Virtual Liquid Water Intrusion in Fuel Cell Gas Diffusion Media
Bibliographic record
Abstract
A cluster based full morphology (CFM) model is developed to predict liquid water intrusion in GDLs. The CFM model is used to simulate water intrusion into a dry GDL microstructure obtained from μ-CT. Numerical water saturation distributions are compared to experimental μ-CT reconstructions of the same GDL sample at varying saturation levels. A quantitative validation of the CFM model results is then provided by studying the number of voxels in the image that contain water in both the CFM model results and the μ-CT reconstructions. Results reveal that CFM simulations showed 56–95.7% agreement in the liquid water voxels when compared to the μ-CT simulations for saturations in the range of 29–92.3%. Gas transport simulations are then performed on μ-CT and CFM partially saturated GDLs in order to study the validity of the CFM model images to estimate transport properties of partially saturated GDLs. A maximum error of 20% was observed between the predicted effective diffusivities obtained from CFM simulations and those obtained directly from simulations on the μ-CT data for saturations below 40%. Effective diffusivity predictions from the CFM simulations agree well with in-plane effective diffusivities in literature while the through-plane effective diffusivities were underpredicted by a factor of 2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".