A quantitative analysis of turbulent drag reduction in a hydrocyclone
Bibliographic record
Abstract
Turbulent drag reduction has been observed to occur over a wide range of additive systems, such as solution of synthetic or natural polymers, and fibre suspensions. In this study, the influence of softwood kraft pulp fibres and synthetic polymer additives on turbulent drag reduction (DR) in a hydrocyclone is investigated. It was demonstrated that cellulose fibre suspensions and aqueous polymeric solutions reduce the fluid energy losses in comparison to water during hydrocyclone operation within the range of reject ratios studied. A maximum drag reduction of 58 % and 55 % was found to occur at a volume split fraction of 50 % for a 0.9 % fibre suspension and a 300 ppm anionic polyacrylamide solution, respectively. Polymer degradation or polymer chain decay displayed adverse effects for 100 ppm and 150 ppm solutions after 22 min of run time at 11.2 kW pumping power and a reject ratio of 25 %. Synergistic effects were observed with pulp suspensions containing both cationic and anionic polyacrylamide (CPAM and APAM, respectively); a maximum DR of 41 % was observed for a 0.7 % fibre suspension containing 100–300 ppm of polymer at a reject ratio of 50 %. Similarly to aqueous polymer solutions, the degradation of 300 ppm APAM or CPAM in a 0.7 % fibre suspension decreased the observed DR up to 38 % after 30 min of run time at 11.2 kW pumping power and a reject ratio of 25 %. The near 43 % reduction in CPAM concentration, due to surface adsorption, when present in a 0.7 % fibre suspension assisted in quantifying the DR variations observed between suspensions containing APAM or CPAM.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".