Numerical modeling of inclined plane jets in a linearly stratified environment
Bibliographic record
Abstract
Wastewater discharges are widespread in industrial and natural environments, and since these discharges may cause serious impacts on the environment and ecological systems, it is important to investigate their mixing processes. The receiving fluids in many practical simulations are often stratified, and the stratification may significantly affect the mixing processes of the wastewater jets, so the mechanisms of wastewater discharges in stratified fluids are quite different from those in a homogeneous environment, and thus well-established theories and models cannot be used for predicting jets in a stratified environment. In this study, a numerical model with the re-normalization group (RNG) k-ε turbulence closure is used to model inclined plane jets in a linearly stratified environment. The characteristic parameters obtained from the experiments and simulations are compared, and the good match demonstrates the capability of the model in modeling inclined plane jets in a linearly stratified environment. The study also proposes new empirical functions, based on a dimensional analysis, for the characteristic parameters, and the proposed functions can be employed for a quick estimation of the important parameters of inclined plane jets in a linearly stratified environment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".