Numerical Reconstruction of Proton Exchange Membrane Fuel Cell Gas Diffusion Layers
Bibliographic record
Abstract
Flooding and dehydration reduce stability and power performance in Proton Exchange Membrane Fuel Cells (PEMFCs). The Gas Diffusion Layer (GDL) plays a crucial role in facilitating reactant gas transport and removing product water from the electrode. To suit various PEMFCs, GDLs with different shapes have been commercialized. The impact of the GDL structure on the surface-tension-driven water transport behavior remains poorly understood. However, this is one important aspect that can be controlled by proper design. In this study, the GDL performance is investigated by comparing curved and straight carbon fibers within the region. Specifically, an image-processing method extracts porosity, domain size, and fiber diameter from an experimental image-based GDL reconstruction. These parameters are utilized by in-house developed computer codes to stochastically reconstruct curved and straight carbon fiber GDLs, respectively. The real and reconstructed GDLs are compared in terms of pore size distribution, tortuosity, and permeability. Liquid transport in these GDLs and corresponding gas channels is simulated using a volume of fluid method in OpenFOAM 7.0. Figure 1(a) presents the T-shaped simulation domain and top view of three GDLs. Figure 1(b) displays the Cumulative Density Function (CDF) of the pore size distribution for the three GDLs, revealing that the main difference between the three GDLs lies in the pore diameter range of 10-30 µm. Upon completion of the research program, we aim to identify the influence of fiber shape on the GDL transport properties as well as the water behavior inside them. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".