The role of added mass in the dispersion of bubble clouds
Bibliographic record
Abstract
The mechanics of bubble clouds are essential to many industrial processes in the energy and chemical realms. In the specific case of hydroelectric turbines, bubble clouds are present when air is injected into the flow to increase dissolved oxygen content in the water flowing through the power plant. Modeling water flows through hydroelectric turbines already presents many difficulties; adding two-phase flows increases the complexity of the models. In particular, modeling the physics of the phenomena driving the mixing of bubbles in turbines is still a challenge. One important factor in existing two-phase flow models is modeling bubble dispersion. In two-phase flows, bubble dispersion comes from different sources such as turbulence, local pressure conditions and bubble-bubble interactions. In this study, we investigate the effect of added mass on the dispersion of bubbles. In the Euler-Lagrange modeling, the contribution of the added mass force in bubbly flow dispersion was quantified by the development of a repulsive force. This force is a consequence of the added mass variation. We called it the Meshchersky force. For the Euler-Euler model, the dispersion due to the added mass variation was not observed. In fact, the added mass coefficient used in this work was developed as a scalar. It was calculated in the acceleration direction. Thus, the resulting Meshchersky force has the same direction as the velocity. A better modeling would consider the added mass as a tensor rather than a scalar. Therefore, taking into account the void fraction gradient dependency to develop a correlation for the added mass tensor would be a solution to adequately model the added mass and Meshchersky forces, and hence bubble dispersion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".