Analysis of natural convection in a nanofluid‐filled triangular enclosure induced by cold and hot sources on the walls using stabilised MLPG method
Bibliographic record
Abstract
Abstract A stabilised meshless local Petrov–Galerkin (MLPG) method with unity as the test function is extended to simulate the buoyancy‐driven fluid flow and heat transfer in a right‐angled, triangular enclosure filled with a nanofluid composed of a mixture of Al2O3 spherical nanoparticles in water. A cold source with a constant temperature Tc and a hot source with a constant temperature Th are placed along the left and bottom walls of the cavity, respectively, with a differential temperature difference between Tc and Th so that Th > Tc. The simulations performed in this study are based on the stream function–vorticity formulation. The moving least‐squares interpolations of the field variables are employed in these MLPG numerical calculations. A streamline upwind technique is employed to obtain stable solutions for high Rayleigh numbers. A parametric study is performed, and the effects of the Rayleigh number, the locations of the cold and hot sources on the respective cavity walls, and the volume fraction of the nanoparticles on the fluid flow and heat transfer inside the cavity are investigated. The results show that the average Nusselt number is generally an increasing function of the volume fraction of the nanoparticles. Moreover, it is concluded from the results that the locations of the cold and hot sources on the respective cavity walls have a significant effect on the flow and temperature fields inside the enclosure. In general, the maximum average Nusselt number occurs when the centres of the cold and hot sources are at Ys = 0.167 and Xs = 0.167, respectively, while the average Nusselt number is minimum for Ys = 0.833 and Xs = 0.833.
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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 | 0.001 | 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".