Flow Characteristics in a Rotating Circular Flume
Bibliographic record
Abstract
Increased construction activity and associated receiving water concerns in the Greater Toronto Area has prompted this study to advance our knowledge on cohesive sediment transport processes in rivers and lake environments. Flow characteristics in a rotating circular flume at the National Water Research Institute (NWRI), Burlington, Ontario were studied using a computational fluid dynamics (CFD) model. The objective of this study was to use a CFD model to predict the complex 3D turbulent flow characteristics, including the tangential flow velocity distribution, turbulent secondary flow circulation patterns, and the bed shear stress distributions. The numerical model was calibrated using experimental data collected using a Laser Doppler Anemometer for velocity profiles and measurements obtained by a Preston tube for bed shear stress distributions. Tangential velocity profiles and bed shear velocity distributions across the rotating circular flume were used to evaluate the accuracy of the model predictions. When compared with experimental smooth bed shear stress data, the model performed reasonably well for the range of flume speeds examined. The calibrated CFD model was then used for simulating a series of 210 scenarios using varying ring operating speeds over a range of flow depths and bed roughness heights. The numerical simulation results were then used to study the complex 3D turbulent flow conditions in the circular flume at NWRI, including velocity profiles, turbulence characteristics of flow and bed shear stress distributions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".