Dynamic sound scattering: Field fluctuation spectroscopy with singly scattered ultrasound in the near and far fields
Bibliographic record
Abstract
Dynamic sound scattering (DSS) is a powerful acoustic technique for investigating the motion of particles or other inclusions inside an evolving medium. In DSS, this dynamic information is obtained by measuring the field autocorrelation function of the temporal fluctuations of singly scattered acoustic waves. The technique was initially introduced 15 years ago, but its technical aspects were not adequately discussed then. This paper addresses the need for a more complete account of the method by describing in detail two different implementations of this sound scattering technique, one of which is specifically adapted to a common experimental situation in ultrasonics. The technique is illustrated by the application of DSS to measure the mean square velocity fluctuations of particles in fluidized suspensions, as well as the dynamic velocity correlation length. By explaining the experimental and analytical methods involved in realizing the DSS technique in practice, the use of DSS will be facilitated for future studies of particulate suspension dynamics and particle properties over a wide range of particle sizes and concentrations, from millimeters down to nanometers, where the use of optical techniques is often limited by the opacity of the medium.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".