Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant

Abstract

This study focuses on enhancing heat and mass transfer in an electronic cooling system such as a rectangular cavity that contains equidistant heated chips along the bottom wall. The cavity of the present study is filled with ethylene glycol (30:70) based hybrid nano coolants of different volume fractions (ϕ) of Multi-walled Carbon Nanotube (MWCNT), Aluminum Oxide (Al2O3), and Copper Oxide (CuO). The set of equations controlling the thermo-solutal natural convection within the enclosure is simulated using the Galerkin weighted residual finite element method (FEM). The study has shown a good agreement of numerical results with different experimental reports within the framework of the present study. A solution space is constructed based on governing parameters such as Rayleigh number, buoyancy ratio, and Lewis number. A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. The ANN model with 6-50-100-50-2 architecture showcases the best fit with the mean squared error of 0.8923 and an R² value of 99.96 % on testing data. The ANN model exhibits its capability to predict heat transfer and mass transfer rates within the error ranges from 1–2 % and 2–3 %, respectively, even at a strong thermal buoyancy force (Ra = 10⁵). This accuracy showcases a novel use of ANN to efficiently predict thermo-fluidic transport behaviors of hybrid nanofluids, offering a faster alternative to resource-intensive simulations. The present study opens new possibilities for real-time, cost-effective cooling solutions, particularly in microelectronics and renewable energy, while maintaining high prediction accuracy by integrating machine learning with the nanotechnology approach.

Keywords

• Heated chips
• Thermo-solutal convection
• Artificial neural network (ANN)
• Hybrid nano-coolant
• Regressor model