Regression Model Featuring Nanofluid in Interconnected Domain: An Innovative Way of Thermal Enhancement

Efficient thermal management is essential in applications ranging from electronics cooling to industrial heat exchangers, where even incremental enhancements in heat transfer can significantly boost reliability and performance. This study presents an innovative approach to enhancing thermal performance by developing a regression model for water (W)-propylene glycol (PG) mixture-based multi-walled carbon nanotube (MWCNT) nanofluid in interconnected oblique triangles aiming to leverage Nanofluids’ higher thermal conductivity for more efficient heat transfer. Using the finite element method (FEM) to solve partial differential equations (PDEs), combined with simulations of computational fluid dynamics (CFD), this research analyzes the buoyancy effects across Rayleigh numbers (10 3 Ra ≤ 10 6 ), solid concentrations (0.1% ≤ φ ≤ 3%) of MWCNT nanoparticles, Prandtl numbers (3.5 ≤ Pr ≤ 150), and various ratios of base fluid W-PG mixtures. The regression model derived from CFD data provides robust predictive capabilities, validated by residual error, ANOVA table, line fit, and normal probability plots, revealing key sensitivities in transfer rates to changes in response variables. Results indicate that the incorporation of a 3% concentrated W-PG-MWCNT nanofluid results in an enhancement in heat transfer by 3.45% for the base fluid mixture ratio of 40:60%, by 9.52% for a ratio of 80:20%, and by 7.75% for a ratio of 60:40%. Also, the W-PG-MWCNT nanofluid composition achieves an 11.11% increase in heat transport compared to W-PG alone, with the highest transfer rate observed at an 80:20% mixture ratio. The regression equation for the mean Nusselt number has about a 95% correlation coefficient, making it an extraordinary new approach to improving thermal performance. This research offers an effective solution for enhancing heat performance, particularly for applications like electronic cooling systems and automotive radiators, where stable thermal management is crucial.