Measuring water velocity in highly turbulent flows: field tests of an electromagnetic current meter (ECM) and an acoustic Doppler velocimeter (ADV)
Bibliographic record
Abstract
Abstract Two field tests were completed to compare the performance of an electromagnetic current meter (ECM) with that of an acoustic Doppler velocimeter (ADV) in gravel‐bed rivers. Research was particularly motivated by the need to measure flow properties in highly energetic turbulent flows. Measurements were made at two field sites, one at moderate velocities (up to 70 cm/s) and with moderate turbulence intensities (10–20% of mean flow), and the other in an area of non‐uniform flow that included locations with fast mean velocities (up to 1.75 m/s) and high turbulent intensities (up to 50% of mean flow). Comparison of means, standard deviations, turbulent kinetic energy and Reynolds shear stress confirm the general agreement between the ECMs and ADVs. The general agreement is subject to limitations associated with the sample volume and frequency response of the instruments, and only applies within restricted velocity (up to ≈1.25 m/s) and turbulence intensity ranges (up to ≈0·125 m/s). At higher turbulence intensities, spectral analysis showed anomalous behavior of the ADV signal, especially in the vertical velocity component. Quadrant analysis of the Reynolds stress suggests that these problems occur predominantly in quadrants 1 and 3. Errors in ADV measurements were estimated using four different methods: one that utilized the characteristic noise floor in spectral plots, one based on internal ADV measurements of signal correlation and two techniques that aggregate errors related to various sub‐factors. Estimates were divergent at high flows. Techniques that rely on sub‐factors appeared to underestimate the impact of high turbulence on signal quality. The key conclusion for future field applications is that the older ECM technology provides the more reliable estimates of flow parameters in high turbulence. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".