Sensitivity Analysis of Mass Transport Properties of Gas Diffusion Layers of Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
A prodigious amount of research has been conducted to study and measure the different properties associated with the mass transfer phenomenon inside the GDL like diffusivity, permeability, porosity, thickness. However, the functional relationship of these parameters with the limiting current; the effect of the individual factor over others; and the interaction, correlation and interdependence of these factors have been subject of little work. Using the experimental data presented in the literature, this paper presents a methodology developed based on a regression model to predict the limiting current from GDL properties. Statistical techniques like factorial design and response surface methodology are used to perform the sensitivity analysis of relevant parameters. The emphasis will be on factor screening to identify efficiently the parameters with most dominant effects. The results obtained will then be used to select the most effective GDL (among those characterized) based on multi criteria decision making tools like weighting sum method (WSM) and TOPSIS with entropy weight. The final goal is to elucidate the impact of each property, and hence to identify the most important parameters that need to be studied and characterized to engineer a better GDL with enhanced water management capabilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".