Measurement techniques to characterize bubble motion in swarms
Bibliographic record
Abstract
A technique was developed to study the effect of surfactant (frother) on individual bubble motion in swarms. The technique was based on high speed cinematography and tracking of multiple moving objects. Image processing algorithms were implemented in Matlab to isolate and measure geometric properties of the bubbles in image sequences recorded at 1 ms interval; and these properties were compiled into a data structure. To track a bubble, the geometric properties and a matching criterion were applied on consecutive pictures to identify the bubble. The bubble trajectory was reconstructed from the data structure for the matched objects. To maximize the number of bubbles identified from an image, de-clustering algorithms were developed and validated. A new shape factor model for ellipsoidal objects and a correction model for pixelation effect were developed. To characterize the level of bubble interaction in a swarm, a technique for measuring average dimensionless bubble inter-distance was developed. To characterize the effect of frother type on bubble surface a new technique for measuring surface flows (Marangoni effect) on a bubble blown in air was developed. The results provided experimental evidence of the mechanisms by which surfactants dampen bubble oscillation and reduce bubble terminal rise velocity and helped to interpret the frother type effect. Experiments to determine the effect of surfactant (concentration and type), bubble interactions and bubble size distribution type on bubble velocity in swarms were conducted using the bubble tracking technique in a rectangular transparent column (12x5x140 cm) with an inclined top section (15°). A flat bubble swarm was generated combining a slot (60 mm x 60 microm) and porous-slot (60 x 1.1 mm) spargers. These combinations allowed the generation of bubble size distributions similar to those in industrial flotation machines. Bubble images were collected at three locations (near the point of generation, the top of the straight section and in the inclined section) to track the impact of surfactant accumulation. The results showed that the presence of surfactant reduced bubble coalescence and breakage and stabilized the bubble surface. As a consequence, the bubble size distribution remained stable as the swarm rose. Experimental measurements of bubble motion in the presence of surfactants showed for both single bubbles and swarms, that surfactant accumulation occurs as the bubbles detach from the generating point, and the impact occurs gradually, determined by the subsequent evolution of bubble aspect ratio. The bubble tracking measurements revealed a velocity-size profile, which was determined by: the predominant bubble size class, bubble interactions (dimensionless bubble inter-distance), and the surfactant type and concentration. The surfactant type seems to be a factor in determining bubble rise velocity for bubbles below 0.8 mm, despite the theory that surface mobility for such small bubbles in unaffected by surfactants. It is thus proposed that surface viscosity plays a role on bubble motion. The new techniques for bubble motion and surface characterization were tested for bubble swarms in presence of Polyglycol and Pentanol. Two contributions to the knowledge emerging from this phase of the work are: (a) bubble motion in swarm describes a bubble velocity-size profile with a high dependency on the bubble distribution type, and (b) surfactant type influences bubble rise velocity for bubbles in range 0.1 to 4 mm.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".