Generation and characterization of fully developed state in open channel flow
Bibliographic record
Abstract
A fully developed approach flow is necessary in open channel studies to maintain commonality among datasets obtained from different facilities. Two-component planar particle image velocimetry is used to study the characteristics of fully developed smooth open channel flow at a constant Reynolds number of 3.9 × 10 4 based on the maximum velocity and flow depth. The near-bed boundary layer is tripped to achieve a fully developed state and compared with the under- and over-tripped cases. The Reynolds stresses and higher-order moments are used as indicators to establish the fully developed state. Flow properties are explored by identifying uniform momentum zones (UMZs) using the probability density function of streamwise velocities. The instances are grouped based on the number of UMZs ( N UMZ ) and conditional averaging of flow variables of each group is used to evaluate the difference in flow properties between the developed and the developing flow. Large-scale ejections are found in the logarithmic layer when N UMZ is higher, whereas a lower number indicates the existence of large-scale sweeping motions. The distribution of the conditionally averaged ratio of the shear contribution from ejections and sweeps and velocity deficits shows a vertical variability in the fully developed state. The large-scale and pointwise quadrant events are used simultaneously to depict variability in inner flow properties between developing and fully developed flow which cannot be recognized in the mean flow characteristics. The sweep events have much higher shear generation in the outer flow in the fully developed state whereas the shear stress contribution from ejection is lower than that in developing flow.
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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 | 0.001 | 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) | 0.000 | 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 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".