Modelling of three-dimensional flow velocities in a deep hole in the East Channel of the Mackenzie Delta, Northwest Territories
Bibliographic record
Abstract
The Mackenzie River has several anomalous deep scour holes in a number of river channels in its delta. Proposed gas pipeline crossings have renewed interest in studying the stability of these scour holes. The main goal of this research project was to study flow velocity and bed shear stress distributions for a 30 m deep hole in the East Channel of the Mackenzie Delta as a first step toward assessing the stability of the scour hole and the risk of its migration during various flow conditions. In this study, a three-dimensional (3D) finite element flow model, FLUENT, using the renormalization group (RNG) k-ε turbulence model (where k is the turbulent kinetic energy and ε is the turbulence dissipation rate) was set up for the scour hole and calibrated using detailed measurements of 3D flow velocities, obtained with an acoustic doppler current profiler. The numerical model was then applied to predict flow velocity and bed shear stress distributions in and around the scour hole for three flow conditions (720, 1000, and 1400 m3/s). Results indicate that two vortices are formed in the river elbow above the scour hole. As the flow rate changed, the sizes of the vortices varied. The region upstream of the hole experienced the greatest magnitudes of bed shear stress.Key words: computational fluid dynamics, finite element, bed shear stress, deep hole, flow reversal.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".