Flow characteristics and wear prediction of Herschel‐Bulkley non‐Newtonian paste backfill in pipe elbows
Bibliographic record
Abstract
Backfill process has become standard practice in mining industry where the backfill slurry is transported from surface to underground via a pipeline system. Paste backfill is one of the types of backfill slurries which in recent years has gained popularity due to its reduced water content, fast solidification time, and environmentally friendly reputation. However, wear and erosion of the pipe have been a major issue in some paste backfill pipeline operations. Paste backfill behaves as a non‐Newtonian fluid and can be modelled as a Herschel‐Bulkley fluid. To better understand the flow behaviour and wear rate of paste backfill in underground pipeline systems, experimental and numerical studies were carried out. The former focuses on the slump test and L‐pipe flow test to characterize paste backfill properties, while the latter aims to develop a three‐dimensional mathematical model to evaluate flow and wear characteristics in pipe elbows. To ensure robust and accurate solutions, the model was verified with analytical solutions and validated against experimental data. The numerical results suggest that elbow design and paste backfill property significantly affect secondary flow generation which is further reflected in the pipe wear rate. Thicker paste backfill slurry flowing in the 5D elbow yields the lowest wear rate which is beneficial for practical application, albeit it comes at a higher pressure drop.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".