Transport phenomena in fuel cells: from microscale to macroscale
Bibliographic record
Abstract
Fuel cells have emerged as one of the most promising energy conversion technologies to help mitigate pollution and greenhouse gas emissions. This relatively young and rapidly evolving technology offers scope for innovation in both computational modelling and design. The operation of a fuel cell depends on the optimised regulation of the flow of reactant gases, product water, heat and charged species in conjunction with reaction kinetics. These strongly coupled processes take place over a broad range of length and time scales, and in diverse structures and materials. This gives rise to a fascinating and challenging array of transport phenomena problems.This paper provides an overview of these transport phenomena in polymer electrolyte membrane fuel cells, and a critical discussion of computational strategies to resolve processes in key components: polymer electrolyte membrane, porous gas diffusion electrodes and microchannels. The integration of the various transport phenomena and components into a CFD framework is illustrated for single fuel cells and for manifolding and gas distribution in a stack. Multi-scale strategies and the coupling of CFD based models to multi-variable optimisation methods are also discussed and illustrated for catalyst layers. The paper closes with a perspective on some of the pacing items toward achieving truly functional computational design tools for fuel cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".