Numerical Modeling of PEM Fuel Cells Under Partially Hydrated Membrane Conditions
Bibliographic record
Abstract
In proton-exchange membrane fuel cells it is particularly important to maintain appropriate water content and temperature in the electrolyte membrane. The water balance depends on the coupling between diffusion of water, pressure variation, and the electro-osmotic drag in the membrane. In this paper we apply conservation laws for water and current, in conjunction with an empirical relationship between electro-osmotic drag and water content, to obtain a transport equation for water molar concentration and to derive a new equation for the electric potential that strictly accounts for variable water content and is more accurate than the conventionally used Laplace’s equation. The model is coupled with a computational fluid dynamics model that includes the porous gas diffusion electrodes and the reactant flow channels. The resulting coupled model accounts for multi-species diffusion (Stefan-Maxwell equation); first-order reaction kinetics (Butler-Volmer equation); proton transport (Nernst-Planck equation); and water transport in the membrane (Schlo¨gl equation). Numerical simulations for a two-dimensional cell are performed over nominal current densities ranging from i=0.4 to i=1.2 A/cm2. The relationship between humidification and the membrane potential loss is investigated, and the impact and importance of two-dimensionality, temperature, and pressure nonuniformities are analyzed and 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 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".