System-dependent behaviors of nanofluids for heat transfer: a particle-resolved computational study
Bibliographic record
Abstract
Nanofluids, the suspensions of metal or oxide nanoparticles in a fluid, have been extensively studied in the past decades for their potential in thermal enhancement in various heat transfer applications. However, controversial findings exist in the literature, and our knowledge and understanding of nanofluid thermal performances are still limited, considering the complicated microscopic mechanisms involved in nanofluids. In this article, unlike the one-phase or two-phase approaches typically adopted in nanofluid simulations, we present a particle-resolved numerical model with the nanoparticles and ambient fluid flows considered explicitly. Governing equations for the fluid flow, heat transfer, particle dynamics, and flow–particle interaction are solved simultaneously, and thermophysical properties of the nanoparticles and base fluids can be considered directly. Several simulations have been conducted for nanofluids of different particle volume fractions and under different thermal and flow conditions. Our results show that adding particles can improve the conductive heat transfer, benefiting from the high conductivity of the nanoparticle materials. However, the increased suspension viscosity can reduce the convection contribution on heat transfer; and the overall thermal performance could be deteriorated with the presence of nanoparticles. The results and analysis imply that nanofluids might be more appropriate for systems where the conduction part is more dominant over the convection part, such as in microchannels and porous materials. The model can also be extended to include other factors for future nanofluid studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| 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".