Numerical investigation of effect of mechanical compression on the transport properties of fuel cell microporous layer using a pore-scale model
Bibliographic record
Abstract
The microporous layer (MPL) plays an important role in water and thermal management of proton exchange membrane fuel cells (PEMFCs). An in-depth investigation of the mechanical compression effect on transport properties in the MPL can help optimize cell performance. In this work, the microstructure of the MPL is numerically reconstructed and the finite element method is applied to simulate mechanical behavior. Besides, the distribution of stress-strain, porosity, and pore size in the MPL under ten different levels of mechanical compression strains are studied. Lastly, the pore-scale model is employed to investigate the effective transport properties of the MPL as a function of compression strain. The analysis reveals that as the MPL strain increases from 0% to 40%, there is a 29% decrease in porosity, a 50% reduction in average pore diameter, a 60% decrease in effective gas diffusivity, a 100% increase in tortuosity, and an 80% increase in electrical and thermal conductivity. With the escalation of mechanical compression, both the magnitude and uniformity of stress-strain-displacement concurrently rise. Mechanical compression strains below 20% exhibit a lesser impact on transport properties. Beyond this threshold, exceeding the 20% compression strain point, mechanical stress assumes a critical role in influencing MPL transport properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".