Influence of gravity on water management and mass transport losses in polymer electrolyte membrane fuel cells
Bibliographic record
Abstract
Traditionally, gravity has been assumed to have a negligible impact on the multiphase transport behaviour in polymer electrolyte membrane (PEM) fuel cells; however, in this study, we reveal how the impacts of gravity and flow field orientation should not be ignored. Gravity-assisted reactant flow provides up to a 21.2 % higher peak power density compared to gravity-opposed reactant flow, owing to enhanced water removal (which we observed via operando synchrotron radiography). We are the first to combine a distribution of relaxation times (DRT) analysis with operando imaging, and for the first time, we separately correlate the presence of liquid water in the channels and GDLs to distinct mass transport loss contributions via this approach. Liquid water accumulation in the cathode GDL is most typically the focus of water management in the PEM fuel cell; however, in this work, we observed significant water accumulation in the anode GDL and channels at gravity-opposed orientations. Specifically, we observed large droplets and slugs in the anode channels which led to significantly higher anode GDL water saturation (≥ 0.23) compared to gravity-assisted angles (≤ 0.11). The force of gravity overpowers the weaker inertial force of reactant hydrogen flow, thereby hindering the removal of water droplets in the anode flow fields, which results in poor water management, reactant starvation and ultimately cell failure.
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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) | 0.000 | 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 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".