A new analytical model for dip modified velocity distribution in fully developed turbulent open channel flow
Bibliographic record
Abstract
This paper presents a new analytical model to predict the streamwise time-averaged velocity profile affected by the dip phenomenon in open channel flows. The novel approach of the present study is that the Finley wake law has been used instead of Coles’ wake law for the outer layer. To validate the new analytical model, six high quality experiments were conducted in a hydraulically rough bed open channel flow by considering variations of aspect ratio, defined as the ratio of the width of the channel to the depth of flow, from 2 to 4. In these controlled experiments, the time-averaged velocities were measured using a Nortek Vectrino-plus acoustic Doppler velocimeter. In addition, 14 sets of available experimental data, including five field experiments conducted across the globe were also used to test the performance of the proposed model. The proposed model, the Finley-dip-modified-log-wake law (FDMLWL), was used to develop a semiempirical equation to compute the dip position as a function of the dip correction factor and the wake parameter. In addition, using the experimental data and FDMLWL, an empirical equation was developed to compute the dip correction factor for hydraulically smooth open channel flows. The comparison of the FDMLWL model with the experimental data belonging to hydraulically smooth, transition, and rough regimes has consistently indicated better representation of the velocity dip phenomenon. The FDMLWL model has also been compared with other analytical models available in the literature and the superior performance of the proposed model is further observed. Finally, based on the satisfactory validation between experimental data and FDMLWL, it is inferred that the proposed model is better suited for modeling zero velocity gradient at the boundary layer edge, as in open channel flows with dip phenomenon.
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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 | 0.000 | 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) | 0.000 | 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".