Bubble cloud configuration effect on the added mass
Bibliographic record
Abstract
Understanding the mechanisms that control the dynamics of bubble clouds is essential to many industrial processes in the energy and chemical realms. Due to the complexity of the two-phase flow configurations, modeling the physics of the phenomena driving the mixing of bubbles within a liquid matrix is still a major challenge. One of the weaknesses of most existing two-phase flow models is due to the incomplete handling of bubble dispersion. This difficulty comes from the fact that dispersion can be driven by numerous complex phenomena, such as turbulence, local pressure distribution, bubble to bubble interaction, etc. In this study, we introduce the effect of added mass fluctuations on the dispersion of small bubbles. Existing models based on the Euler–Euler approach do not take into account local flow variations due to bubble distributions. Therefore, these models do not correctly describe fine dispersion features. Solving the potential flow around N bubbles allows to take into account the effect of the added mass on bubble cloud distributions. To this aim, a complete added mass model, which includes local bubble configurations via the void fraction gradient, is developed. The void fraction gradient allows us to account for the asymmetry of the bubble cloud around a single central bubble. Consequently, the proposed model can only represent regular and irregular bubble cloud distributions. This methodology results in a more consistent consideration of the added mass effects as well as Meshchersky's force, which should be included in hydrodynamic two-phase flow models. The proposed approach can be implemented in Euler–Euler models intended to consider the dispersion of bubbles caused by the effect of added mass.
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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 | 0.000 |
| 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".