Effect of Acoustic Doppler Velocimetry Sampling Frequency on Statistical Measurements of Turbulent Axisymmetric Jets
Bibliographic record
Abstract
Acoustic Doppler velocimeters (ADVs) are used extensively in various field and laboratory studies of hydraulic engineering. However, their accuracy in predicting statistics of turbulence quantities has been questioned. Two fundamental limitations of this type of velocimeter are Doppler noise and the damping of fluctuations due to the temporal averaging performed by the instrument. An important factor that may affect both error sources is the sampling frequency of the ADV. An experimental investigation of the effect of the ADV sampling frequency on the measurement of both the mean and the high-order statistics of the flow in a turbulent jet was conducted. The experiments were carried out in the self-similar zone of an axisymmetric nonbuoyant jet at a Reynolds number of 10,000 released into quiescent water. Measurements of the mean and RMS velocities, spectra, and Reynolds shear stresses at different sampling frequencies are presented. Results were compared with those of other measurement techniques and interpreted using a novel analytical model quantifying the noise and the damping effect on the basis of their nonvanishing statistical correlation in the postaveraging domain, as well as the ratio of the flow’s integral timescale to the sampling interval. The damping effect at high sampling frequencies was eliminated using a hypothesis of proportionality of a relative change in the correlation coefficient to a change in the noise variance, provided that the integral timescale is adequately larger than the sampling interval. The proposed precision-enhancement technique (referred to herein as denoising and reverse-damping transformation) was shown to improve the accuracy of velocity variances. The results and model offer an opportunity to improve the precision of ADV measurements in turbulent flows.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".