Liquid Water Effects on the Performance of a PEMFC With Serpentine-Parallel Channels
Bibliographic record
Abstract
Water management is one of the most important issues for the performance of Proton Exchange Membrane Fuel Cell (PEMFC). Understanding of water liquid behaviors in the gas-liquid flow of PEMFC is necessary for the optimization of PEMFC. In this study, a three-dimensional and unsteady PEMFC model with serpentine-parallel channels has been incorporated to investigate not only the fluid flow, heat transfer, species transport, electrochemical reaction, and current density distribution but also the behaviors of water liquid in the gas–liquid flow of the channels and porous media. The results show that tracking the interface of water liquid in a reacting gas-liquid flow in PEMFC can be fulfilled by using Volume-of-Fluid (VOF) algorithm combined with solving the conservation equations of continuity, momentum, energy, species transport and electrochemistry. Consequently, the behaviors of liquid water were fully understood by presenting the motion and deformation of water droplets inside the channels and the penetration of liquid through the porous media at different time instants. Additionally, the presence of liquid water in the channels significantly influences the flow fields. Due to the blockage of water liquid, the gas flow became unevenly distributed, the high pressure regions took place around the locations where water liquid appears, and the reactant transport in the channels and porous media was hindered by water liquid occupation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".