Transport phenomena in fuel cells: from microscale to macroscale
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Bibliographic record
Abstract
AbstractFuel 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.Keywords: CFDmembrane transportcatalystporous mediatwo-phase flowmulti-scale model AcknowledgementsThe authors would like to acknowledge the contributions from a number of former and current graduate students, research associates, colleagues and collaborators; from UVic: Aimy Bazylak, Torsten Berning, Brian Carnes, Jeff Fimrite, Shawn Litster, Bojan Markicevic, Jon Pharoah, Marc Secanell, Henning Struchtrup and Xun Zhu; from Angstrom Power: Ged McLean; and from Ballard Power Systems: John Kenna, Sanjiv Kumar and Ryan Mackie.Financial support from the following is also gratefully acknowledged: the MITACS Network of Centres of Excellence; the Natural Sciences and Engineering Research Council (NSERC) of Canada; the Canada Research Chairs program; Angstrom Power Inc.; and Ballard Power Systems.Figures 5, 7 and 8 are reproduced with permission from Elsevier.
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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 it