Brownian Motion and Thermophoretic Effects in Mini Channels with Various Heights
Bibliographic record
Abstract
Flow-through mini channels have received tremendous interest from researchers over a long period. However, the study of flow between the channel and on top of the channel has received little to no attention. In the present paper, different parameters have been used to investigate this heat enhancement. The height of 10 mini channels has been varied, allowing the corresponding aspect ratio to vary from 3 to 6, 9, and 12. When the aspect ratio is 12, flow circulates through the mini channel only, and when the aspect ratio is less than 12, flow is distributed between the one circulating inside the channel and moving on top of the channel. Different flow rates are studied corresponding to a Reynolds number varying from 250 to 1250 if water is the working fluid. Brownian and thermophoresis effects are taken into consideration to investigate the nanoparticle sedimentation. Results revealed that the optimum configuration, if one needs to take into consideration the friction factor, is 12. If one ignores the pressure drops, then the optimum configuration is when the aspect ratio is equal to 6. This means that the flow interaction between the one circulating in the channel and above the channel plays a major effect in heat removal.
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 | 0.000 |
| 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".