Experimental and Numerical Investigations of Two-Phase Electrolysis Processes - Electrical Energy Conversion in Hydrogen Production
Bibliographic record
Abstract
During two-phase electrolysis processes, for example for hydrogen production, there are bubbles which are created at electrodes. This implies a great vertical motion source in the normal earth gravity field and then a quite important natural two-phase convection. All other fields are then affected. Heat, mass and electricity transfers are modified due to both bubbles screening (at surface and in volume) and to bubbles transport promotion. Many numerical modeling for two-phase processes such as kerosene pulverization in engines or coal combustion sciences have shown the difficulties of these multi-physics processes. Both particles and reactor scales must be considered according with a strong coupling modeling. In these processes the particles injection is "in the flow". In boiling or electrolysis processes, a new difficulty is added: particles birth or injection is strongly coupled to the local flow properties and leads to a complex boundary condition at surfaces. Electrical and electrochemical properties and processes are disturbed. This disturbance can lead to the modification of the local current density and to anode effects for example. There is few works concerning the local modelling of electrochemical processes during a two-phase electrolysis process. There are also few local experimental measurements in term of chemical composition, temperature or current density which will allow the numerical calculations validation. The present work shows the started numerical modeling strategy and the first results, both experimental and numerical obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".