Analysis of entropy generation and natural convection in an inclined partially porous layered cavity filled with a nanofluid
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Bibliographic record
Abstract
The present numerical study investigates the analysis of thermodynamic irreversibility generation and the natural convection in inclined partially porous layered cavity filled with a Cu–water nanofluid. The finite difference method with up-wind scheme is used to solve the governing equations. The study is achieved by examining the effects of nanoparticle volume fraction, inclination angle, and the porous layer thickness. Besides, the computations are achieved within the laminar range of the Rayleigh number. The results show that at Ra = 10 4 , a reduction of total entropy generation is recorded with increasing nanoparticle volume fraction when the porous layer thickness is greater than 0.2. Moreover, when Ra is less than 10 5 , the nanoparticle volume fraction increases the heat transfer irreversibility, and improves the overall thermal performance. It is found also that for a low Rayleigh number, the largest porous layer thickness and the highest cavity orientation improve the thermal performance. On the contrary, at high Rayleigh numbers, these parameter ranges give the worst thermal performance.
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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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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