Estimating the fatigue lifetime of mechanically reinforced membranes in fuel cell applications
Bibliographic record
Abstract
This work presents an innovative projection algorithm to estimate the fatigue lifetime of mechanically reinforced membranes in proton exchange membrane fuel cells (PEMFCs), using in-situ finite element modeling and pressure-differential accelerated mechanical stress testing (ΔP-AMST). The method uses critical accumulated plastic dissipation energy to extend ΔP-AMST results to real-world durability, reducing durability testing time and cost. The constitutive model accounts for temperature, humidity, strain rate, and compressive stress from clamping and gas pressure differentials and can thus be applied to both ΔP-AMST and in-situ fuel cell conditions. The findings of the validated CAPDE-based lifetime prediction model show that reinforced membranes are more vulnerable to fatigue under fuel cell operation at high temperatures. However, minimizing dry phases in humidity cycles and optimizing membrane swelling ratios can improve fatigue lifetime. Variations in the Young's modulus of adjacent layers have minimal effect, while reducing the channel-to-land width ratio improves membrane fatigue life but may impact fuel cell performance. These insights highlight the utility of the model as well as the importance of balancing design and operation for optimal PEMFC durability and efficiency. • Novel method projects membrane fatigue lifetime using ΔP-AMST and FEM modeling. • CAPDE introduced as a threshold for membrane fatigue failure prediction. • FEM model includes effects of RH, temperature, clamping, and pressure loads. • Reduced dry phase and higher swelling ratio boost membrane fatigue lifetime. • CL and GDL properties have minimal effect on membrane fatigue performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".