Analyzing Acoustic Damping Effects in Bubble Oscillations Across Various Liquids
Bibliographic record
Abstract
The study of acoustic interactions between sound fields and bubbles has diverse applications in medicine, engineering, and biology, including optimizing ultrasound imaging, reducing watercraft damage, and advancing our understanding of aquatic animal biology. The study of sound-bubble interactions also provides a flexible tool since bubbles can be modeled using theoretical frameworks, such as simple harmonic oscillators. Our investigation focuses on modeling vibrating bubbles in various liquids as harmonic oscillators. Our research is motivated by a common Brazilian practice used to assess alcohol content, where the sound of a partially filled bottle being struck changes if the bottle is quickly inverted beforehand. This distinct sound results from the liquid’s state, particularly the bubbles formed within it. Specifically, this sound difference can be attributed to the damped oscillations of the induced bubbles in the viscous liquid. We recorded the sound spectrum before and after the rotation to compare peak frequencies. Liquids were categorized based on how long they maintained the sound difference, quantifying the damping phenomenon using quality factors from a Lorentzian fit of the sound spectrum. This established a direct relationship between the oscillation’s period and amplitude and the liquid's properties. Results indicated that as viscosity increases, the quality factor decreases, reducing bubble vibrations and causing the sound difference to fade more quickly. This study highlighted the role of viscous damping as a sound attenuator and successfully demonstrated the relationship between emitted frequencies in different beverages, enhancing our understanding of bubble dynamics.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".