Two-phase modelling of nanofluid heat transfer in a microchannel heat sink
Bibliographic record
Abstract
The problem of a steady, two-dimensional, laminar forced flow and heat transfer of a nanofluid, Water-Al 2 O 3 mixture, circulating inside a microchannel, 0.1mm thickness by 25mm length, was numerically studied. The nanofluid considered is composed of saturated water and alumina metallic particles with different average diameters, 36nm and 47nm. All fluid properties are assumed temperature-dependent and evaluated using classical two-phase mixtures formulas, while for thermal conductivity and dynamic viscosity, recent in-house experimental data were used. The particle diffusion constant due to Brownian motion was estimated using the Einstein's relation. The fluid exhibits a parabolic axial velocity, uniform temperature and particle concentration profiles at the inlet; the usual non-slip and uniform wall temperature conditions prevail on both walls; at the exit, the 'outflow boundary' condition is imposed. The system of governing equations (conservation of mass, momentum, energy, and species) was successfully solved using a FEM imbedded within a commercial powerful code and a 28558-cell non-uniform grid. Results obtained for the Reynolds range of 200-2500 clearly show the beneficial effects due to the use of nanofluids on the heat transfer coefficient. Results using the two-phase model show that the spatial distribution of particle concentration is highly non uniform; it has been found to vary considerably in the vicinity of the heated walls while remains nearly uniform in a large central region of the channel. The effects due to the particle concentration and size were also studied. A 'single-phase fluid v/s twophase fluid model' comparison has clearly shown a certain discrepancy among the results obtained.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".