. Adv. Funct. Mater. 2012, 22, 555-560 [3] P. Mukherjee et al. J. Electrochem. Soc. 2006, 153, A840-A849 [4] K. Lange et al. J. Electrochem. Soc. 2010, 157, B1434-B1442 [5] D. Banham et al. J. Power Sources. 2011, 196, 5438–5445 [6] C. Yeong et al. Physical Review E. 1998, 57, 495-506 [7] L. Pant et al. Phys. Rev. E. 2014, 90, 023306 [8] Open Fuel Cell Simulation Toolbox (openFCST). Available at www.openfcst.org Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,214
Écart entre enseignants0,199 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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