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Record W4285399112 · doi:10.1149/ma2022-01412468mtgabs

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

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

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMembraneMaterials scienceComposite materialDurabilityElectrolytePolymerForensic engineeringElectrodeChemistryEngineering

Abstract

fetched live from 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

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.192
Teacher spread0.177 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes2
Has abstractyes

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