Bubble Effects on the Acoustic Doppler Velocimeter (ADV) Measurements
Bibliographic record
Abstract
Acoustic Doppler Velocimeter (ADV) is a useful technique for measuring flow velocities with frequency variations of up to approximately 200 Hz in laboratory settings and in field applications. Although measuring velocity with ADV has advantages over other velocity measurement methods, this technique is sensitive to operating conditions: in addition to noise, the signal can contain spikes with large amplitudes, a disadvantage of ADV. In this study, the effect of bubbles on ADV signals is experimentally assessed in a laboratory setting. Bubbles can intersect the sampling volume and the acoustic beams creating spikes. The impact and amplitude of these spikes is a function of the bubble size and position when it crosses the ADV sampling volume and the acoustic beams. Bubbles that intersect the sampling volume generate spikes in all three velocity directions simultaneously; bubbles that intersect acoustic beams, which span between the sampling volume and the ADV receivers, impact the velocity data in one or two directions, and has a negligible effect in the third direction. Bubbles that intersect the X-direction acoustic beam create spikes in velocity data in both X- and Z-directions, but have no significant impact on the Y-direction; the Y- and X-directions have spikes and the Z-direction is not significantly impacted, when bubbles intersect the Y-direction acoustic beam. In addition, spikes increase the magnitude of the power spectra at high frequencies. Without bubbles, the autocorrelation in the time domain decreases in value as the time-lag increases, approaching zero after 5 seconds. The presences of bubbles cause a large peak in the autocorrelation at a zero time-lag, and no autocorrelation thereafter. Furthermore, the autocorrelation without bubbles permit turbulence length scales to be calculated because of the positive autocorrelation value; unless spikes are removed by using an appropriate filter when bubbles are present, turbulence length scales cannot be calculated because the autocorrelation is zero.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 0.001 |
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".