A Factorial Study to Investigate the Purging Effect on the Performance of a Dead‐End Anode PEM Fuel Cell Stack
Bibliographic record
Abstract
Abstract This paper presents an experimental investigation of the effect of hydrogen purging time period and duration on the performance of a proton exchange membrane (PEM) fuel cell stack with a dead‐end anode. The objective is to develop a better understanding of the interactions between the purging parameters and the cathode air‐stoichiometry. It employs a full factorial approach for three factors (purging period, purging duration and air‐stoichiometry) with two levels and three replications. The study is performed on a 300 cm 2 , 24‐cell PEM fuel cell stack with the rated power of 1.5 kW. The stack was operated with water cooling, fully humidified air and dry hydrogen at the ambient pressure. The results showed that the stack performance is significantly influenced by the interactions of the purging parameters and the cathode air‐stoichiometry. The least square model was utilized to determine the optimum values of these parameters with regard to the stack performance and hydrogen utilization. For the present stack, the optimum values of parameters were: purging period of 3 min, purging duration of 4 s and the cathode air‐stoichiometry of 200%.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".