Numerical Modeling of Inflow into a Wet Bed with Complex Free-Surface Interactions using a Weakly Compressible Smoothed Particle Hydrodynamics Method
Bibliographic record
Abstract
The interaction between the surface flood and the drainage system’s outflow is an important source of uncertainty in urban flood modeling. In the present study, the Weakly Compressible Smoothed Particle Hydrodynamics method was used to model the outflow from the drainage system, considering the effect of its interaction with the surface flood. To perform modeling, a new open boundary condition was defined. First, an experimental problem of dam-break propagation over a wet bed was modeled and the numerical results were compared with the experimental data. Investigations showed that the average error of the numerical model is about 2% and its maximum error is less than 4%. Then, to control the efficiency of the defined open boundary conditions, a problem of jet injection into the water tank was investigated. It was observed that the results of the numerical model are in good agreement with the experimental data. Finally, the problem of the inflow from the bottom and its interaction with the flow caused by the dam break was modeled and its results were interpreted and compared with a volume of the fluid numerical model. In general, the results showed that the developed numerical model has an acceptable accuracy in simulating complex flows.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".