Heat Transfer Enhancement through Thermal Dispersion in Hybrid Nanofluid Saturated Mixed Convection along a Horizontal Cone
Bibliographic record
Abstract
This study explores the influence of thermal dispersion on mixed convection flow of a hybrid nanofluid past a horizontal cone.The working fluid is ethylene glycol containing cylindrical alumina (Al₂O₃) and silica (SiO₂) nanoparticles in equal volume fractions.Compared with a single alumina-based nanofluid, the hybrid suspension exhibits significantly improved thermal transport capability.To analyze the problem, the governing nonlinear partial differential equations are reduced to ordinary differential equations using similarity transformations, and the resulting system is solved numerically with the Bvp4c method.The investigation shows that suction strongly enhances the heat transfer rate by reducing the thickness of the thermal boundary layer, while injection diminishes it, particularly under forced convection conditions.Thermal dispersion is found to decrease heat transfer efficiency by weakening the near-wall temperature gradient, with its impact being more pronounced in forced and mixed convection regions.In contrast, a higher Biot number consistently increases heat transfer, with stronger effects observed as the flow approaches free convection dominance.Overall, the results demonstrate that hybrid nanofluids, when coupled with optimized boundary conditions, can deliver substantial improvements in convective heat transfer performance.These findings underscore the potential application of such fluids in advanced cooling systems, heat exchangers, and energy-related technologies where efficient thermal regulation is critical.
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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".