Computational investigation for silica-molybdenum disulfide/water-based hybrid nanofluid over an exponential stretching sheet with spectral quasi-linearization method
Bibliographic record
Abstract
The current research is focused on analyzing gyrotactic microorganisms within a hybridized nanofluid (NF) model by considering magnetohydrodynamics, electroosmosis, and radiation effects. Demarcated flow is mathematically modeled to yield coupled nonlinear partial differential equations, which are consequently transmuted into ordinary differential equations (ODEs) by adopting similarity transformations. The spectral quasilinearization method is used to generate the solutions of the transformed ODEs via MATLAB. The influence of various flow parameters on both mono NF and hybrid NF phases for velocity, thermal, concentration, and density of motile microorganism is depicted using graphs. The convergence and residual errors are demonstrated in tables for various influenced parameters on hybrid NF. Additionally, interested physical quantities like shear stress and rate of thermal diffusion at the wall have been tabulated by varying the controlling parameters. It is concluded from the current analysis that the higher velocities and temperatures are observed in hybrid NF model as compared with the mono NF model. Biot number and Hartmann number enhance the temperature profile. The velocity is an enhancing function of mixed convection parameter and bioconvection Rayleigh constant. NF flow over a stretching sheet finds applications in enhancing heat transfer for efficient cooling systems, such as electronics and solar collectors, as well as improving drug delivery in biomedicine, nanoparticle synthesis, and chemical processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".