Heat Transfer Enhancement in Laminar Graetz and Taylor Flows Using Nanofluids
Bibliographic record
Abstract
This paper focuses on heat transfer in mini scale tubes under laminar developing flow conditions for a constant wall temperature boundary condition. An experimental study was preformed using Aluminum Oxide nanoparticles (< 50nm) for continuous and segmented fluid streams. A two step method was employed to prepare several samples of aluminum oxide nanofluid with different concentrations 0.25, 0.5 and 1% by volume. Heat transfer enhancement in mini scale tubes (∼1 mm) was assessed using the dimensionless Graetz parameter L*, dimensionless mean wall heat flux q*, and Nusselt number Nu. In this study we investigate the effect of nanofluid concentration on laminar heat transfer enhancement in mini-scale circular tube under continuous and segmented flow using gas as a segmenting medium. The initial results show a maximum of 10–65% enhancement of Nusselt number as compared with pure water under the same conditions as a function of L*. For the upper limit of concentration of 1% Al2O3 nanofluid, the friction factor was found to be less than 5% greater, which means a small sacrifice on pumping power is to be expected. This study provides new insights on the thermal behaviour of nanofluids under laminar developing flow and segmented flow conditions in straight tubes.
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".