Modeling Gas Diffusion Layers in Polymer Electrolyte Fuel Cells Using a Continuum-Based Pore-Network Formulation
Bibliographic record
Abstract
Multiscale modeling of porous media in polymer electrolyte fuel cells is of paramount importance to improve predictions and assist the design of new materials. In this work, a composite-continuum-network formulation is presented to model species diffusion and convection in gas diffusion layers (GDLs). The model can be incorporated into CFD codes with moderate computational cost. The macroscopic model is based on a structured mesh composed of parallelepiped control volumes (CVs) and differential connectors (with negligible volume). The CV mesh embeds an internal structured pore network, which is used to determine analytically local anisotropic effective transport properties (effective diffusivity and permeability). The global structural parameters and effective transport properties predicted by the model are in good agreement with previous experimental data. Moreover, the results show that heterogeneities in the GDL can have significant influence on the fluxes from/to the catalyst layer, thus affecting local degradation rates.
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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".