Heat transfer investigation of laminar developing flow of nanofluids in a microchannel based on Eulerian–Lagrangian approach
Bibliographic record
Abstract
In this article, laminar forced convection of nanofluids in a parallel plate microchannel under constant wall temperature is numerically investigated. A Eulerian–Lagrangian two‐phase method is employed to simulate the flow and heat transfer of nanofluid in the microchannel. Navier–Stokes equations were solved using a finite difference method based on the projection algorithm while a Runge–Kutta method have been used to solve Lagrangian equations of the particle phase. A parallel code is developed on a cluster of processors which indicates a good performance to solve an Eulerian–Lagrangian problem. The convective heat transfer coefficient of nanofluids is better than the base fluid particularly in the entrance region. The results based on two phase modelling, show a slightly greater improvement in the heat transfer coefficient in comparison to the homogeneous single‐phase nanofluid method. The obtained results show that the heat transfer enhancement increases as the nanoparticles volume fraction increases, and decreases with the Reynolds number for Cu–water nanofluid, while the alumina–water nanofluid have a different behaviour. A comparison of two different nanofluids showed the importance considering all of the nanofluid's properties not just thermal conductivity.
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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 | 0.000 |
| 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) | 0.001 | 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".