Effectiveness in incorporating Brownian and thermophoresis effects in modelling convective flow of water-Al<sub>2</sub>O<sub>3</sub> nanoparticles
Bibliographic record
Abstract
Purpose Nanofluids are widely used in heat transfer phenomena owing to the higher rate of heat removal as compared to their base fluids. Nanoparticle’s motion in nanofluids is analysed by slip mechanisms that consider physical properties, which can be found in literature. It is assumed that among few, only Brownian motion and thermophoresis affect the slip mechanism to produce a relative velocity between the nanoparticles and the base fluid. The purpose of this paper is to study the effects of Brownian motion and thermophoresis in a square cavity by considering it pure fluid as well as porous cavity. Design/methodology/approach A finite element method is used to solve the flow porous equations together with the heat transfer equation and the mass transfer equation numerically. The heat and mass transfer equations were modified to take into consideration the Brownian motion as well as the thermophoresis effect. Findings A negligible amount of Brownian motion and thermophoresis effect has been found by considering 1 to 3 Vol.% of aluminium oxide as nanoparticles suspended in base fluid of water. Practical implications This study has provided an interesting insight into the importance of Brownian motion as well as the thermophoresis effect in heat enhancement. Originality/value The present study is believed to be an interesting and original contribution on nanofluid thermal behaviours.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".