A Model To Predict the Concentration of Submicrometer Solid Particles in Viscous Media Confined inside Horizontal Cylindrical Channels
Bibliographic record
Abstract
As light oil reserves dwindle, the extraction and processing of heavy crude oils are becoming increasingly important. The use of ultradispersed catalysts (nanometric catalytic particles dispersed in the oil) is considered to be a promising way to upgrade these materials. To simulate such processes, mass transfer of the ultradispersed particles must be estimated. However, an adequate mathematical expression to describe the motion of these particles through viscous media in a horizontal cylindrical channel is still missing. In this paper, we developed and solved a time-dependent, two-dimensional convective−dispersive model, which simulates the transient deposition and suspension of ultradispersed particles immersed in a viscous medium, inside the cross section of a horizontal cylindrical channel in a stagnant situation. The results of the modeling are compared with a series of experiments that permit knowledge of the concentration of the ultradispersed particles inside a horizontal cylinder. These experiments were performed using particles in the submicrometer range (average sizes of 198 nm) and fluid media with diverse densities and viscosities. The effect of the fluid medium properties and the initial particle concentration in the calculation of the dispersion coefficient was also studied. The conditions needed to maintain the solid particles suspended in the liquid medium, contained in a vessel with cylindrical geometry, are also unveiled by the presented model.
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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".