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Enregistrement W2599779789 · doi:10.1149/ma2017-01/31/1482

Understanding the Role of the Micro-Porous Layer on Fuel Cell Performance Using a Non-Isothermal, Two-Phase Model

2017· article· en· W2599779789 sur OpenAlexaff
Jie Zhou, Andreas Pütz, Marc Secanell

Notice bibliographique

RevueECS Meeting Abstracts · 2017
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensAutomotive Fuel Cell Cooperation (Canada)University of Alberta
Organismes subventionnairesnon disponible
Mots-clésProton exchange membrane fuel cellWater transportMaterials scienceElectrolyteChemical engineeringMembrane electrode assemblyElectrodeIsothermal processPorosityWater vaporEvaporationComposite materialChemistryFuel cellsWater flowThermodynamicsEnvironmental engineeringEnvironmental science

Résumé

récupéré en direct d'OpenAlex

Water management is a critical factor in improving fuel cell performance at high current densities [1]. Under dry conditions, keeping the ionomer phase in polymer electrolyte membrane (PEM) and catalyst layers (CLs) sufficiently hydrated is critical to maintaining high protonic conductivity and reducing the ohimc losses. When the cell is operating at high relative humidity, removing the excessive water generated in the electrodes is of importance in order to avoid liquid water accumulation and achieve high performance. An efficient membrane electrode assembly (MEA) design therefore requires an extensive understanding of water management in fuel cells. The use of a micro-porous layer (MPL) in fuel cells is known to improve water and heat management resulting in a better performance. Experimental evidence has shown that remarkable fuel cell performance improvements are still possible by modifying the MPL composition [2] and micro-structure, for instance, by milling holes [3]. In situ heat and water flux measurements conducted by Thomas et al. [4] showed that by inserting an MPL between CL and gas diffusion layer (GDL), the temperature in the electrodes increased by at least 1° C at high current density. The warmer electrode therefore, results in a higher evaporation which also facilitates the diffusive transport of water vapour by creating a higher concentration gradient. Based on experimental observations from the ex situ diffusive vapour flux and liquid permeation flux measurements as well as in situ electrochemical performance with varying MPLs, Owejan et al. [5] hypothesized the role of MPL is to prevent the condensed water in the GDL from forming a liquid film at the GDL/CL interface and creating an in-plane diffusive path for reactant. In their study, the thermal conductivity in porous layers demonstrated a significant impact on improving the performance by creating a higher temperature driven diffusive flux. In order to understand the effects of MPL on capillary-driven flow and phase change induced flow, a multi-dimensional, non-isothermal, two-phase numerical model is developed in OpenFCST [6]. The porous media transport properties for two-phase flow are estimated using a micro-scale mathematical pore size distribution model [1] which is capable of accounting for the layer mixed wettabilities and micro-structure. Experimental validation of two-phase flow models is rarely performed even though it is of importance. In this study, the electrochemical performance of an MEA with a SGL 24BA and SGL 24BC is measured in our laboratory at varying operating conditions and also predicted using the numerical two-phase model. Membrane water transport, water fluxes in liquid and vapour form at cathode boundary, and the phase change induced flow are analyzed for studies with and without an MPL. The performance analysis under hot/dry condition indicates that adding an MPL in the dry condition results in an excessive protonic transport loss in the membrane, especially at high current density. Under hot/wet condition, the additional heat preserved on the electrodes by adding the MPL leads to a substantial reduction in water accumulation. Simulation results at the cold/wet condition highlight that adding an MPL not only leads to a decrease in water accumulation in the electrode but also creates an in-plane diffusion pathway for gas transport in the cathode. The increase in temperature in the electrode also results in a decrease in relative humidity, especially at the anode. This leads to a higher membrane water content gradient between the fully saturated cathode and the anode which results in a higher back diffusion. A parametric study of MPL thermal conductivity suggests that the excessive water in the cathode can be removed as water vapour by decreasing the MPL thermal conductivity under fully humidified conditions. However, an extremely low MPL thermal conductivity can lead to a significant deterioration of performance even at high relative humidity due to the membrane dehydration. The paramteric study highlights the optimal MPL conducivity for our cell. An optimal MPL design requires a comprehensive water balance between membrane hydration and sufficient electrode water evaporation. References: [1] A. Z. Weber et al.,J. Electrochem. Soc., 2004, 151, A1715–A1727. [2] P. G. Stampino et al., Catalysis Today, 2009, 147, S30–S35. [3] R. Alink et al., Journal of Power Sources, 2013, 233, 358–368. [4] A. Thomas et al., International Journal of Hydrogen Energy, 2014, 39, 2649–2658. [5] J. P. Owejan et al., J. Electrochem. Soc., 2010, 157, B1456–B1464. [6] M. Secanell et al., ECS Transactions, 2014, 64, 655–680. 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)0,000
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,008

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,002
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,040
Tête enseignante GPT0,249
Écart entre enseignants0,209 · 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

Citations3
Publié2017
Routes d'admission1
Résumé présentoui

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