Hydrostatic versus nonhydrostatic hydrodynamic modelling of secondary flow in a tortuously meandering river: Application of <scp>Delft3D</scp>
Bibliographic record
Abstract
Abstract Given the importance of pressure gradients in driving secondary flow, it is worth studying how the modelled flow structures in a natural river bend can be impacted by the assumption of hydrodynamic pressure. In this paper, the performance of hydrostatic versus nonhydrostatic pressure assumption in the three‐dimensional (3D) hydrodynamic modelling of a tortuously meandering river is studied. Both hydrostatic and nonhydrostatic numerical models were developed using Delft3D‐Flow to predict the 3D flow field in a reach of Stillwater Creek in Ottawa, Canada. An acoustic Doppler velocimeter was employed to measure the 3D flow field at a section in a sharp bend of the simulated river at two flow stages. The results of the Delft3D hydrostatic model agreed well with the acoustic Doppler velocimeter measurements: The hydrostatic model predicted reasonably accurately both the streamwise velocity distribution across the section and the magnitude and location of the primary secondary flow cell. The results of the Delft3D nonhydrostatic approximation showed that the model was not conservative and could not accurately generate either the secondary flow or the streamwise velocity distribution. This study illustrated the superior performance of the hydrostatic over nonhydrostatic 3D modelling of the secondary flow using Delft3D. Several possible reasons for unfavourable performance of the nonhydrostatic version of Delft3D are discussed, including the pressure correction technique employed in Delft3D. Considering the uncertainties that may arise in both modelling and field measurements, the 3D hydrostatic Delft3D model was capable of reasonably predicting the river bend flow structures in the studied meandering creek.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".