Ex-Situ Characterization of Two-Phase Flow Regimes in Proton-Exchange Membrane Fuel Cells Under Non-Isothermal Conditions
Bibliographic record
Abstract
Efficient water management is crucial for the good performances of proton-exchange membrane fuel cells (PEMFCs). The geometric and physical characteristics of the components of a PEMFC as well as operating conditions have an impact on the transport of water through the porous transport layer (PTL) and the two-phase flow regimes in the microchannels. One parameter of importance is the local temperature, which affects properties such as surface tension and is coupled with phase change. Indeed, a temperature difference of about 5K is expected across the PTL, with spatial variations due to the geometry of the flow field plate. We present preliminary results obtained with a first experimental setup for the ex-situ characterization of two-phase flow regimes in the flow channels. Water is pushed through the PTL, which is sandwiched between a porous metal foam and the flow field plate. The air flow rate, temperature and humidity can be controlled. The cell can be heated up by applying an electrical current through the metal foam. A transparent window is located on top of the flow channel. The two-phase flow within the micro-channels is visualized using a high-speed camera and laser-induced fluorescence. Preliminary results obtained under isothermal conditions at room temperature show that different two-phase flow regimes occur in the channels depending on the operating conditions, in good qualitative agreement with data from the literature. Eventually, a new visualization cell is presented that is expected to correct the flaws of the previous design and will allow a better thermal control. It will be possible to adjust the temperature gradient and the mean temperature in order to observe their impact on two-phase flow regimes for different types of PTL and flow rates. The results will provide a better understanding of water transport in PEMFC and benchmark data for the validation of numerical models.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".