Dean flow velocity of shear-thickening SiO2 nanofluids in curved microchannels
Bibliographic record
Abstract
We report the effects of a curvilinear microchannel width, height, and radius of curvature, as well as the kinematic viscosity and axial velocity of shear-thickening nanofluids, on the average Dean velocity (VDe) of the secondary flow in the microchannel. Manipulation of inertial and Dean drag forces in curvilinear microchannels has enabled high-throughput and high-resolution size-based separation of microparticles and cells in various biomedical applications. VDe plays a deterministic role in the estimation of the Dean drag force and the design of these microfluidic devices. Despite the previous numerical and experimental studies on VDe of Newtonian and shear-thinning viscoelastic fluids, VDe of shear-thickening metallic nanofluids, such as SiO2 nanoparticles in water, in curved microchannels is still unknown. Such shear-thickening fluids are being used in thermal microsystem applications and are on the verge of entering the field of inertial microfluidics for particle and cell sorting. Our investigations have shown that VDe of shear-thickening SiO2–water nanofluids scales directly with the channel width and the fluid axial velocity, while being inversely proportional with the SiO2 concentration and the channel radius of curvature. Our non-dimensional analysis has led to the development of an empirical correlation that relates VDe-based Reynolds number of the nanofluid to the Dean number and the normalized kinematic viscosity of the nanofluid. It provides a significant accuracy in estimating VDe of shear-thickening fluids, compared to application of Newtonian or shear-thinning equations in the literature, which could be useful toward future design of particle and cell sorting and washing microdevices.
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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 | 0.001 |
| 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".