Bibliographic record
Abstract
The micromechanical process of solid particle erosion can be affected by a number of factors, including impact angle, flow geometry, and particle size and shape. Erosion can also be affected by fluid properties, flow conditions, and the material comprising the impact surface. Of these several different potential impacting factors, the most critical ones for initiating erosion are particle size and matter, carrier phase viscosity, pipe diameter, velocity, and total flow rate of the second phase. Three turbulence models which are heavily dependent on flow velocities and fluid properties in their environment are k-epsilon (k-ε), k-omega (k-ω) and The Shear Stress Transport Model (sst). More extreme erosion generally occurs in gas-solid flow for geometries which experience rapid alterations in flow direction (e.g., in valves and tees) because of unstable flow and local turbulence. The present study provides results from computational fluid dynamics (CFD) simulations that feature dilute water-solid flows in complex pipelines, highlighting the dynamic behavior displayed by the flows’ entrained solid particles. Specifically, the impact of fluid velocities in relation to erosion location is tested on sand particles measuring 10, 70, 100 and 200 microns. For the CFD analysis testing, liquid velocities of 20, 25, 30, 35 and 40 m/s are applied. The difference is evident between velocities of 20 m/s and 40 m/s, giving an erosion rate of 1.73 x10⁻⁴ kg/m².s and 2.11x10⁻³ kg/m².s, respectively, when the particle solid is 200
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.001 |
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".