Bipolar plate design of low temperature fuel cells by the assistance ofcomputational fluid dynamics
Bibliographic record
Abstract
Proton Exchange Membrane Fuel Cell (PEMFC) is the low-temperature type of fuel cell that generates electrical power through the electrochemical reactions. Bipolar plates (BP) are the crucial component of PEMFC which provides the path for the transport of reactant gases to the whole active area of the Fuel Cell. Poor flow-field design can lead to non-even distribution of gas flow in the cell, which can result in reactants starvation at the local area of the active cell. In addition, the pressure drop of the fuel cell system is highly dependent on the BP design, specifically when multiple cells are sandwiched together in series in the stack. Therefore, obtaining optimal flow-field pattern would be necessary for optimal design at the cell level to increase the performance and reliability of the system at the stack level. Although numerical modelling and simulation via the computers made it possible to analyze the performance and reliability of fuel cell before any fabrication, or build and test, in reality detailed numerical calculation would be challenging and expensive. Therefore, this study focuses on 2D simulation with adopting engineering assumptions to analyze the reactant flow inside the BP at the cathode side, and various possible designs of BP with different flow-field patterns are simulated and analyzed. The details of the present study will be presented at the conference.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".