Thermal Enhancement in the Ternary Hybrid Nanofluid (SiO₂+Cu+MoS₂/H₂O) Symmetric Flow Past a Nonlinear Stretching Surface: A Hybrid Cuckoo Search-Based Artificial Neural Network Approach

Abstract

In this article, we considered a 3D symmetric flow of a ternary hybrid nanofluid flow (THNF) past a nonlinear stretching surface. The effect of the thermal radiation is considered. The THNF nanofluid SiO2+Cu+MoS2/H2O is considered in this work, where the shapes of the particles are assumed as blade, flatlet, and cylindrical. The problem is formulated into a mathematical model. The modeled equations are then reduced into a simpler form with the help of suitable transformations. The modeled problem is then tackled with a new machine learning approach known as a hybrid cuckoo search-based artificial neural network (HCS-ANN). The results are presented in the form of figures and tables for various parameters. The impact of the volume fraction coefficients ϕ1, ϕ2, and ϕ3, and the radiation parameter is displayed through graphs and tables. The higher numbers of the radiation parameter (Rd) and the cylinder-shaped nanoparticles, ϕ3, enhance the thermal profile. In each case, the residual error, error histogram, and fitness function for the optimization problem are presented. The results of the HCS-ANN are validated through mean square error and statistical graphs in the last section, where the accuracy of our implemented technique is proved.

Keywords

• heat transfer
• hybrid nanofluid
• regression
• nonlinear problems
• artificial neural network
• optimization problems