Progresses on two-phase modeling of proton exchange membrane water electrolyzer
Bibliographic record
Abstract
The Proton Exchange Membrane (PEM) water electrolyzer is considered promising energy storing means for harnessing variable renewable energy sources to produce hydrogen. Understanding the internal fluid dynamics, which are often challenging to directly observe experimentally, has prompted the use of numerical models to investigate two-phase flow within PEM water electrolyzers. In this study, we provide a comprehensive review of prior research focusing on two-phase modeling of PEM electrolyzers, encompassing both components at mesoscopic scales and the full electrolyzer at the macroscopic level. We delve into the specifics of various modeling approaches for two-phase flow at different scales and summarize and discuss the current state of the art in the field. Presently, two-phase models for the full electrolyzer predominantly employ a macroscopic homogeneous assumption. However, mesoscopic and microscopic models capable of tracking phase interfaces are limited to components. Challenges persist in integrating various modeling scales into a comprehensive electrolyzer model, particularly in coupling two-phase flow between the channels and porous media. Future efforts may focus on developing multi-scale models and simulating two-phase flow under fluctuating input conditions. Additionally, given the structural similarities between PEM water electrolyzers and PEM fuel cells, we compare and discuss differences in two-phase modeling between the two technologies. This work shall offer insights for researchers in the field of two-phase modeling of PEM water electrolyzers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".