Brownian motion and thermophoretic effects of flow in channels using nanofluid: A two-phase model
Bibliographic record
Abstract
Nanofluid is a new class of fluid that aims to enhance heat transfer. Nanoparticles sedimentation may play a role in the heat extractions from a hot surface. Brownian and thermophoretic effects may help in understanding the sedimentation effects. In the present paper, we attempted to investigate these phenomena (Brownian motion and thermophoretic effects) in three-rectangular channel configurations heated from below. The working fluids we considered are three different mixtures with 1%vol Al2O3 nanoparticles in various base fluids such as water, ethylene glycol, and a mix of 50% water and 50% ethylene glycol. Different flow rates were implemented in the model using the finite element technique. Results revealed that 1%vol Al2O3/water is the best mixture for heat removal based on thermal efficiency criteria. The presence of solid-blocks in the channel further enhanced the performance of the 1%vol Al2O3/water nanofluid; when the height/base of the blocks increases. As the nanoparticles diameter increases, the average Nusselt number and the thermal efficiency of nanofluid also increases. It appears that between 31 to100 nm particle diameter, the increase in heat extraction is minimal. For a Reynolds number below 600, as the nanoparticles diameter increase above 31 nm, the sedimentation increases accordingly. However, regardless of the nanoparticle's diameter, for a Reynolds number in the range of 840, uniform nanoparticles distribution is observed.
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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) | 0.001 | 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 | 0.002 | 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".