Erosion–Corrosion of 30°, 60°, and 90° Carbon Steel Elbows in a Multiphase Flow Containing Sand Particles
Bibliographic record
Abstract
Erosion-corrosion in flow changing devices as a result of sand transportation is a serious concern in the hydrocarbon and mineral processing industry. In this work, the flow accelerated erosion-corrosion mechanism of 90°, 60°, and 30° long radius horizontal-horizontal (H-H) carbon steel elbows with an inner diameter of 50.8 mm were investigated in an experimental closed-flow loop. For these geometrical configurations, erosion-corrosion was elucidated for erosive slug flow regimes and the extent of material degradation is reported in detail. Qualitative techniques such as multilayer paint modeling and microscopic surface imaging were used to scrutinize the flow accelerated erosion-corrosion mechanism. The 3D roughness characterization of the surface indicates that maximum roughness appears in downstream adjacent to the outlet of the 90° elbow. Microscopic surface imaging of eroded elbow surfaces disseminates the presence of corrosion pits on the exit regions of the 90° and 60° elbows, but erosion scars were formed on the entry regions of the 30° elbow. Surface characterization and mass loss results indicated that changing the elbow geometrical configuration from a small angle to wide angle significantly changed the mechanical wear mechanism of the tested elbows. Moreover, the maximum erosive location was identified at the top of the horizontally-oriented elbow for slug flow.
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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 | 0.000 |
| 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 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".