Pore Network Modeling of Compressed Fuel Cell Components with OpenPNM
Bibliographic record
Abstract
Abstract Pore network modeling is used to model water invasion and multiphase transport through compressed PEFC gas diffusion layers. Networks are created using a Delaunay tessellation of randomly placed base‐points setting the pore locations and its compliment, the Voronoi diagram, is used to define the location of fibers and resultant pore and throat geometry. The model is validated in comparison to experimental capillary pressure curves obtained on compressed and uncompressed materials. Primary drainage is simulated with an invasion percolation algorithm that sequentially invades pores and throats separately with excellent agreement to experimental data, but required a slight modification to account for the higher aspect ratio of compressed pores. Compression is simulated by scaling the through‐plane coordinates in a uniform manner representing a GDL wholly beneath the current‐collector land. The relative permeability and diffusivity show some dependence on uniform compression. In‐plane porosity variations introduced by land‐channel compression are also investigated which have a marked effect on the limiting current. Saturation at breakthrough does not appear to be dependent on compression. However, a more important parameter, namely the peak saturation, is shown to influence the fuel cell performance and is dependent on the percolation inlet conditions.
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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 | 0.001 | 0.001 |
| 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".