Heat transfer enhancement in turbulent tube flow using Al2O3 nanoparticle suspension
Bibliographic record
Abstract
Purpose To study the hydrodynamic and thermal behaviors of a turbulent flow of nanofluids, which are composed of saturated water and Al2O3 nanoparticles at various concentrations, flowing inside a tube submitted to a uniform wall heat flux boundary condition. Design/methodology/approach A numerical method based on the “control-volume” approach was used to solve the system of non-linear and coupled governing equations. The classical κ-ε model was employed in order to model the turbulence, together with staggered non-uniform grid system. The computer model, satisfactorily validated, was used to perform an extended parametric study covering wide ranges of the governing parameters. Information regarding the hydrodynamic and thermal behaviors of nanofluid flow are presented. Findings Numerical results show that the inclusion of nanoparticles into the base fluid has produced an augmentation of the heat transfer coefficient, which has been found to increase appreciably with an increase of particles volume concentration. Such beneficial effect appears to be more pronounced for flows with moderate to high Reynolds number. In reverse, the presence of nanoparticles has induced a rather drastic effect on the wall shear stress that has also been found to increase with the particle loading. A new correlation, Nufd=0.085 Re0.71 Pr0.35, is proposed to calculate the fully-developed heat transfer coefficient for the nanofluid considered. Practical implications This study has provided an interesting insight into the nanofluid thermal behaviors in the context of a confined tube flow. The results found can be easily exploited for various practical heat transfer and thermal applications. Originality/value The present study is believed to be an interesting and original contribution to the knowledge of the nanofluid thermal behaviors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".