Cumulative Damage Modeling of Fuel Cell Membranes
Bibliographic record
Abstract
The strongly varying humid environment under which fuel cell membranes operate introduces detrimental mechanical stresses that inflict damage to the membrane1. The damage incurred by the membrane during each humidity cycle when accumulated over time may lead the membrane to fatigue failure. The residual fatigue life of the membrane is determined here by using a cumulative-damage model2. A time-temperature-humidity dependent constitutive model and a multi-physics fuel cell finite element model are first developed to characterize the material properties and estimate the coupled mechano-hygral-thermal stress field in the membrane. Using this multi-level modeling approach, and with the aid of experimentally obtained fatigue data, fail-safe regions (see Figure) are identified for the case of accelerated stress test loading. For general loads simulating fuel cell operation, the distribution of stress-reversals is calculated for the load history using rain-flow counting algorithm. The regions of membrane that are more susceptible to fatigue damage are identified. It is also found that high amplitude stress cycles with low occurrence percentage are more detrimental than high occurrence low-amplitude cycles. This work was supported by Natural Sciences and Engineering Research Council of Canada, Simon Fraser University Community Trust Endowment Fund, Canada Research Chairs, Mitacs through the Mitacs Accelerate program, and Ballard Power Systems. 1 Borup, R., et al., Scientific aspects of polymer electrolyte fuel cell durability and degradation. Chemical Reviews, 2007. 107(10): p. 3904-3951. 2 Fatemi, A. and L. Yang, Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials. International Journal of Fatigue, 1998. 20(1): p. 9-34. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".