Effect of Acoustic Doppler Velocimeter Sampling Volume Size on Measurements of Turbulence
Bibliographic record
Abstract
Abstract The relatively large sampling volume of acoustic Doppler velocimeters (ADVs) is expected to influence their measurement of turbulence. To study this effect, a series of experiments using different sampling volume sizes was conducted in an axisymmetric turbulent jet. The results show that the mean velocities are not significantly affected by the size of the sampling volume. On the other hand, reducing the sampling volume size results in an increase in the variances of the u and υ velocities, while its effect on the variance of the w velocity is negligible. Application of a noise-reduction method to the data renders the velocity variances nearly independent of sampling volume size, suggesting that the difference was mainly due to Doppler noise. The principal conclusion of this work is, therefore, that—as long as the characteristic length of sampling volume is much smaller than the integral length scale of flow—increasing the sampling volume size (i.e., increasing spatial averaging over highly correlated scatterers) can reduce Doppler noise and result in more accurate measurements of the velocity variances. Application of noise-reduction methods to the data is found to be especially important when the sampling volume size is reduced to capture smaller scales, or for near-boundary measurements. Furthermore, noise due to mean velocity shear, even at the largest velocity gradient along the jet radial profile, is found to be negligible in the present work.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".