Estimating Transverse Mixing in Open Channels due to Secondary Current-Induced Shear Dispersion
Bibliographic record
Abstract
For transverse mixing problems in open channels, the effect of secondary currents on a tracer cloud is to disperse and mix the tracer in three dimensions more rapidly than would be the case if transverse turbulent diffusion were acting alone. Transverse mixing of this kind is difficult to simulate using two-dimensional depth-averaged mixing models which typically requires the specification of an equivalent dispersivity combining the effects of vertical shear dispersion and horizontal turbulent diffusion. In this paper, a two-dimensional vertically averaged and moment (VAM) equation technique for describing the transverse mixing mechanics of passive tracer vertical shear dispersion in approximately uniform curved open channel flow is detailed. An analytical expression is generated and compared to previous works in the literature describing the longitudinal development and asymptotic value of transverse dispersivity due to secondary current vertical shear dispersion. This expression is shown to describe the changing value of dispersivity even within the advective zone where traditional two-dimensional models cannot be applied. Distributions of depth-averaged concentration generated from numerical simulations of the VAM equations are compared to other higher fidelity computational results. The practical value of this VAM analysis framework is discussed relative to the potential for implementation in modern river modeling software packages.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".