Assessing different methods of generating a three-dimensional numerical model mesh for a complex stream bed topography
Bibliographic record
Abstract
Three-dimensional numerical models of flow over complex bed geometry are becoming widely used in river and coastal engineering. Boundary-fitted coordinate grids are typically used to deal with this problem in natural channels. Recently, a regular structured grid method based on numerical porosity has been developed for high-resolution gravel-bed models. A simpler alternative approach is to use a Cartesian mesh with an interpolated 3D solid object representing the river bed. The objective of this study is to assess the impact of these three methods of mesh generation on the simulated flow field above complex bed topography around stream deflectors in a laboratory setting. Results show marked differences between the three types of simulations when running mesh sensitivity analysis. Because the bed porosity approach uses the digital elevation model (DEM) information in each cell to represent bed topography, it requires a finer mesh resolution in order to reach an accurate solution. Although there was good qualitative agreement between the simulated flow fields and Acoustic Doppler Velocity measurements, the quantitative comparison showed relatively poor agreement for all mesh design types. However, the three mesh types were in good agreement when compared to each other for velocity and pressure variables (average correlation coefficient, r, of 0.95), with the 3D object bed and bed porosity showing the best agreement (r = 0.97). Simulated turbulence variables (KE and EP), however, showed more scatter (r = 0.85) and slopes markedly different from unity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".