Investigation of the Solids Suspension and Dispersion in Newtonian and Non-Newtonian Fluids With the Coaxial Mixers
Bibliographic record
Abstract
Achieving a high degree of solid dissemination at optimum power consumption in a liquid-solid system is a challenging task. No information is available in the literature regarding slurry suspension in dense systems, slurry suspension and dissemination in non-Newtonian fluids, and suspension and distribution of poly-disperse solids in Newtonian and non-Newtonian fluids, using coaxial mixers. The prime objectives of this study were aimed at assessing the mixing capability of a coaxial system comprised of a central axial-flow impeller with a high rotating speed and an outer anchor with a low rotating speed in suspending and dispersing solid particles in water (Newtonian) and carboxymethyl cellulose (CMC) solutions (non-Newtonian) using flow visualization (electrical resistance tomography), numerical (computational fluid dynamics), and statistical (response surface methodology) techniques. The goal also included examining the suitability of a coaxial mixer in suspending the polydisperse particles in Newtonian and non-Newtonian fluids. The 2D images acquired by ERT and CFD methodologies were utilized in creating solid concentration profiles. Different mixing indexes were employed to analyze the impacts of both solid concentration gradients (axial and radial) on the degree of solids distribution. The mixingcapability of thecoaxial mixingsystemwas comparedtothat of a traditional single impeller system in terms of the degree of axial homogeneity with respect to total power drawn. RSM was utilized to determine the optimum design parameters and conditions to achieve the best suspension quality inside the coaxial reactors. The effects of different factors namely solids content, central impeller speed, anchor speed, CMC concentration, central impeller types, central impeller spacing, poly-disperse ratio, particle size, solid specific gravity, and rotating mode on the suspension and dispersion phenomena were comprehensively analyzed. The results demonstrated that the coaxial mixer facilitated
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".