Tri-hybrid nanofluids for thermal applications: stability, magneto-hydrodynamics, and machine learning prediction

Abstract

This study presents a comprehensive experimental and analytical investigation into the thermophysical and magneto-hydrodynamic (MHD) properties of //MWCNT/DIW tri-hybrid nanofluids (THNFs) across varying nanoparticle ratios with Sample A (15 wt.%, 80 wt.%, 5 wt.% MWCNT), Sample B (20 wt.%, 70 wt.%, 10 wt.% MWCNT), Sample C (20 wt.%, 60 wt.%, 20 wt.% MWCNT), Sample D (25 wt.%, 50 wt.%, 25 wt.% MWCNT), and Sample E (33.33 wt.% of each material). The effects of temperature (10–50 °C) on viscosity, thermal conductivity (TC), electrical conductivity (EC), stability, and sedimentation were analysed for advanced thermal management applications. Results indicate that the hybridization ratios markedly influence THNF properties. Higher content enhances stability by reducing particle agglomeration, while increased and MWCNT fractions elevate EC, however, excessive MWCNT raises viscosity, potentially impacting pumping efficiency. Notably, Sample E offers an optimal balance of TC, stability, and viscosity at lower concentrations. pH measurements reveal an acidic trend that decreases with rising temperature and volume fraction, potentially leading to corrosion in metallic systems. Strategies such as surfactant addition and surface functionalization are proposed to mitigate these effects. Moreover, machine learning models (Gradient Boosting, Random Forest, LightGBM) identified temperature as the dominant factor influencing TC and viscosity, while nanoparticle volume fraction primarily affected pH and EC, achieving high predictive accuracy (R2 > 0.96, MSE < 0.000025).