Characterization of the hydrodynamics within a toroid wear tester
Bibliographic record
Abstract
Abstract The toroid wear tester (TWT) is a lab‐scale device used for the assessment of slurry erosion in pipelines. Historically, its application has been limited to the relative ranking of material performance under different slurry flow conditions; however, recent studies have indicated that TWT tests could be predictive and directly applied to slurry pipeline design—provided that the flow inside a TWT is better characterized. In the present study, air‐liquid multiphase flow inside the TWT was investigated. Torque measurements were taken to characterize friction loss for different air‐liquid combinations. A visualization experiment was also conducted to evaluate flow patterns within the TWT. In the experiment, the displacements of spherical glass beads were used to estimate velocity vector fields for different TWT rotational speeds. A computational fluid dynamics (CFD) analysis was also conducted to complement the experimental measurements. A 3D transient analysis using the volume of fluid (VOF) approach was used to model the system. The simulation results agreed closely with the experimental findings. Furthermore, the simulations revealed that strong secondary flows (back flow, rotation) exist in the TWT. These type of flows do not occur in horizontal pipelines. Therefore, to use the TWT as a tool for slurry pipeline wear assessment, the differences in the flow field between the two systems must be properly quantified.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".