Combined Pore Network and Finite Volume Modelling of Water Movement in PEM Fuel Cell Porous Transport Layers
Bibliographic record
Abstract
The effects of water transport in the porous transport layers (PTLs) of polymer electrolyte membrane (PEM) fuel cells are commonly studied using sub-scale pore network models or geometric models which generate effective properties that can be applied in computational fluid dynamics (CFD) models using a continuum approach at larger length scales. This approach is commonly uncoupled and uses a steady state approach, thereby approximating and averaging important details of water transport effects. To capture water transport in sub-scale CFD models requires extensive computer resources and is often very difficult to converge. In this work, a three-dimensional pore network model is implemented in the open-source, finite-volume CFD code, OpenFOAM. The model captures transient water transport through a network of pores using an experimentally determined pore size distribution. This model is directly coupled to a continuum model to directly capture the effect of liquid water on fuel cell performance. After a description of the method, results are compared with those from an in-house pore-network model for code verification. The method presents a novel approach which benefits from both pore-network modelling and CFD to investigate the subject of water transport in PEM fuel cells.
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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) | 0.001 | 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".