3D Numerical Study of a Flowing Electrolyte – Direct Methanol Fuel Cell With an Implemented Bi-Layered Membrane Electrode Diaphragm Assembly
Bibliographic record
Abstract
In the present work, fluid dynamic simulation and experimental studies are compared to assess the validity of using computational fluid dynamics (CFD) to accurately predict the pressure losses experienced across each of the three fluid channels in a flowing electrolyte direct methanol fuel cell: methanol flow through anodic-serpentine channels; air flow through the cathodic-serpentine channels; dilute sulfuric acid flow through the flowing electrolyte (FE) channel located between two membrane-electrode assemblies (MEAs). The methanol flow rate is varied from 5 to 25 mL/min and the airflow is varied from 0.5 to 5 L/min. The flowing electrolyte flow rate is also varied from 5 to 25 mL/min in order to analyze pressure levels within the FE channel, which, according to this analysis, must be larger than the adjacent serpentine channels. This pressure difference is particularly important to maintain the distance (and flow structure) between the MEAs without affecting performance of the fuel cell. Adequately controlling the pressure of each of three fluids disables the MEAs ability to deform without the use of an electrolyte spacer, effectively creating an inter-dependent bi-layered membrane electrode diaphragm assembly (Bi-MEDA). Through CFD simulation, it was observed that pressure equalization through the Bi-MEDA approach supports the elimination of a flowing electrolyte channel spacer from current FE-DMFC designs. The reduction of the spacer is expected to decrease ohmic losses currently experienced in all FE-DMFC designs. Despite several approximations, simulations predicting pressure losses throughout the two serpentine fuel channels are compared against obtained experimental data, showing relatively good agreement for a single cell arrangement.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".