Thermal Dispersion Impact on Hybrid Nanofluid Saturated Mixed Convection over Horizontal Cone
Bibliographic record
Abstract
This study investigates the effects of thermal dispersion on mixed convection flow of a hybrid nanofluid over a horizontal cone, where the base fluid is ethylene glycol and the suspended nanoparticles are cylindrical-shaped alumina (Al₂O₃) and silica (SiO₂).The hybrid nanofluid, composed of equal volume fractions of alumina and silica, demonstrates markedly enhanced thermal performance compared to the mono nanofluid containing alumina alone.The governing nonlinear partial differential equations are transformed into a system of ordinary differential equations using similarity transformations and are solved numerically via the Bvp4c method.The results reveal that suction significantly enhances the heat transfer rate by thinning the thermal boundary layer, whereas injection has a diminishing effect, particularly in the forced convection regime.Thermal dispersion tends to reduce heat transfer by weakening the temperature gradient near the surface, with stronger effects observed in forced and mixed convection regions.Moreover, an increase in the Biot number consistently enhances heat transfer, especially as the flow shifts toward free convection dominance.These findings highlight the potential of hybrid nanofluids for advanced thermal management applications, including cooling technologies, heat exchangers, and energy systems where efficient convective heat transfer is essential.
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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 | 0.000 |
| 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) | 0.000 | 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 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".