Numerical Investigation of Flowfield in PEM Fuel Cell Stack Headers
Bibliographic record
Abstract
This study addresses the factor often overlooked in analysis of fuel cell stack performance, namely the influence of the disturbances in the flowfields in the stack inflow and outflow headers. The flowfield in the header, formed by a superposition of numerous secondary in/outflows, has a complex and fundamentally unsteady nature, which has been shown by previous studies to result in non-uniform flow distribution of flow parameters along the header length. These non-uniformities can have significant effect on the components flow rates through the individual fuel cells in the stack, resulting in a differences in operating condition between individual cells, potentially compromising overall stack performance. Present work uses numerical simulation approach to model flowfield in the inflow and outflow headers. The objective of the present work is to investigate the effects of flow disturbances in the headers on stack performance. Flow rate differences between individual cells and the extent of transient variation in the flow rates through individual cells due to disturbances in the headers are investigated. Both inflow and outflow headers are modeled as a complete system, simulating the entire feedback loop between them, allowing direct modeling of transient variations of flow rate through the individual cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".