Evolutionary Modeling of Inclined Dense Jets Discharged from Multiport Diffusers
Bibliographic record
Abstract
Yan, X. and Mohammadian, A., 2020. Evolutionary modeling of inclined dense jets discharged from multiport diffusers. Journal of Coastal Research, 36(2), 362–371. Coconut Creek (Florida), ISSN 0749-0208.Multiport diffusers are becoming popular for discharging wastewaters into the receiving water. In this work, an approach based on multigene genetic programming (MGGP) is presented and applied to model the mixing properties of inclined dense jets discharged from multiport diffusers. Explicit mathematical models were formulated for the nondimensional terminal rise height, the nondimensional impact distance, and the nondimensional impact dilution, which are the most important parameters for describing effluent mixing properties. The developed model can be employed to predict mixing parameters as functions of the densimetric Froude number, the nondimensional port spacing, and the nondimensional current speed. The models were trained and tested with experimental data for three different ambient flow conditions: stationary, coflowing, and counterflowing. Therefore, these models are generally valid for these different scenarios. Sample Pareto-optimal MGGP models were compared, and the best ones are identified. The single-gene genetic programming (SGGP) algorithm was also utilized to develop models for the same parameters. It was found that the best MGGP models outperformed the best SGGP models and the existing empirical formulations. A confidence analysis for the best MGGP models is also reported. On the basis of a detailed evaluation of the performances of the MGGP models, the MGGP technique was proved to be a useful tool in developing models for predicting the mixing properties of inclined dense discharges from multiport diffusers.
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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 | 0.001 | 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 | 0.000 |
| 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".