Multi‐scale simulation of falling film short‐path distillation
Bibliographic record
Abstract
Abstract This study investigated issues relating to the multi‐scale simulation of falling film short‐path distillation. Both hydrodynamic conditions and gas phase kinetics have a considerable effect on the distillation process. At the micro‐scale, the DSMC (direct simulation Monte Carlo) method was adopted. Both molecular rotation and translation have been taken into account, and we obtained the temperature distribution along the distillation gap. At the meso‐scale, we used Excel as a bridge between the micro‐scale and macro‐scale. We obtained the evaporation efficiency for different temperatures. At the macro‐scale, the evaporation efficiency was used to modify the Langmuir equation, then the modified Langmuir equation was written into the mass transfer source. We used Fluent to carry out the numerical simulation. Thus, the gas phase and liquid phase were coupled. Only considering the gas phase and neglecting any surface wave in the liquid film leads to inaccurate results; this paper makes up for past deficiencies. It is found that the evaporating temperature, condensing temperature, and distillation gap have significant effects on the temperature distribution in the gas phase. The heat and mass transfer processes are correlated with the interfacial vorticity . To quantitatively describe the relation between the interfacial vorticity and concentration in the interface, we proposed a correlation coefficient . It is also observed that the average value of is 0.63, and the local maximum value of is usually located in the region of the wave trough.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".