An Updated Two-Phase Flow Regime Map in Active PEM Fuel Cells Based on a Force Balance Approach
Bibliographic record
Abstract
The water balance in proton exchange membrane (PEM) fuel cells still remains a topic of much investigation in order to maintain satisfactory cell performance. One specific water management issue relates to the gas-liquid flows that occur when water enters the reactant flow field channels, which are typically microchannels or minichannels. Due to its unique water introduction, the Lockhart-Martinelli (LM) approach has been revised for its applicability in predicting the two-phase pressure drop in these channels where water emerges from a gas diffusion layer perpendicular to the direction of gas flow. In the revised LM approach, the Chisholm parameter C is found not to vary strongly as a function of key fuel cell operating variables (relative humidity, temperature, materials, gas stoichiometry), whereas it does vary as a function of flow regime and current density. A new flow regime map was proposed based on all pressure drop data collected from active fuel cells, where an accumulating flow regime is presented in addition to single-phase, film/droplet, and slug flow. The proposed accumulating regime is linked to water droplet dynamics, namely, water droplet emergence, growth, and detachment. A force balance approach shows when detachment will occur, which clarifies the bounds of the accumulating regime in terms of superficial gas velocity (gas stoichiometry ratio) and liquid velocity (current density). The balance considers different wetting scenarios in the channels and a range of superficial velocities of importance to PEM fuel cells.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".