NATURAL CONVECTION OF NANOFLUIDS IN A CAVITY INCLUDING THE SORET EFFECT
Bibliographic record
Abstract
Convection of a binary mixture in a cavity is studied numerically. The flow is driven by a buoyancy force due to an externally applied constant temperature difference on the vertical wall of the cavity, while the horizontal surfaces are impermeable and adiabatic. A nanofluid is used and the effects of the cross phenomenon "Soret effect" were considered in the analysis. The flows are found to be dependent on the particle concentration φ, the Rayleigh number RaT, the Lewis number Le, the solutal to thermal buoyancy ratio N, and the thermal boundary conditions. Numerical results for finite amplitude convection, obtained by solving numerically the full governing equations, are found to be in good agreement with the analytical solution based on the scale analysis approach. We have proposed a modified formulation of the conservation equations governing the flow and heat transfer of nanofluids, taking into account important changes of nanofluid thermal conductivity and viscosity as well as the spatial change of the particle concentration that is induced by the Soret effect. Results have shown that such an effect increases nanofluid heat transfer. The optimal particle volume concentration, which maximizes heat transfer, is estimated to be 2%. The increase of natural convection with nanoparticle concentration is weak in comparison to that found in forced convection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".