Investigations on the dynamics of particle clouds in stagnant water using response surface methodology
Bibliographic record
Abstract
Disposal of sediments during land reclamation and dredging is an important component in many environmental engineering projects. Dredged material can be discharged into the ambient water either instantaneously to form a particle cloud or continuously to form a slurry jet. This paper presents the results of laboratory experiments to understand the dynamics of particle cloud in stagnant water. Different independent parameters such as particle size, nozzle diameter, cumulative mass of sand particles, and release height were tested. Due to wide ranges of selected variables and interactions between parameters, response surface methodology (RSM) technique was employed to determine the importance and effectiveness of each parameter on the growth and motion of particle clouds. A narrow range of non-dimensional cloud buoyancy in thermal regime was selected for design of experiments. Particle size was found to be the most significant parameter for the response predictions. The RSM results showed that increasing the nozzle diameter from 5 mm to 14 mm slightly increased the cloud width if all other parameters were kept unchanged. Statistical analysis of results indicated that the effect of the release height on growth of particle cloud was not significant; however, it was effective on variations of frontal velocity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".