Investigation of the combination of TiO<sub>2</sub> nanoparticles and drag reducer polymer effects on the heat transfer and drag characteristics of nanofluids
Bibliographic record
Abstract
Abstract To compensate for drag increment due to the addition of nanoparticles to heat‐transfer fluids, it seems that one could add drag reducer polymeric agents to these fluids. So, in this work, experiments were carried out for solutions of two types of polyacrylamide (FLOPAAM 3330S and FLOPAAM 3630S) at three distinct concentrations (25, 40, and 55 ppm), and TiO2‐water nanofluid at concentrations of 0.015, 0.02, 0.025, and 0.03 L/L. The steady state turbulent convective heat transfer and the friction factor of the suspension of TiO2 in a dilute solution of very high molecular weight, polyacrylamide (hybrid fluid), in a coiled tube were analyzed. Experimental measurements were carried out from a Reynolds number of 11 000 to 21 000 and a constant temperature (24 °C) of the cool bath. The obtained results have shown that for the suspension of nanoparticles, the Nusselt number and drag coefficient increase, whereas for dilute solutions containing only the drag reducer agent, both the Nusselt number and the drag coefficient decrease. However, the combination of the nanoparticle and polymeric drag reducer agent increased the heat transfer coefficient, but decreased the drag coefficient, especially at the highest measured Reynolds number (Re = 21 000).
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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 | 0.000 |
| 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)
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".