Simulation of a Full Fuel Cell Membrane Electrode Assembly Using Pore Network Modeling
Bibliographic record
Abstract
A pore network model has been applied to a both sides of a fuel cell membrane electrode assembly. The model includes gas transport in the gas diffusion layers and catalyst layers, proton transport in the catalyst layers and membrane, and percolation of liquid water. This paper presents an iterative algorithm to simulate a steady state isothermal cell with a 3D pore network model for constant voltage boundary condition. The proposed algorithm provides a simple method to couple the results of the anode and the cathode sides by iteratively solving the uncoupled equations of the transport processes. It was found that local water blockages at the GDL/CL interface not only affect concentration polarization, but also might change ohmic polarization of the cell. Depending on the liquid water configuration in the porous electrodes, the protons generated in the anode need to travel longer paths to reach the active sites of the cathode; consequently, the IR loss will be increased in the presence of liquid water. This finding highlights the strength of pore network models which resolve discrete water blockages in the electrodes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".