An Open-Source Toolbox for Multiphase Flow Simulation in a PEM Fuel Cell
Bibliographic record
Abstract
A proton exchange membrane (PEM) fuel cell is an electrolytic cell that can convert chemical energy of hydrogen reacting with oxygen into electrical energy with no greenhouse gases generated in the process. To satisfy increasingly demanding application needs, providing fuel cells with better performance and higher efficiency are of paramount importance. Computational fluid dynamics (CFD) analysis is an ideal method for fuel cell design optimization. In this paper, a comprehensive CFD-based numerical tool that can accurately simulate multiphase flow and the major transport phenomena occurring in a PEM fuel cell is presented. The tool is developed using the Open Source Field Operation and Manipulation (OpenFOAM) software (a free open-source CFD code). This makes it flexible and suitable for use by fuel cell manufacturers and researchers to get an in-depth understanding of the cell working processes to optimize the design. The distributions of velocity, pressure, chemical species, Nernst potential, current density, and temperature at case study conditions are as expected. The polarization curve follows the same trend as those of the I-V curves from numerical model and experimental data taken from the literature. Furthermore, a parametric study showed thekey role played by the concentration constant in shaping the cell polarization curve. The developed toolbox is well-suited for research and development which is not always the case with commercial code. The work therefore contributes to achieving the objectives outlined in the International Energy Agency (IEA) Advanced Fuel Cell Annex 37 which promotes open-source code modelling of fuel cells. The source code can be accessed athttp://dx.doi.org/10.17632/c743sh73j8.1.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".