Flow and Heat Transfer of Nanofluids With Temperature Dependent Properties
Bibliographic record
Abstract
Numerical simulations of laminar convective heat transfer with nanofluids in two different geometries involving a straight pipe and a 90° curved pipe are presented. The Navier-Stokes and energy equations for an incompressible Newtonian fluid are solved in a body fitted coordinate system using a control-volume method. In the present work, the nanofluid is a mixture of water and alumina particles, and its thermophysical properties are considered as a function of temperature as well as particle concentration. The accuracy of the models employed for estimating the effective thermophysical properties of this nanofluid are first evaluated using available experimental data for heat transfer in a straight pipe. The same models are then employed for the simulation of flows in a curved pipe. Present results indicate that both the nanoparticle and curvature effects enhance the heat transfer performance but at the expense of increased pressure drop. However, in the present case, the nanoparticle contribution to the pressure drop is dominant, which increases by up to two orders of magnitude at higher nanoparticle concentrations. The ratio of the nanofluid Prandtl number to the based fluid Prandtl number is established as a criterion for the choice of a nanofluid. This ratio must be less than 1 to achieve higher heat transfer rates with relatively low pressure drops as the particle concentration is increased.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".