Effect of heated wall position on heat transfer and entropy generation of Cu–water nanofluid flow in an open cavity
Bibliographic record
Abstract
This paper reports the numerical results of the mixed convection and entropy generation of Cu–water nanofluid flow in an open cavity heated from different sides with non-uniform temperature distribution. The finite volume method is used to solve the governing equations. The analysis is carried out by a range of Richardson numbers, 0.01 ≤ Ri ≤ 10, at a nanoparticle volume fraction of 0 ≤ [Formula: see text] ≤ 0.1, and Reynolds number Re = 200, with a cavity aspect ratio of L/H = 2. Three heating modes are considered: (A) the left wall is heated (inflow side, assisting flow); (B) the horizontal bottom wall is heated; and (C) the right wall is heated (outflow side, opposing flow). The results show that the heat transfer and the entropy generation increase with increasing Richardson number and nanoparticle volume fraction. The highest heat transfer and entropy generation are obtained with heating mode C (opposing flow). The contribution of heat transfer and fluid friction irreversibilities in the entropy generation depends on Richardson number and the heater position. The present investigation shows that the configuration with non-isothermal heater located at the bottom wall (B) has the highest performance in terms of heat transfer enhancement with minimum entropy generation.
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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 | 0.000 | 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".