Bibliographic record
Abstract
One of the primary challenges in fuel cell stack models, is the lack of numerical methods and computational resources available to handle a full-scale stack geometry (which can often have ~10-100 single cells stacked in series) to at least the same grid resolution and detailed physics as could be obtained in an equivalently detailed single cell model. To help resolve this challenge, a 3D modelling approach is proposed, and applied to a 5-cell direct methanol fuel cell (DMFC) short-stack. In this approach, the flow fields, backing layers and membranes are solved numerically in a 3D manner, whereas the electrochemical performance is solved analytically. This approach allowed for the detailed physics to be incorporated into the model without the requirement of a high mesh density within the MEA. Thus softening the computational load. Since it is well-known that non-uniform flow distributions within the stack’s cells and within the MEA can lead to accelerated aging of the fuel cell components, a parametric study on the anode and cathode flow rates, and methanol concentrations are examined numerically. The model was used to shed light onto the mechanisms that lead to non-uniform flow behaviour within the stack’s cells; help identify methods to maintain a uniform flow and concentration distribution within the stack; and to provide methods to minimize methanol crossover to the cathode. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 661579. Project Name: Development of a High Performance Flowing Electrolyte-Direct Methanol Fuel Cell Stack Through Modeling and Experimental Studies Acronym: FEDMFC Publication date: 2017-05-17
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".