Varying Gravity Force Using Magnetic-Field Emulated Artificial Gravity: Application to Cocurrent Gas−Liquid Flows in Porous Media
Bibliographic record
Abstract
A method was proposed to investigate the effects of modifying, via magnetic fields, both modules and directions of the net gravitational body forces of gas and liquid and to follow their incidence on the evolution of pressure drop, wetting efficiency, and liquid holdup in cocurrent gas−liquid downflow and upflow fixed beds. New pseudogravity cases arose and were rationalized in terms of driving or resisting forces in two-phase flows. The method was based on applying on nonmagnetic (paramagnetic and diamagnetic) fluids strong static inhomogeneous magnetic fields generated in the vertical atmospheric bore of a superconducting magnet. Depending on the signs of the magnetic susceptibility and of the gradient of magnetic induction, the gas or liquid was subjected to macrogravity, microgravity, and retrogravity. Gas and liquid in microgravity, gas in macrogravity and liquid in microgravity, gas in retrogravity and liquid in macrogravity, and gas and liquid in retrogravity were the combinations studied. The variations of pressure drop, liquid holdup, and wetting efficiency were rationalized by distinguishing the conditions whereby the total apparent fluids’ weight under the magnetic field was either acting as a driving or as a resisting force. Liquid holdups in cocurrent downflow were found to decrease in retrogravity and increase in microgravity and macrogravity, whereas in upflow liquid holdups increased regardless of the prevailing artificial gravity.
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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".