Oxygen Bubble Nucleation Modeling in a PEM Electrolyzer Electrode
Bibliographic record
Abstract
The polymer electrolyte membrane (PEM) electrolyzer is a promising technology that reduces water into hydrogen and oxygen, from which the hydrogen is captured and stored for use in fuel cells. One of the challenging issues in PEM electrolyzers is flow inhibition of liquid water within the porous gas diffusion layer (GDL), due to the formation of oxygen bubbles (1). The bubbles block pores within the GDL, limiting the transport of liquid water to the catalyst layer, which in turn negatively affects the electrolyzer performance (2). Moreover, the presence of the oxygen bubbles can increase the solution resistance, inhibit electron transfer, and consequently increase ohmic losses, leading to efficiency reduction (1). Investigation of the oxygen bubbles behavior generated at the anode side of the PEM electrolyzer in operational pressure and temperature can give us a more accurate insight for predicting its influence on the performance. A number of promising studies on bubble nucleation and detachment have been done over the past few years. Various numerical techniques were introduced and improved to increase the accuracy of the bubble interface tracking such as volume-of-fluid (VOF) (3), moment-of-fluid (MOF) (4), and level-set (LS) methods (5). In this study, a numerical simulation has been performed to mimic the nucleation, growth and detachment of the oxygen bubbles in electrolyzers. Utilizing a state-of-the-art multiphase algorithm (6), a three-dimensional, two-phase computational algorithm was developed using the LS method to track the oxygen bubble interface expansion. To simulate the multiphase system more precisely, the thermodynamic free energy of the system has been taken into account by employing the Gibbs free energy function. The behavior of the oxygen bubble through the GDL as a function of the geometrical properties of the GDL was then studied and compared to the experimental results presented by Arbabi et al. (7). Acknowledgements The authors would like to gratefully acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC). References 1. H. Matsushima, T. Nishida, Y. Konishi, Y. Fukunaka, Y. Ito and K. Kuribayashi, Electrochimica Acta, 48, 4119 (2003). 2. H. Ito, T. Maeda, A. Nakano, C. M. Hwang, M. Ishida, A. Kato and T. Yoshida, International Journal of Hydrogen Energy, 37, 7418 (2012). 3. C. W. Hirt, J. L. Cook and T. D. Butler, Journal of Computational Physics, 5, 103 (1970). 4. V. Dyadechko and M. Shashkov, Journal of Computational Physics, 227, 5361 (2008). 5. S. Osher and J. A. Sethian, Journal of Computational Physics, 79, 12 (1988). 6. H. Montazeri and C. A. Ward, Journal of Computational Physics, 257, Part A, 645 (2014). 7. F. Arbabi, A. Kalantarian, R. Abouatallah, R. Wang, J. Wallace and A. Bazylak, ECS Transactions, 58, 907 (2013).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".