3-D Numerical Simulation of Gas-Liquid Flow in a Minichannel With a Non-Uniform GDL Surface
Bibliographic record
Abstract
Gas-liquid two-phase flow in rectangular minichannels of polymer-electrolyte membrane fuel cells (PEMFCs) has a major impact on the fuel cell performance and durability. Different from traditional two-phase flow in other applications, water in the PEMFCs is introduced into the minichannel from the gas diffusion layers (GDLs) through random pores of different sizes. Meanwhile, the four channel surfaces may have different wettabilities due to the different materials used. Thus, the microstructure of GDLs and the surface wettability should be considered in investigating the two-phase flow in PEMFC channels. One challenge in simulating PEMFCs is that, full consideration of detailed microstructure of GDL needs extremely large computational time. In this work, we simplified the microstructure of GDL to a number of representative pores on the 2D GDL surface. A 3-D minichannel with 1.0 mm × 1.0 mm square cross section and 100 mm long was used in the simulation. Operating conditions and material properties were selected according to realistic fuel cell operating conditions. Volume of fluid (VOF) method was employed to explicitly track the droplet surfaces emerging from the non-uniform GDLs. Simulation results show that, as the flow develops along the channel, the flow pattern evolves from corner flow on the bottom and side wall to corner flow on the top wall, annular flow and slug flow. The effects of liquid injection rates were studied, and it is found that the high liquid flow rate would accelerate the flow pattern development. The effect of wall surface material wettability was also studied by changing the hydrophobicity of GDL surface and side walls, separately. Simulation results show that the material wettability has a strong impact on the two-phase flow pattern, with a more hydrophilic side walls and/or a more hydrophobic GDL surface being more beneficial for expelling water out of the channel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".