Natural convection in a triangular cavity filled with a nanofluid-saturated porous medium using three heat equation model
Bibliographic record
Abstract
The present study aims to examine the local thermal non-equilibrium natural convection heat and mass transfer of nanofluids in a triangular enclosure filled with a porous medium. The effect of the presence of nanoparticles and the thermal interaction between phases on the flow, temperature distribution of phases, the concentration distribution of nanoparticles as well as the Nusselt number of phases is theoretically studied. The interaction between the phases of nanoparticles and the base is taken into account by using a three thermal energy equation model while the concentration distribution of nanoparticles is modeled by Buongiorno’s model. A hot flush element is mounted at the vertical wall of the triangle enclosure to provide a constant temperature of T h while the inclined wall is at a constant temperature of T c . A three heat equation model by considering the local thermal non-equilibrium model of nanoparticles, the porous medium, and the base fluid is developed and utilized for natural convection of nanofluids in an enclosure. The drift-flux of nanoparticles due to the nano-scale effects of thermophoresis and Brownian motion effects is addressed. The governing equations are represented in a non-dimensional form and solved by employing the finite element method. The results indicate that the increase of Rayleigh number shows a significant increase in the average Nusselt number for the base fluid phase, a less significant increase in the average Nusselt number for the solid matrix phase, and almost an insignificant effect in the average Nusselt number of the nanoparticle phase. Increasing the buoyancy ratio parameter (the ratio of mass transfer buoyancy forces to the thermal buoyancy forces) tends to reduce and increase the average Nusselt number in fluid and porous phases, respectively. An optimum value of buoyancy ratio parameter for the average Nusselt number of the nanoparticle phase is observed.
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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 | 0.000 | 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 | 0.000 |
| 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 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".