Numerical Predictions of Transport Phenomena in a Proton Exchange Membrane Fuel Cell
Bibliographic record
Abstract
Transport phenomena play an important role in the performance of the proton exchange membrane fuel cell. Water generated by electrochemical reactions and transported by osmotic drag and back diffusion can cause saturation or flooding, preventing oxygen from reaching catalysis sites. Dehydration may also occur, resulting in poor proton conductivity. Balancing water content within the membrane involves judicious water and heat management strategies. In this paper, detailed mathematical models for the prediction of all significant aspects of physicochemical hydrodynamics for a proton exchange membrane fuel cell are employed. Fully coupled heat and mass transfer and electrochemistry are considered, and the dependence of water transport on these factors is taken into account. Two distinct approaches were considered: a fully three-dimensional approach and a hybrid scheme, whereby the electrochemistry and electric fields are treated as locally one dimensional in the membrane assembly. Comparisons between the two approaches are presented and discussed. The numerical results suggest a dependence of the rate-of-water removal on temperature, current density, and inlet humidification levels, and also that the oxygen concentration in the air channels significantly affects current density distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".