Condensation Based Pore Network Modelling of Water Transport in Hydrophobic PEM Fuel Cell GDLs
Bibliographic record
Abstract
In this work, a novel pore network model is employed to simulate water transport originating from condensation in the polymer electrolyte membrane (PEM) fuel cell gas diffusion layer (GDL). Liquid water transport follows the rules of invasion percolation with trapping, where two mobile phases are considered. Flow conditions are based on dynamic pressure changes in the network. The GDL is assumed to be a hydrophobic pore network, where capillary forces dominate over gravitational and viscous forces. The model follows a condensation based algorithm that begins with a single nucleation site from where liquid water spreads with continuing condensation. To account for a humidity gradient within the GDL, water flow is assumed to originate from condensation occurring in pores facing the cathode catalyst layer. Modelling parameters and their effect on the saturation profile are discussed. Little impact was found on the saturation profile when trapping logic was made more sophisticated, recognizing conditions leading to air trapping in a single throat. It is shown that saturation profiles for slow flow (i.e. slow condensation rates) can be predicted with reasonable accuracy from a known throat topology alone. However, as condensation rates are increased, raising network viscous forces to levels comparable to network capillary forces, the flow patterns begin to depend on a number of variables such as pore sizes and pore filling rates. At such condensation rates, flow patterns show high sensitivity to variance in condensation rates and become much less predictable from simple geometries.
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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 | 0.000 |
| 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".