Gas diffusion layer characterization and microstructural modeling in polymer electrolyte fuel cells
Notice bibliographique
Résumé
Polymer electrolyte fuel cells (PEFCs), as promising clean energy power sources, are potential substitutes not only for stationary power generation but also for mobile applications specifically in transportation due to their high power density and performance as well as lack of pollutants. PEFC vehicles are at the dawn of commercialization, but still, cost, performance, and durability of current PEFCs need to be further improved to facilitate vast market integration especially under high current density conditions. Pursuant to this goal, comprehensive multidisciplinary understanding of multiphase transport of mass, heat, and electricity in the PEFC constituents including the gas diffusion layer (GDL), as the centerpiece of this thesis, will help to make progress towards material optimization and subsequently fuel cell performance improvements. The GDL transport capability is determined by its effective transport properties which are strongly dependent on its morphological, microstructural, and physical characteristics. Therefore, accurate knowledge regarding the correlation between the GDL microstructure and its transport properties is essential for improving the performance and durability of PEFCs as well as for material optimization, fuel cell design, and prototyping in the area of fuel cell development and manufacturing. In this context, this thesis aims to develop a fast and cost-effective design tool for GDL microstructural modeling and transport properties simulation. Given the limitations of experimental, analytical, and tomographic techniques, stochastic microstructural model development to retrieve the heterogeneous GDL microstructure is a more reliable and flexible tool for GDL material design and prototyping assignments to reduce cost and time of the design cycle. Inspired by the randomness of the GDL porous media structure and its fabrication process, the GDL microstructure is virtually reconstructed as a collection of stochastic processes to provide a robust representation of the structure. The technique of stochastic microstructural reconstruction relies on statistical correlation functions which describe the probabilities of the porous media constituents’ distribution and aim to encompass all the details of the porous media. The obtained 3D digitized realizations of the stochastic model are then used as a domain for numerical computation of transport properties. In this thesis, a unique stochastic GDL microstructural modeling framework inspired by manufacturing information and characterization data is developed in which all GDL substrate and MPL components are resolved, and thoroughly validated with literature and measured data for a variety of MPL-coated GDLs. The effects of PTFE loading and liquid water saturation on the GDL substrate anisotropic transport properties for both gas and liquid phases are found to be highly coupled and are therefore simulated and analyzed jointly. Furthermore, a parametric study is conducted to investigate the effect of MPL pore morphology composition on the MPL and MPL-coated GDL transport properties. The validated stochastic design tool can be used as a fast and accurate framework for reconstructing GDL porous materials and understanding the correlation between the GDL morphology and transport properties. This paves the way for development of improved GDL materials with desired transport properties in modern PEFCs.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».