Study of Particulate Flow in the Impeller of a Slurry Pump Using PIV
Bibliographic record
Abstract
Particle Image Velocimetry (PIV) technique in conjunction with refractive index matching was successfully utilized to investigate the velocities of the slurry particles in the impeller of a centrifugal slurry pump. Tests were performed in an optically clear centrifugal slurry pump at speeds of 725 rpm and 1000 rpm using a slurry made up of sodium iodide solution as the working fluid and glass beads (500μm mean diameter) as solid particles at volumetric concentrations of 1%,2%, and 3%. In the intra blade region of the impeller, the highest particle velocities were obtained on the suction side of the blade and in the blade trailing edge region as the blade sweeps through and velocity magnitude increases with the pump speed. But this magnitude was less than that of circumferential velocity of the blade tip. Relative velocity plots show that flow separation takes place on the suction side of the blade in the region below the blade tip for clear fluid flow conditions. This was expected as the pump is made to operate with a slurry and not a single-phase liquid. At higher pump speeds and particle volumetric concentrations, a marked improvement in the slurry flow in the impeller is observed i.e., the recirculation zone decreases. This results from the centrifugal forces on the particles and its inertia at that speed. Also the slurry particles are pushed on the pressure side of the blade and slide along it which can result in frictional wear. These results are discussed in this paper.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".