LAMINAR FORCED FLOW AND HEAT TRANSFER ENHANCEMENT BY USING WATER-BASED NANOFLUIDS IN A MICROCHANNEL
Bibliographic record
Abstract
In this work, the problem of laminar forced convection flow and heat transfer of nanofluids inside a uniformly heated rectangular-cross-section microchannel was numerically investigated, using the classical assumption of homogeneous-single-phase fluid and a full 3D mathematical model. For the three water-based nanofluids considered, namely water-Al2O3 with 36nm and 47 nm particle sizes and water-CuO with 29nm particle-size, available experimental data for nanofluids thermal conductivity and dynamic viscosity were employed. Numerical results obtained for the range: Reynolds number from 200 to 1500, Prandtl number varying from 7 to 32 and particle volume fraction from 0 to 9%, have eloquently shown that the inclusion of nanoparticles into the base fluid produces a considerable increase of the heat transfer coefficient, which, in turn, has reduced fluid and channel wall temperatures. Such an enhancement clearly becomes more pronounced with an increase of particle volume fraction and/or the mass flow rate. Results for the averaged heat transfer coefficient and Nusselt number as function of the flow Reynolds number and particle volume fraction are shown and discussed. The influence of the nanoparticle size and nanofluid type on the heat transfer enhancement is also shown.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".