An Analysis of Two-Phase Flow Pressure Drop in Operating Proton Exchange Membrane Fuel Cell Channels With the Lockhart-Martinelli Approach
Bibliographic record
Abstract
Proton exchange membrane fuel cells (PEMFCs) are considered one of the most promising alternatives for the automotive industry owing to their high energy efficiency, zero emission at the vehicle use stage, and low temperature operation. Water as a byproduct plays a complex role in fuel cell operation. In particular, the inevitable occurrence of liquid water leads to gas-liquid two-phase flows in various components of PEMFCs including flow channels of which diameters range from micrometers to millimeters. In conventional minichannels and microchannels, the Lockhart-Martinelli (LM) approach has been employed to predict the two-phase pressure drop of gas-liquid systems. This approach has previously been updated by our group to more accurately reflect the introduction of liquid water into the flow channels of a PEMFC i.e. from a porous media perpendicular to the gas flow. Importantly, the LM method normalizes the data independent of the flow field design and operating conditions like temperature, pressure, and relative humidity. This paper analyzes the increasing amount of experimental data on two-phase flow pressure drops/two-phase flow multipliers in the literature with these approaches. The focus is the cathode side (therefore an air/water system), and data is collected from multiple research groups using active fuel cells (electrochemically produced water). The traditional LM approach greatly under-predicts the two-phase pressure drop at low current densities. However, the analysis is applied over a range of current densities, and it better predicts results at higher current densities (>600 mA cm−2). Literature correlations for the Chisholm parameter C, a flow regime dependent parameter in the LM equation, have been proposed for non-active (external water injection) fuel cells but do not match the results from operating fuel cells. C is shown here to vary with current density, flow stoichiometry (gas velocity), gas diffusion layer, and slightly with relative humidity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".