CFD Investigation of Flow and Heat Transfer of Nanofluids in Isoflux Spirally Fluted Tubes
Bibliographic record
Abstract
Abstract In this work, the problem of flow and heat transfer of nanofluids in spirally fluted tubes is investigated numerically using the CFD code Fluent. The tube investigated in this work is characterized by the existence of helical ridging which is usually obtained by embossing a smooth tube. A tube of diameter of 15 mm, 1.5 mm groove depth and a single helix with pitch of 64 mm is chosen for simulation. This geometry has been chosen for simulation because it has been investigated experimentally for pure fluids and would, therefore, provide a verification framework with our CFD model. The result of our CFD investigation compares very well with the experimental work conducted on this tube geometry. Interesting patterns are highlighted and investigated including the existence of flow swirl as a result of the existence of the spirally enhanced ridges. This swirl flow enhances heat transfer characteristics of this system as reported in the literatures. This study also shows that further enhancement is achieved if small amount of nanoparticles are introduced to the fluid. These nanoparticles (metallic-based nanoparticles) when introduced to the fluid enhances its heat transfer characteristics.
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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 | 0.001 | 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".