A Study on Liquid Water Removal From Gas Diffusion Layer by Pressure Gradient
Bibliographic record
Abstract
Water removal from the gas diffusion layer (GDL) is crucial for the stable and efficient operation of proton exchange membrane (PEM) fuel cells. The static pressure gradient induced by reactant flow in the flow channel is one of the main driving potential for the liquid water to be drawn from the GDL. In the PEM fuel cells with interdigitated and serpentine flow channels, considerable amount of reactant flows through the GDL due to the pressure gradient between adjacent flow channels. Such pressure gradient and resultant cross flow may also play an important role for the water removal from GDL during operation. In this work, liquid water transport in the GDL is studied numerically to investigate the effect of pressure gradient and the surface hydrophobicity on the water removal from the GDL. The fibrous porous structure of carbon paper is modeled by distributing impermeable cylinders in random directions. Unsteady two phase simulation has been performed utilizing a commercial software FLUENT based on the volume of fluid (VOF) scheme to determine the phase boundary. The permeability of the numerical medium is compared with the experimental measurements in literature resulting in a good agreement. It is shown that the surface hydrophobicity of the fiber is a dominant parameter to initiate the water transport in the GDL for the pressure gradient in typical operating conditions. Cross flow occurring in the serpentine flow channels may be effective to get rid of the liquid water in the gas diffusion layer. Present work may provide useful data to design and optimize the important properties of gas diffusion layer such as permeability and surface hydrophobicity.
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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 | 0.001 |
| 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".