Bibliographic record
Abstract
Given that the use of nano-fluids has increased in heat exchangers and since the flow regime in heat exchangers is often turbulent, justifying the effectiveness of using nano-fluids requires studying the nanofluids turbulent flow.Analysis of nano-fluids steady flow, containing water-based fluid and aluminum oxide nanoparticles AR, AF and AK, has performed in developing and fully developed turbulent flow, in the pipe with a diameter of 150 mm and a length of 30 m by Gambit and Fluent software.After examining the independence of numerical results from the network, the results of numerical modeling were compared with the experimental results and given the consistency of numerical results with existing relationships, created model was used to study the nano-fluids flow.In this study, the impact of the type of nanoparticles on the parameters of nano-fluids flow in turbulent flow regime has been thoroughly investigated.Of the three aluminum oxide nanoparticles of AR, AF and AK, the nano-fluid, containing the aluminum oxide nanoparticles of AF, has the greatest coefficient of friction, pipe wall shear stress, the viscous drag force and pressure drop and nano-fluids, containing aluminum oxide nanoparticles AR, has the lowest ones.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.001 |
| Insufficient payload (model declined to judge) | 0.920 | 0.894 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".