Flow and heat transfer of Poly Dispersed SiO2 Nanoparticles in Aqueous Glycerol in a Horizontal pipe: Application of ensemble and evolutionary machine learning for model-prediction - K. V. Sharma; M.T. Naik; Praveen Kumar Kanti; D.M. Reddy Prasad; Prabhu Paramasivam; Abinet Gosaye Ayanie (PLoS ONE, 2025)
Stable nanofluid dispersion with SiO2 particles of 15, 50, and 100 nm is generated in a base liquid composed of water and glycerol in a 7:3 ratio and tested for physical characteristics in the temperature range of 20-100oC. The nanofluid showed excellent stability for over a month. Experiments are undertaken for the flow of nanoﬂuid in a copper pipe and measured for their heat transfer coefficient and ﬂow behavior. The convection heat transfer coefficient increases with the flow Reynolds number in the transition-turbulent flow regime. The experimental results further reveal that the friction factor enhancement with 0.5% concentration has increased by 6% as compared to the base liquid. It was employed for prognostic model development using XGBoost and multi-gene genetic programming (MGGP) to model and predict the complex and nonlinear data acquired during experiments. Both techniques provided robust predictions, as witnessed by the statistical evaluation. The R2 statistics of the XGBoost-based model was 0.9899 throughout the model test, while it was lowered to 0.9455 for the MGGP-based model. However, the change was insignificant. The mean squared value was 8.37 for XGBoost, while it increased in the MGGP model to 45.12. Similarly, the mean absolute error (MAE) value was higher (6.623) in the case of MGGP than in XGBoost at 2.733. The statistical evaluation, Taylor diagrams, and violin plots helped determine that XGBoost was superior to MGGP in the present work.