Priyadharshini, P (2025) Thermal and flow analysis of chemically reactive Casson hybrid nanofluids withmachine learning validation. Thermal and flow analysis of chemically reactive Casson hybrid nanofluids withmachine learning validation: 74. pp. 1-23.
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Thermal and flow analysis of chemically reactive Casson hybrid nanofluids with machine learning validation.pdf - Published Version
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Abstract
This research investigates the magnetohydrodynamic flow of a chemically
reactive Casson hybrid nanofluid within a Sodium Alginate base, flowing over a curved
stretching surface in a porous environment. The analysis accounts for internal heat sources,
magnetic field influence, reactive diffusion, and thermophoretic effects to improve thermal
performance. Methodology: The model considers transport effects, including Brownian
motion, thermophoresis, internal heating, viscosity, and Arrhenius-type reactions. Similarity
transformations reduce the governing PDEs to ODEs, which are solved using MATLAB’s
BVP4c. The sensitivity of thermal and flow parameters is further evaluated using Multiple
Linear Regression (MLR). Core findings: Results indicate that elevating the Biot number
can boost the Nusselt number by approximately 42%, emphasizing improved heat transfer
at the surface. The heat generation parameter exerts the strongest effect on thermal output,
with a sensitivity index peaking at 2.8673. Furthermore, the curvature parameter plays a
significant role in modulating surface shear. The sensitivity analysis pinpoints parameter
combinations that yield optimal performance, reinforcing the utility of machine learning in
fluid system optimization. Validation: Comparisons to previous studies demonstrate excellent
agreement, as deviations remain under 1.6% for skin friction and 2.3% for the Nusselt
number when the curvature parameter equals zero. These results affirm the robustness of the
applied transformations and numerical approach. Furthermore, the MLR model perfectly
matches numerical outputs, reaching an R2 score of 1.0, confirming predictive accuracy.
Applications: The findings reference engineering applications, specifically solar thermal
systems, HVAC equipment, and miniaturized heat exchangers. By combining numerical
modeling with machine learning, this study offers a reliable approach for designing and
controlling energy-efficient thermal systems under varying physical conditions.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Casson hybrid nanofluid · Curved surface · Multiple linear regression · Sensitivity analysis |
| Divisions: | PSG College of Arts and Science > Department of Mathematics |
| Depositing User: | Dr. B Sivakumar |
| Date Deposited: | 03 Nov 2025 05:15 |
| Last Modified: | 03 Nov 2025 05:15 |
| URI: | https://ir.psgcas.ac.in/id/eprint/2499 |
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