Effect of ultrasound pressure and bubble-bubble interaction on the nonlinear attenuation and sound speed in a bubbly medium
Bibliographic record
Abstract
The presence of bubbles changes the attenuation and sound speed of a medium. These changes in medium properties depend on the nonlinear behavior of bubbles which are not well understood. Previous studies employed linear models for the calculation of the attenuation and sound speed of bubbly mediums. These predictions are not valid in the regime of nonlinear oscillations. In addition, bubble-bubble interactions are often neglected. In this work, we have numerically simulated the attenuation and sound speed of a bubbly medium by solving a recently developed nonlinear model and considering the bubble-bubble interactions. A cluster of 52 interacting bubbles was simulated, with sizes derived from experimental measurements. Broadband attenuation measurements of monodisperse solutions were performed with peak pressures ranging within 10-100 kPa. The bubble solutions had mean diameters of 4-6 micron and peak concentrations of 1000 to 15000 bubbles/ml. At lower concentrations (with minimal microbubble interactions) model predictions are in good agreement with experimental measurements. At higher concentrations, new secondary peaks in plots of attenuation and sound speed as a function of frequency appear. By simulating bubble-bubble interactions, the numerical results could predict the frequency shift of the peaks of attenuation and sound speed, and the generation of secondary peaks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".