Numerical Modeling of Multiple Inclined Dense Jets Discharged from Moderately Spaced Ports
Bibliographic record
Abstract
Wastewaters are often discharged into water bodies from multiport diffusers in the form of inclined dense jets, and it is important to predict their mixing characteristics for a sound sustainable design for seawater desalination. Compared with single jets and multiple horizontal or vertical jets, the mixing processes of multiple inclined dense jets are more complicated, and thus the existing theoretical, analytical, or simplified numerical methods cannot effectively predict their dilution properties. Recent advances in numerical modeling techniques have provided a new avenue of simulating wastewater jets as three-dimensional phenomena, but their application to multiple inclined dense jets has rarely been reported. In this study, a fully three-dimensional numerical model is employed to simulate multiple inclined brine discharges from diffusers with moderately spaced ports, with the standard and re-normalization group (RNG) k-ε turbulence closures being tested. The simulated characteristic variables are compared to experimental data, and the results show that the simulations match very well with the experiments, demonstrating that the numerical model is a promising tool for simulating inclined dense jets discharged from multiport diffusers. The study also found that the RNG k-ε model performs better than the standard k-ε model without significantly increasing the computational costs.
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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 | 0.001 |
| 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".