HEAT TRANSFER ENHANCEMENT IN A RADIAL FLOW COOLING SYSTEM USING NANOFLUIDS
Bibliographic record
Abstract
A numerical investigation on the possible heat transfer enhancement capabilities of coolants with suspended metallic nanoparticles (in this case Al2O3 nanoparticles dispersed in water or ethylene glycol) inside radial flow cooling systems is presented in this paper. The laminar forced convection flow of these nanofluids between two coaxial and parallel disks with central axial injection has been considered. Results clearly indicate that considerable heat transfer benefits are possible with the use of either of these fluid/solid particle mixtures. For a Water/Al2O3 nanofluid with a volume fraction of nanoparticles of 7.5%, a 45% increase in the average wall heat transfer coefficient is found for a same Reynolds number. In the case of an Ethylene Glycol/Al2O3 nanofluid, the average wall heat transfer coefficient has a 70% increase for a volume fraction of 7.5%. In general, it was found that the local Nusselt number increases with the particle volume fraction and Reynolds number and decreases with an increase in channel height (distance separating the disks). Local heat transfer also changes noticeably with the behaviour of the hydrodynamic field (i.e. flow separation areas). Although considerable increases in heat transfer capabilities are found, associated increases in wall shear stresses are also noticed. Approximately two fold increases in wall shear stress are found in the case of a water/Al2O3 nanofluid with a particle volume fraction of 5%.
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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) | 0.000 | 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".