ADV Data Analysis for Turbulent Flows: Low Correlation Problem
Bibliographic record
Abstract
This study was motivated by difficulties encountered when analysing ADV measurements taken in turbulent flows in which correlations were often lower than 70% in the bottom 5 cm of flow. The low correlations were attributed to an increase in turbulence for flows over rough boundaries and this assumption was confirmed experimentally. The influence of the ADV velocity range setting on correlation values was also examined. It was confirmed that a higher velocity range setting resulted in higher correlations for measurements away from the boundary, but in the near-bed region this was not always the case. Given the low correlation for much of our data, it was necessary to determine the variability in average velocities and Reynolds stresses for different degrees of filtering based on correlation. It was found that average velocities were much less sensitive to the value of the correlation filter than Reynolds stresses. In conclusion, for turbulent flows, ADV measurements could be used to calculate average velocities and generate velocity profiles if measured data were edited based on the correlation filter set to as low as 40%, provided that after filtering 70% or more data were retained for analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".