Effects of thermodiffusion on Darcy–Brinkman convection in a rotating binary nanofluid saturating a porous layer
Bibliographic record
Abstract
In this study, the onset of convection in a rotating horizontal porous layer saturated by a binary nanofluid was theoretically investigated under the influence of thermodiffusion and nanoparticles. To study the onset of convection, the Darcy–Brinkman model was employed for the porous medium, and the thermal Rayleigh number was derived analytically using linear stability analysis. Water–ammonia-based silver, copper, and alumina, three binary nanofluids, were considered, and the effect of nanoparticles on the stability of the system was analysed using addition factor analysis. The Brinkman model for viscosity and Bruggeman model under the mean field approach for thermal conductivity were used to study the impact of nanoparticles. Heat transport was examined by calculating the heat transfer coefficient. The effects of nondimensional parameters involved in binary nanofluids on the onset of convective instabilities are depicted pictorially. The dependence of the size of the convection cells on nondimensional parameters is also discussed. The Darcy–Taylor number decreases the size of convection cells, whereas the Darcy number increases the size of convection cells. The size of the convection cells is independent of the porosity of the medium, Lewis number, volume fraction of nanoparticles, Soret effect of nanoparticles, and the solute in binary nanofluids. Furthermore, the Darcy–Taylor number and porosity have stabilising effects, whereas the Darcy number, volume fraction of nanoparticles, and Lewis number have destabilising effects on the system. Soret effects of nanoparticles and solutes exhibit dual consequences on stability. They stabilise the binary nanofluid layer if δ4 < –1 and destabilise when δ4 > –1. Their effect is immaterial if δ4 = –1.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".