Developing laminar mixed convection of nanofluids in an inclined tube with uniform wall heat flux
Bibliographic record
Abstract
Purpose The aim is to study the conjugate problem of developing laminar mixed convection flow and heat transfer of water‐Al2O3 nanofluid inside an inclined tube submitted to a uniform wall heat flux. Design/methodology/approach The set of non‐linear, coupled and fully elliptic governing equations has been solved using a “finite‐control‐volume” numerical method, the classical power‐law scheme for computing heat and momentum fluxes staggered and non uniform grids for spatial discretization of various regions of the tube. Findings Numerical results have shown that the presence of nanoparticles slightly intensifies the secondary flow due to buoyancy, in particular in the developing region. It also increases the average Nusselt number and decreases slightly the product ReCf with respect to those of water. For the horizontal inclination, two new correlations have been proposed to calculate these two variables in the fully developed region, for Grashof number ranging from 103 to 105 and particle volume concentrations up to 7 per cent. Practical implications The results of this study can be employed for various practical heat transfer and thermal applications using nanofluids. Originality/value The present study constitutes an original contribution to the knowledge of nanofluid thermal behaviour.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".