Mesoscopic study of miscible nanoflow instabilities
Bibliographic record
Abstract
Nanofluids have recently been introduced as a remedy to control flow instability. The complex behavior of nanoparticles under different hydrodynamic and thermodynamic conditions makes the modeling and predictions of the process complicated, and such an erratic nature entails the carefully scrutinized analysis of hydrodynamic movement and deposition of nanoparticles. In this study, the effects of nanoparticles on instability are examined using the lattice Boltzmann approach. The flow geometry is a porous medium consisting of regularly arranged disks, and the adopted mesoscopic model accounts for heat transfer effects as well as nanoparticle deposition. A new probabilistic model has been proposed for particle deposition to better predict the behavior of nanoparticles. It is shown that nanoparticles behave differently at various viscous regimes and the instability is controlled by physical and chemical properties of the nanoparticles. The study also reveals some interesting behavior of nanoparticles at different sizes and surface potentials which directly affect the instability. Furthermore, thermal induced instabilities show how nanoparticles behave differently at various temperatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".