Flow Structures in a U-Shaped Fuel Cell Flow Channel: Quantitative Visualization Using Particle Image Velocimetry
Bibliographic record
Abstract
Flow through an experimental model of a U-shaped fuel cell channel is used to investigate the fluid dynamic phenomena that occur within serpentine reactant transport channels of fuel cells. Achieving effective mixing within these channels can significantly improve the performance of the fuel cell and proper understanding and characterization of the underlying fluid dynamics is required. Classes of vortex formation within a U-shaped channel of square cross section are characterized using high-image-density particle image velocimetry. A range of Reynolds numbers, 109⩽Re⩽872, corresponding to flow rates encountered in a fuel cell operating at low to medium current densities is investigated. The flow fields corresponding to two perpendicular cross sections of the channel are characterized in terms of the instantaneous and time-averaged representations of the velocity, streamline topology, and vorticity contours. The critical Reynolds number necessary for the onset of instability is determined, and the two perpendicular flow planes are compared in terms of absolute and averaged velocity values as well as Reynolds stress correlations. Generally, the flow undergoes a transition to a different regime when two recirculation zones, which originally develop in the U-bend region, merge into one separation region. This transition corresponds to generation of additional vortices in the secondary flow plane.
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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 | 0.000 |
| 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 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".