A Flow Model for the Settling Velocities of non Spherical Particles in Creeping Motion, Part II
Bibliographic record
Abstract
This paper follows previous work regarding the settling velocity of non spherical particles in creeping motion.In the previous work it was found that the shear stress in the fluid is opposed the mass of the fluid.The challenge of the shear stress by the mass imply a pressure gradient by default, i.e. the transfer of the shear stress to the mass is in the form of a surface stress (Pa/m), perpendicular to the shear stress, controlled by the mechanics of viscosity.The dynamics are triggered by the wall shear of the particle.Examination using measured settling velocities shows that the pressure gradient is a unique value for the fluid properties, so that the computed shear stress equal to the viscosity when the velocity gradient is equal to unit and the velocity is satisfied simultaneously, hence, defining the size of the expansion about the shear stress.We learned that application of the viscosity principle demand simultaneous consideration of the volumetric nature of the pressure gradient and the geometry dependence of the velocity gradient.We here undertake an examination to find how the pressure gradient is controlled by the fluid properties and a solution is reached.The solution is in good agreement with published experimental data.In addition we pursued further improvement of the relationships derived previously with further simplification.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".