Numerical Analysis of Water Transport in PEM Fuel Cell Membranes Using a Phenomenological Model
Bibliographic record
Abstract
A numerical investigation on the water transport across the membrane of a proton exchange membrane fuel cell is carried out to gain insight into water management issues, which are crucial to the efficient operation of such fuel cells. The transport equation of water content based on a phenomenological model, which includes an electro-osmotic drag term and a diffusion term, is solved using the finite volume method for a 1-D configuration with the assumption of a uniform temperature distribution. Transport properties including the drag coefficient and diffusion coefficient of water in the membrane and the ionic conductivity of the membrane are expressed as functions of water content and temperature. The effects on the water flux across the membrane and on overall membrane protonic conductivity due to variations of these properties are studied. The numerical results show that water transport in the membrane is mainly determined by the relative strength of electro-osmotic drag and diffusion, which are affected by operating conditions such as current density and relative humidity at the membrane surface, and design parameters such as membrane thickness and membrane material. Computed water fluxes for different humidity boundary conditions indicate that for a thick membrane, e.g. Nafion 117, electro-osmotic drag dominates transport over a wide range of operating conditions, whereas for a thin membrane, e.g. Nafion 112, diffusion of water becomes equally important under certain conditions. Implications of the one-dimensional investigation on comprehensive CFD based modelling of proton exchange membrane fuel cell are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".