Local Voltage Degradations (Drying and Flooding) Analysis Through 3D Stack Thermal Modeling
Bibliographic record
Abstract
Temperature is a key parameter of fuel cell efficiency. In air cooled fuel cell stacks, large temperature disparities are observed. This temperature distribution has a significant influence on cell behavior in the stack, resulting in voltage disparities. The aim of this study, thus, is to correlate the temperature distribution in the stack to local voltage degradations, such as membrane drying and electrodes flooding. Indeed, the temperature has a strong impact on the water distribution in the cells because the saturation pressure is thermo-dependent. As a result, the hottest cells are prone to drying, whereas the coolest cells tend to be flooded, depending on the operating conditions. Measurements show that while drying, cell voltages decrease slowly and continuously until complete shutdown of the cells, whereas flooding results in quick voltage drops. Under drying conditions, voltage can be improved by increasing the inlet gas humidity or decrease in the stoichiometric ratio. In the case of flooding cells, purging the stack or reducing the inlet gas humidity is necessary to avoid complete shutdown of the cells. Consequently, small cell temperature variations through the stack can be responsible for large voltage variations from one cell to another. The cooling device must thus be optimized to reduce stack temperature nonuniformity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".