Particle-resolved simulations for nanofluid thermal enhancement in channel flows
Bibliographic record
Abstract
Nanofluids have been studied extensively for improved heat transfer performances in various thermal systems. In this article, we adopt the particle-resolved method to study the thermal enhancement of nanofluids in channel flows under constant wall temperature (CWT) and constant heat flux (CHF) conditions. The smoothed profile method is employed to describe the nanoparticles suspending in the base fluid and the lattice Boltzmann method is utilized to solve the flow and temperature fields. Unlike the continuum representation of nanofluids, this particle-resolved approach can incorporate the thermophysical properties of the base fluid and nanoparticles directly in simulation, and flow and temperature distributions around nanoparticles are available. Our simulations reveal detailed information on the nanoparticle influence on the wall thermal distributions, and illustrate the enhancement mechanism for heat transfer between the boundary wall and fluid flow: The nanoparticles near the wall increase the local heat flux under CWT and decreases the local wall temperature under CHF condition. The heat transfer coefficient is utilized to characterize the thermal performance for nanofluids, and it increases 6–16% for the nanofluid systems considered in our simulations. The effects of key system parameters, such as the nanoparticle volume fraction, thermal conductivity, and Reynolds number, are also investigated. Our results show that, for both CWT and CHF systems, the particle volume fraction and thermal conductivity can both increase the heat transfer coefficient; however, the particle conductivity effect becomes less significant or even negligible when it is greatly larger than the base fluid conductivity. For the limited range and relatively low values of Reynolds numbers considered in this work, it appears that the Reynolds number has no obvious influence on the system thermal performance. These finding could be beneficial for a better understanding of the complexity of nanofluid systems and for future nanofluid development and applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".