Condensation in PEM Fuel Cell Gas Diffusion Layers: A Pore Network Modeling Approach
Bibliographic record
Abstract
A two-dimensional dynamic pore network model is developed and employed to simulate the through-plane transport of liquid water originating from condensation in hydrophobic gas diffusion layers (GDLs) for polymer electrolyte membrane (PEM) fuel cells. The model tracks viscous and capillary forces over a range of specified condensation rates and nucleation positions. A simplified mass transport assumption of a uniform water vapor flux between the cathode catalyst layer and the liquid water cluster allows for a computationally inexpensive model. Stochastically generated steady-state saturation profiles are compared to investigate the effects of nucleation position, channel rib presence, coalescence assumptions, and condensation rates on liquid water distribution. Results indicate that GDL saturation conditions become increasingly more desirable as nucleation sites are placed further away from the catalyst layer, and saturation profiles are significantly higher when nucleation is adjacent to a hydrophobic rib compared to the gas channel or a hydrophilic rib. Meanwhile, the trapping assumption that affects liquid water coalescence in small throats has little impact on saturation patterns but has a large impact on the system’s viscous forces. Finally, the model can predict the limiting water cluster growth rates of capillary dominated growth for a given pore network.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".