Study of Al<sub>2</sub>O<sub>3</sub>/copper–water nanoparticle shape, slip effects, and heat transfer on steady physiological delivery of MHD hybrid nanofluid
Bibliographic record
Abstract
This paper contains the analytical investigation of magnetohydrodynamic (MHD) flow of copper/Al 2 O 3 –water hybrid nanofluid with unstable peristaltic motion. Three different geometries (bricks, cylinder, and platelets) along with velocity and thermal slip conditions are studied in detail to reach the precise solution. Flow geometry of a non-uniform tube of finite length, experimental values of base fluid, and considered nanoparticles are taken into account to examine the theoretical investigation of formulated equations. Dimensionless control equations, which are subject to physically realistic boundary conditions, are closely studied to obtain precise results. The shape effects of nanoparticles on velocity, temperature distribution, and heat transfer on the length of the non-uniform tube with variation of the various flow parameters are discussed in a graphical description to understand the theoretical aspects to validate the medical analysis. The observations from the analysis state that copper/Al 2 O 3 –water carry maximum velocity for smaller values of slip parameter. Temperature distributions for heat absorption parameter are more significant as fluid flow accelerates when large values are chosen. Large values of thermal slip parameter yield enhancement in pressure gradient and Cu–water nanofluid has higher impact than hybrid nanofluid. Platelet-shaped nanoparticles of hybrid nanofluid have more significant effect on pressure gradient than cylinder- and brick-shaped nanoparticles of Cu–water nanofluid. An intrinsic property of peristaltic transport (i.e., trapping) is also discussed. The trapped bolus decreases for platelets and cylinder-shaped nanoparticles, whereas, the size of the trapped bolus increases for brick-shaped nanoparticles. This model is applicable to a drug delivery system and to design the micro-peristaltic pump for transporting nanofluids.
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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) | 0.001 | 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) | 0.000 | 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".