Double diffusive convection and thermodiffusion of fullerene–toluene nanofluid in a porous cavity
Bibliographic record
Abstract
Abstract The full Brinkman equation coupled with the heat and mass transfer equations was solved numerically using the finite element technique. A square cavity filled with hydrocarbon nanofluid of fullerene–toluene with different concentration values of fullerene was subject to various heating conditions. Results have confirmed that in the presence of nanofluid a heat transfer enhancement is present until a certain amount of initial concentration of the nanofluids. The heat convection coefficient was found to be 16% higher when nanofluid is used as the wetting fluid. In addition, it was determined that the concentration of fullerene in toluene has its limitation in heat removal enhancement. In fact, beyond 5% of fullerene, there is no noticeable enhancement of the heat removal in the system. The model was also used to study thermodiffusion effects in the cavity. Despite the small value and negligible effects of thermodiffusion in general heat and mass transfer problems, which is <5% in the case of studying nanofluids, a maximum value of 20% variation of fullerene concentration has been detected. Moreover, fullerene separation was investigated for different heating intensities. As the Rayleigh number increases, the mixing was found to reduce the separation, which diminishes the Soret effect in the system.
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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 | 0.000 |
| 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) | 0.000 | 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".