The Effects of Thermophoresis and Brownian Motion on the Flow of a Nanofluid in Rectangular Channels Using Buongiorno’s Model
Bibliographic record
Abstract
Nanofluids is an emerging sector of working fluids that promise to enhance heat transfer by increasing fluids’ thermal conductivity by inputting metal nanoparticles into a base fluid. There are two ways of modelling nanofluids; single-phase and two-phase. Since nanoparticle sedimentation may influence heat extraction, the Buongiorno two-phase model will be of focus to understand Brownian motion as well as thermophoretic effects on nanofluids. This research presents the case of investigating the thermal performance of plain water, as well as Al 2 -3ater and TiO -2ater nanofluids with varying concentrations and flow rates in rectangular channelled configurations. The results revealed that the Nusselt number and thermal efficiency index augmented by increasing the Reynolds number and nanoparticle concentrations. Furthermore, higher Reynolds numbers and nanoparticle concentrations increased homogeneity in nanoparticle distributions. Lastly, 2 and 3-channel configurations were utilized in the study, the 2-channel model yielded symmetrical flow allowing uniform nanoparticle distributions, leading to higher thermal efficiencies.
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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 | 0.001 |
| 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".