Bibliographic record
Abstract
In the present study, the behavior of various sizes of black powder particulates, carried by a turbulent flow of natural gas, is numerically predicated in a horizontal pipeline. The particles are magnetite and are considered as discrete or a dispersed phase; however, the gas phase is considered as a continuous phase. The numerical approach taken to simulate the dispersed phase is a Lagrangian approach, which is essentially computation of particles trajectories. The turbulence effect on the dispersion of the particles, due to turbulent eddies in the gas phase, is predicted using a stochastic discrete-particle approach. Several case studies have been examined and they include: instantaneous injection of diverse particle sizes, continuous injection of five different particle sizes and multiple injection. For the case with instantaneous injection, it has been found that sudden injection of relatively high mass loading of particles would alter the flow profile in the core region and subsequently increases the turbulent intensity. For all cases it has been found that most particles in the core region of the flow move faster than the gas. Also, for all studied cases, the velocity profiles of gas and particles, at different pipeline stations, have been presented and analyzed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".