Development of a XGBJSO hybrid machine learning model for predicting heat transfer coefficient of water and Al ₂ O ₃ nanofluid in flow boiling

A flow boiling examination of water along with a nanofluid composed of water-Al 2 O 3 flowing through an annulus has been carried out. The examination was carried out for the mass flux, pressure, and heat flux. The volumetric concentration of Al 2 O 3 -water nanofluid was constant at 0.10 %. Experimental results were validated with Chen correlation which showed good agreement. Results demonstrated that the heat transfer coefficient of both water-Al 2 O 3 nanofluid and water enhanced with the enhancement in pressure, heat flux, and mass flux. Water-Al 2 O 3 nanofluid showed better performance than water and the best heat transfer coefficient was observed for water-Al 2 O 3 nanofluid at pressure 1.5 bar, heat flux 144 kWm −2 , and mass flux 1,015 kgm −2 s −1 . A linear heat transfer coefficient correlation was generated for water-Al 2 O 3 nanofluid. This research created the XGBJSO machine learning model (XGBoost with Jelly Fish Search Optimizer), which showed remarkable predicted accuracy for the heat transfer coefficient of Al 2 O 3 nanofluid in water. A high R 2 score of 0.99986 for the training set and 0.98453 for the testing set, showed a significant correlation between predicted and actual values. The XGBJSO framework offers a scalable and adaptable solution for thermal system optimization. This work advances the field of heat transfer prediction and provides a foundation for future research in thermal management.