MATHEMATICAL MODELING OF PEM FUEL CELL CATHODES: COMPARISON OF FIRST-ORDER AND HALF-ORDER REACTION KINETICS
Bibliographic record
Abstract
Mathematical modeling helps researchers to understand the transport and kinetic phenomena within fuel cells and their effects on fuel cell performance that may not be evident from experimental work.In this thesis, a 2-D steady-state cathode model of a proton-exchangemembrane fuel cell (PEMFC) is developed.The kinetics of the cathode half-reaction were investigated, specifically the reaction order with respect to oxygen concentration.It is unknown whether this reaction order is one or one half.First-and half-order reaction models were simulated and their influence on the predicted fuel cell performance was examined.At low overpotentials near 0.3 V, the half-order model predicted smaller current densities (approximately half that of the first-order model).At higher overpotentials above 0.5 V, the predicted current density of the half-order model is slightly higher than that of the first-order model.The effect of oxygen concentration at the channel/porous transport layer boundary was also simulated and it was shown the predicted current density of the first-order model experienced a larger decrease (~10-15% difference at low overpotentials) than the half-order model.Several other phenomena in the cathode model were also examined.The kinetic parameters (exchange current density and cathode transfer coefficient) were adjusted to assume a single Tafel slope, rather than a double Tafel slope, resulting in a significant improvement in the predicted fuel cell performance.Anisotropic electronic conductivities and mass diffusivities were added to cathode model so that the anisotropic structure of the porous transport layer was taken into account.As expected, the simulations showed improved performance at low current densities due to a higher electronic conductivity in the in-plane direction and decreased performance at high current densities due to smaller diffusivities.Additionally, the concentration overpotential was accounted for in the model; however it had little influence on the simulation results.
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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".