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Enregistrement W4285399112 · doi:10.1149/ma2022-01412468mtgabs

Modeling and Simulation of the Mechanical Properties of Reinforced Fuel Cell Membranes

2022· article· en· W4285399112 sur OpenAlexaffabout
Mohsen Mazrouei Sebdani, Erik Kjeang, Heather Baroody

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueFuel Cells and Related Materials
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésMembraneMaterials scienceComposite materialDurabilityElectrolytePolymerForensic engineeringElectrodeChemistryEngineering

Résumé

récupéré en direct d'OpenAlex

Polymer electrolyte fuel cells (PEFCs) have gained popularity over internal combustion engines due to their zero CO2 emission, low working temperature, and high efficiency. However, transportation markets generally require high durability and reliability, which remains a significant challenge for PEFC developers. For instance, the thin, ion-conducting membranes used in PEFCs must be able to withstand both chemical and mechanical stresses during dynamic operation. Reinforced membranes even still there are durability challenges that remain even though it is more robust. These membranes have limited mechanical strength, and micro-cracks may allow hydrogen permeation and cause ultimate fuel cell failure. Dynamic stresses caused by temperature and humidity cycles are causing micro-cracks to form and propagate. Because the membrane is constrained by the other parts of the membrane-electrode assembly (MEA), any change in temperature and humidity can create swelling strain and thermal strain in the membrane, resulting in residual stress [1]. The first step in understanding this damaging phenomenon is to simulate the membrane's visco-elastic and visco-plastic behavior while taking temperature, humidity, and strain rate into account. The objective of the present work is to develop such a constitutive mechanical model for mechanically reinforced membranes, which are commonly used in modern PEFCs. Khorasany et al. [2] developed a fatigue lifetime model based on the elastic-plastic constitutive method and Smith-Watson-Topper (SWT) fatigue equilibrium for conventional, non-reinforced fuel cell membranes. In the present work, for strains below the yield point, the linear elasticity by Hooke's law has been considered and the visco-elastic and visco-plastic behaviors of the membrane have been neglected, but for every temperature and humidity, the related Young’s modulus and Poisson’s ratio have been obtained from prior experiments, and for plastic yield response, the Von Mises yield criterion has been selected. Khattra et al. [3] used the G’Sell-Jonas theory for the constitutive model of a non-reinforced membrane, which can only be employed for tensile stresses, while in this study, a generalized G’Sell-Jonas approach; equation 1, has been developed for the reinforced membrane that can also be used for compressive stresses that are common during in-situ fuel cell conditions. This phenomenological theory accounts for the effects of temperature, humidity, and strain rate on membrane mechanical properties and, when combined with thermal and swelling strains, provides a comprehensive model of membrane behavior for both ex-situ and in-situ conditions. For the reinforced membrane, tensile stress-strain tests in two principal in-plane directions have been done that showed its isotropic behavior and, therefore, provide input data for the parameters of the proposed generalized G’Sell-Jonas model. σ(ε,T,H)generalized G'Sell-Jonas=step(ε)K(T,H)(1-e-w(H)abs(ε))eh(H)ε^2 (1) step(ε)=+1 ε≥0, -1 ε<0 In the above equations, K, w, and h are empirical parameters dependent on temperature (T) and humidity (H), and ε is strain. Elastic modeling based on Von-Mises has been studied for constitutive models with stresses below 1 MPa, while isotropic work hardening based on the generalized G'Sell-Jonas theory has been considered for higher stresses in order to follow plastic behavior. In elastic mode, the membrane's Young’s modulus is calculated using a function of humidity and temperature derived from tensile tests. Tensile tests under four environmental conditions and two strain rates in two principal in-plane directions, as well as fatigue tests for extracting S-N curves for this membrane, have been obtained using dynamic mechanical analysis (DMA). Based on Figure 1, the mechanical strength of this isotropic reinforced membrane decreases with increasing temperature and humidity, but the effect of temperature is greater. Because of the viscoelastic nature of the membrane, by increasing the strain rate, the membrane stiffness has been increased too. In the fatigue tests done in DMA, the force track is 150% (= ×100%), R-value = , and frequency is 10 Hz. FEM modeling based on G’Sell-Jonas’s theory shows a strong agreement with the experiments that has been illustrated in Figure 1. The fatigue lifetime distribution based on generalized G’Sell-Jonas’s theory and SWT parameters extracted from Khorasany’s paper [2] have been simulated that reveals when the maximum stress in fatigue cycles is more than 17-18 MPa, a huge drop in fatigue lifetime is observed. Acknowledgments Funding for this research has been provided by AVL Fuel Cell Canada and Mitacs. References Alavijeh, A.S., et al., Effect of hygral swelling and shrinkage on mechanical durability of fuel cell membranes. Journal of Power Sources, 2019. 427: p. 207-214. Khorasany, R.M., et al., Mechanical degradation of fuel cell membranes under fatigue fracture tests. Journal of Power Sources, 2015. 274: p. 1208-1216. Khattra, N.S., et al., Residual fatigue life modeling of fuel cell membranes. Journal of Power Sources, 2020. 477: p. 228714. 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: aucune
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)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0020,001
Intégrité de la recherche0,0030,001
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 Opus0,014
Tête enseignante GPT0,192
Écart entre enseignants0,177 · 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

Citations1
Publié2022
Routes d'admission2
Résumé présentoui

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