Analysis of unsteady mixed convection of Cu–water nanofluid in an oscillatory, lid-driven enclosure using lattice Boltzmann method
Bibliographic record
Abstract
Abstract The unsteady physics of laminar mixed convection in a lid-driven enclosure filled with Cu–water nanofluid is numerically investigated. The top wall moves with constant velocity or with a temporally sinusoidal function, while the other walls are fixed. The horizontal top and bottom walls are, respectively, held at the low and high temperatures, and the vertical walls are assumed to be adiabatic. The governing equations along with the boundary conditions are solved through D2Q9 fluid flow and D2Q5 thermal lattice Boltzmann network. The effects of Richardson number and volume fractions of nanoparticles on the fluid flow and heat transfer are investigated. For the first time in the literature, the current study considers the mechanical power required for moving the top wall of the enclosure under various conditions. This reveals that the power demand increases if the enclosure is filled with a nanofluid in comparison with that with a pure fluid. Keeping a constant heat transfer rate, the required power diminishes by implementing a temporally sinusoidal velocity on the top wall rather than a constant velocity. Reducing frequency of the wall oscillation leads to heat transfer enhancement. Similarly, dropping Richardson number and raising the volume fraction of the nanoparticles enhance the heat transfer rate. Through these analyses, the present study provides a physical insight into the less investigated problem of unsteady mixed convection in enclosures with oscillatory walls.
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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 | 0.001 | 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".