Effect of Heat Transfer on the Growing Bubble with the Nanoparticles/Water Nanofluids in Turbulent Flow
Bibliographic record
Abstract
This paper is devoted to study the effect of heat transfer on the temperature distribution in a superheated liquid during the growth of vapour bubbles immersed in different types of nanoparticles/water nanofluids between two-phase turbulent flow. The mathematical model is formulated and solved analytically depending on Scriven's theory and using the modification of the method of the similarity parameters between two finite boundaries. The characteristics of vapour bubble growth and temperature distribution are obtained by using the thermo-physical properties of nanoparticles nanofluids. The results indicate that the nanoparticle volume concentration reduces the bubble growth process under the effect of heat transfer. The better agreements are achieved, for bubble dynamics in turbulent nanofluid using the appropriate numerical and theoretical data for the values of concentration rate of nanoparticles χ=0,0.2,0.4. The temperature distribution surrounding the regime of bubble growth in pure water is more intensive than in other cases of Al2O3/H2O, Fe3O4/H2O and CuO/H2O nanofluids in turbulent flow. A Comparison of the current solution with previous works is carried out and discussed.
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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) | 0.001 | 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".