Heat transfer and flow characteristics of nanofluid in a narrow annulus: Numerical study, modelling and optimisation
Bibliographic record
Abstract
Abstract This study attempts to evaluate the flow and heat transfer characteristics of water–Al2O3 nanofluid in a narrow annulus. The effects of volume fraction, the size of particles and the ratio of inner wall heat flux to outer wall heat flux were investigated on the convective heat transfer coefficients and friction coefficients at inner and outer walls of the annulus. Using smaller particles caused a greater heat transfer coefficient. Meanwhile, at higher volume fractions, changing the size of particles led to more considerable changes in the convective heat transfer coefficient and friction coefficient. As per the observation made, the value of heat transfer coefficient at the inner wall was larger than that of the outer wall. In contrast with the results of applying constant properties, changing the volume fraction will change the friction coefficient in the case of using variable properties. Moreover, genetic algorithm was used in combination with compromise programming in order to find the optimum values of the input parameters using neural network correlation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".