Flow uniformity in and its effect on the performance of polymer electrolyte membrane fuel cell stacks
Bibliographic record
Abstract
In this work the effect of flow uniformity on a PEM fuel cell stack performance has been investigated to optimize the stack design. Based on the hydraulic resistance network method, a correlation for the ratio of pressure drop in flow channel, to that in stack manifold, has been derived analytically as a measure of flow uniformity among the cells in the stack. It has been shown that the amount of flow variation can be predicted via the pressure drop ratio, and is found that they are inversely proportional to each other. The results indicate that the output voltage degrades rapidly as the amount of flow variance is increased. Sufficient flow uniformity is crucial to minimize the cell‐to‐cell voltage variation. However, the space available for the manifold is limited on bipolar plate and excessive flow uniformity may result in net performance degradation either due to a reduction in the active cell area or excessive pumping power. Optimization has been carried out based on net output power which is obtained by subtracting the pumping powers for the anode and cathode streams from the stack output power. The effect of minor loss on cell‐to‐cell voltage variation as well as stack output voltage has been investigated and it may become considerable when the number of flow channels per bipolar plate is small.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".