Investigation of the nonlinear propagation of ultrasound through a bubbly medium including multiple scattering and bubble-bubble interaction: Theory and experiment
Bibliographic record
Abstract
Understanding of the propagation of ultrasound through a bubbly medium is a challenging task because of the nonlinear dynamics of the bubbles and their effect on the attenuation and sound speed of the medium. The majority of the studies on this subject apply linear models, which will generate inaccurate results, especially at higher-pressure excitations. These studies have also ignored the effect of bubble-bubble interaction and nonlinear multiple scattering. In this work, we have numerically simulated the attenuation and sound speed of a bubbly medium by solving our recently developed nonlinear model. An efficient method to investigate the nonlinear bubble-bubble interaction and multiple scattering is developed, and this phenomenon is included the numerical investigations through considering a cluster of 130 randomly distributed interacting bubbles with sizes derived from experimental measurements. Broadband experimental attenuation measurements of monodisperse lipid-coated microbubble solutions were performed with peak acoustic pressures ranging within 10-100kPa. The bubble solutions had mean diameters of 4-6 micron and peak concentrations of 1000 to 15000 bubbles/ml. At lower concentrations (with minimal bubble-bubble interactions), predictions of the model (attenuation and sound speed vs frequency) in the absence of interaction are in good agreement with experimental measurements. At higher concentrations, secondary peaks in the attenuation and sound speed diagrams as a function of frequency appear. Through considering the bubble-bubble interactions, the numerical results can predict the quantitative and qualitative changes in the attenuation and frequency as well as the generation of secondary peaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".