Three-Dimensional Numerical Simulations of Buoyant Jets Discharged from a Rosette-Type Multiport Diffuser
Bibliographic record
Abstract
In some outfall systems, wastewaters are discharged into ambient water bodies using rosette-type diffusers in the form of multiple buoyant jets, and it is essential to simulate their mixing characteristics for practical applications and optimal design purposes. The mixing processes of a rosette jet group are more complicated than single jets and multiple horizontal or vertical jets, and thus the existing methods cannot be effectively used to simulate their mixing and dilution properties. With the recent advancements in numerical modeling approaches, numerical simulation of wastewater jets as three-dimensional phenomena can be feasible. The present study deals with a fully three-dimensional numerical simulation for buoyant jets discharged from a rosette-type multiport diffuser, with the standard and re-normalization group (RNG) k-ε turbulence models. The simulated results are compared with experimental data, and the results show a good agreement with the experimental data, demonstrating that the numerical model is an efficient and effective tool for simulating rosette jet groups. It was also concluded that the RNG k-ε model leads to better results than the standard k-ε model with a comparable computational cost. The validated model was further utilized to investigate the influences of port inclinations on the mixing behaviors.
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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 | 0.000 | 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 | 0.001 |
| 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".