Numerical investigation of the subharmonic response of a cloud of interacting microbubbles
Bibliographic record
Abstract
Microbubbles (MBs) usually exist in polydisperse populations and often strongly interact with each other. Accurate investigation of the dynamics of the MBs requires considering the interaction between Microbubbles. We have developed an efficient method for numerically simulating N interacting MBs. The subharmonic (SH) responses of a polydispersions of 3-52 microbubbles with sizes between 2-5 microns, excited with ultrasound with a frequency and pressure of 1.8-8 MHz and 1-500 kPa were investigated. We show that if the frequency is set to the SH resonance of the larger MBs, the smaller MBs oscillations are controlled by the larger MB and the SH amplitude of the population of MBs increases. For small enough distances between MBs, one large MB may control the oscillations of the rest. If the excitation frequency is equal to the SH resonance of the smaller MBs, there exists two pressure regions. For lower pressures, the oscillations of the larger MB are out of phase with the smaller MBs and the resulting SH amplitude is smaller than the case without the bigger MB. As the pressure increases the oscillations of the larger MB becomes in phase with the smaller MBs and the SH response of the system is enhanced.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".