Mechanistic investigation for shape factor analysis of SiO<sub>2</sub>/MoS<sub>2</sub> – ethylene glycol inside a vertical channel influenced by oscillatory temperature gradient
Bibliographic record
Abstract
The present article aims to examine shape factor effects of SiO2/MoS2 hybrid nanoparticles suspended in ethylene glycol (EG) confined in a vertical rotating channel under the combined influence of mixed convection, thermal radiation, magnetohydodynamics, and periodic temperature. This study provides exact closed-form solutions for velocity and temperature distributions. Mathematical investigation is carried out by formulating the physical problem in Cartesian coordinates. Effect of significant emerging parameters is displayed and examined through graphs. It is concluded that the magnitude of velocity is higher in the case of small rotations than it is in the case of large rotations. It is noted that velocities upsurge for increasing values of the pressure gradient. The simple fluid has the lowest temperature distribution and the temperature is an increasing function of [Formula: see text]. Hybrid nanofluid having blade-like nanoparticles has a high temperature profile. Moreover, it is observed that temperature distribution is higher for SiO2/MoS2–EG hybrid nanofluid than for MoS2–EG nanofluid. Skin friction phase angle is a decreasing function of Ω, Gr, Re, and N while it is an increasing function of M and A. Magnitude of skin friction decreases with an increase in Ω, Re, M, N, and favorable pressure gradient; however, it increases with an increase in Gr. Nusselt number phase angle is an increasing function of N and [Formula: see text] for SiO2/MoS2–EG hybrid nanofluid. Nusselt number amplitude is a decreasing function of N but it has an increasing trend for rising values of [Formula: see text].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".