Exploring two-phase bubble dynamics and convective mass transfer in thermochemical Cu–Cl cycle for hydrogen production
Bibliographic record
Abstract
• Sparger sizes influence on the bubble size distribution and mass transfer analyzed. • An empirical mass transfer model has been developed using Buckingham PI theorem. • Developed an empirical correlation: Sh = Re 0.726 Bo 0.083 • Bubble dynamics of the Cu–Cl for H 2 production could be optimized. • A non-linear relationship between bubble size and mass transfer rate is observed. In water-splitting processes such as thermochemical cycles for hydrogen production, phase transitions cause bubble flow, vapor transfer, and gas redissolution. Proper bubble formation and distribution can significantly reduce energy required for gas dispersion and mixing, enhancing operational efficiency. The research examines bubble dynamics across various sparger sizes and develops a mass transfer model. The Re numbers range from approximately 630 to 1660, and the corresponding Bo numbers vary between 0.35 and 4. An empirical correlation for the Sh number is developed using a multi-objective genetic algorithm, demonstrating excellent predictive capability (R 2 ≈ 0.98). A predictive 1D model for a rising bubble in rectangular reactors is formulated, which is directly applicable to water-splitting processes such as the thermochemical Cu–Cl cycle. Results demonstrate that as bubbles grow larger, the increase in mass transfer rate becomes less pronounced due to a relatively smaller interfacial area for mass transfer per unit volume of gas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".