Efficient non-hydrostatic modelling of flow and bed shear stress in a pier scour hole
Bibliographic record
Abstract
Predicting 3-D flow in a pier scour hole and the associated bed shear stress τb is important for the safe and economical design of bridge piers. This paper combines layered, hydrostatic hydrodynamic computations with non-hydrostatic pressure corrections, exploring a new modelling approach for efficient and reliable predictions of 3-D flow velocity. The law of the wall method is used for estimating τb. Its suitability for incorporation into layered models for bedload transport and pier scour simulations is also discussed. The predicted flow shows realistic features: strong downward flow adjacent to the upstream nose of a circular pier, vortex motions in the vertical and horizontal direction, and meandering flow wakes. The velocity results compare well with available experimental data. In the approach region, τb is uniform. It attains a local maximum immediately before flow enters the scour hole and then drops non-linearly in the scour-hole region toward the pier. In the wake region, τb has very low values. The τb predictions are consistent with the experimental data. In multi-layer models, when applying the law of wall method, one should use near-bed velocities as opposed to bottom-layer velocities to obtain more reliable τb estimates and avoid noisy results, which can cause a numerical instability problem in bedload transport simulations.
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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 | 0.000 | 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".