Physical Modeling of Fuel Cells and their Components
Notice bibliographique
Résumé
Abstract This chapter presents an overview of the status of physical modeling of polymer‐electrolyte fuel cells (PEFCs), the understanding gained from modeling and its impact on optimization of the operation regime and new cell design. It begins with the physical theory of proton transport in polymer‐electrolyte membranes (PEMs). This comprises microscopic aspects of the elementary act of proton transfer in aqueous environments, their realization in a single water‐filled pore and the statistical geometry of the pore network. Based on these fundamental properties, electroosmosis and different models of water backflow under fuel cell operation conditions are discussed. The resulting water‐content profiles and current–voltage performance are compared with experimental data. The diffusion model of water backflow perceives the membrane as a solution of water in a polymer host, while the hydraulic permeation model rests on the idea of a swelling porous structure, inside of which the proton and water transport take place. An important result is the critical current density, at which water content near the anode drops below the percolation threshold for water network proton conductance. Next, theories of performance and structure of composite catalyst layers are presented, mainly focusing on the cathode as an eminent example. They account for the transport of feed gas, protons and electrons, as well as for the reaction at the membrane/catalyst interface, and result in a “phase diagram”, which suggests an optimum thickness of the layer, subject to the basic parameters and the target current density. The relation between the structure and performance, rationalized using the concepts of the percolation theory, paves the way for an optimum composition of the layer. A similar theory of the complex impedance connects the composite structure and ac response. This gives a tool for determining the catalyst layer parameters. With this background we move toward 3D effects in the cell. The approach rests on a quasi‐three‐dimensional (Q3D) model of the stack element. The fuel cell stack is a two‐scale system. The small and large scales are determined, respectively, by membrane‐electrode assembly (MEA) thickness and by the length of the feed channel. The fully 3D model of the stack element is split into a 2D model of a cell cross section (internal model) and a 1D model of the feed flow along the channel (channel model). The two models are coupled via the local current density along the channel and the overall solution is obtained by iterations. The model is designed to investigate the interplay of small‐ and large‐scale processes in PEFC/DMFC. It results in distributions of gas concentrations, proton and electron currents, and reaction rates in a cell cross section, perpendicular to a long meander‐like channel, that is, it gives a functional “map of a cell”. Model equations and numerical procedures are discussed. The results of simulations are shown for the stack modules of the gas‐feed DMFC and hydrogen–oxygen PEFC. A simplified theory of “along‐the‐channel” feed gas consumption, important for understanding the cell starvation effect is surveyed and compared with simulations and experiments. The message of the model results is summarized and discussed in the sequence of the rising potential for improving PEFC/DMFC design and operation.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».