Compressional rheology model for vacuum filtration-consolidation of sludge in geotextile tubes
Bibliographic record
Abstract
Recently, the disposal of high-water-content dredged sludge through geotextile tubes with vacuum-assisted prefabricated horizontal drains (PHDs) has gained growing popularity for its convenience and efficiency. However, existing simulations for this typical coupled filtration-consolidation process using conventional consolidation models exhibit deficiencies due to the absence of effective stress and invalidity of Darcy's law in the particle-wandering filtration phase. In this study, based on the compressional rheology theory, a two-dimensional coupled filtration-consolidation model constituted by the compressive yield stress Py( ϕ) and hindered setting factor r( ϕ) is developed to elucidate the solid–solid and solid–fluid interactions during slurry dewatering. A novel approach for measuring consistent Py( ϕ) and r( ϕ) relationships is proposed. The numerical solution is derived utilizing the alternating direction implicit difference method and verified against a classical one-dimensional compressional rheology model and a field trial. Further analysis suggests that constitutive parameters, e.g., gel point, affect the dewatering efficiency by determining the relative degree of soil disorder, obstruction to particle movement, and vacuum transmission effect, while design parameters, e.g., PHD spacing and tube height, impact the magnitude and radiation range of vacuum pressure to influence the overall efficiency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".