Numerical studies on effects of bubbles regular array on the liquid‐phase turbulence
Bibliographic record
Abstract
Abstract It is well known that additive drag‐reducing methods have broadly developing prospects, so studies on mechanisms of additive drag reduction are very necessary. We hypothesised that the main reason for bubbles induced drag reduction is the modification on liquid‐phase turbulence structure by the addition of bubbles, and therefore such modification is the focus of our investigation. In this paper, effects of bubbles on liquid‐phase turbulence under the circumstance of regular bubble array were investigated by using Euler–Lagrange two‐way numerical simulations. The liquid‐phase velocity field was solved by using direct numerical simulations (DNS) in Euler frame of reference, and the bubble motion was tracked by using Newtonian motion equations that took into account interaction forces including drag force, shear lift force, gravity force, buoyant force, and inertia force in Lagrange frame of reference. The coupling between the phases was realised by regarding the interphase forces as momentum source terms of the continuous phase. Similarities and differences for effects of bubbles and surfactants on liquid turbulent flows were also analysed. The study indicated that addition of bubbles enhances the mean streamwise velocity, greatly reduces the Reynolds stress, and shows anisotropic suppression to the velocity fluctuations. The interphase force has a great influence on budget of energy balance. It is a gain term near the wall and is a loss term in a wide range of the channel core.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".