CFD application in slurry transport through Annular Jet Pump -A Mixture Model Approach
Bibliographic record
Abstract
The transport of slurry plays a critical role in determining the efficiency, cost, and sustainability of largescale mining operations.Annular Jet Pumps (AJPs), owing to their simple geometry, absence of moving parts, and low maintenance demands, represent a promising alternative to conventional pumping systems.This study presents a detailed numerical investigation of sand-water slurry flow in an AJP using the mixture model within a CFD framework.The Realizable k-ε turbulence model is incorporated to capture the multiphase turbulence characteristics, enabling accurate prediction of particle-fluid interactions and energy dissipation mechanisms.A comprehensive parametric analysis is conducted to assess the influence of dispersedphase particle size, solid volume fraction, and geometric parameters, including nozzle radius and convergence angle, on suction performance, pressure recovery, and specific energy consumption (SEC).The results indicate that careful optimization of operating and geometric parameters can substantially enhance suction capacity while minimizing SEC, thereby improving the overall energy efficiency of the system.Model predictions are validated against established experimental and numerical benchmarks from the literature, showing strong agreement and confirming the reliability of the adopted methodology.The outcomes of this work underscore the potential of modular AJPs as sustainable, energy-efficient solutions for slurry transport in mining, with broader implications for reducing environmental footprint and operational costs.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".