The onset of penetrative convection stimulated by internal heating in a magnetic nanofluid saturating a rotating porous medium
Bibliographic record
Abstract
In this work, we investigate the effect of rotation on the onset of penetrative convection stimulated by internal heating in a thin layer of magnetic nanofluid saturating a porous medium. A model that includes the effect of Brownian diffusion, thermophoresis, and magnetophoresis is considered, while the Brinkman model is used for the porous medium. The following three boundary conditions are considered: rigid–rigid, rigid–free, and free–free. We discretized the partial differential equations by applying the Chebyshev pseudospectral method and used the QZ algorithm to solve the resulting eigenvalue problem for water and ester-based magnetic nanofluids. The nature of stability is determined by using the numerical method and is found to be stationary. The results indicate that the onset of convection is advanced with an increase in the Lewis number Le, concentration Rayleigh number Rn, and modified diffusivity ratio NA but the opposite is true in the case with an increase in the width of magnetic nanofluid layer d, Langevin parameter αL, porosity ε, Darcy number Da, modified diffusivity ratio [Formula: see text], and Taylor number TA. Moreover, the parameter NB does not affect the stability of the system significantly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".