Thermal Enhancement in the Ternary Hybrid Nanofluid (SiO 2 +Cu+MoS 2 /H 2 O) Symmetric Flow Past a Nonlinear Stretching Surface: A Hybrid Cuckoo Search-Based Artificial Neural Network Approach.

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 SiO 2 +Cu+MoS 2 /H 2 O 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 ϕ 1 , ϕ 2 , and ϕ 3 , and the radiation parameter is displayed through graphs and tables. The higher numbers of the radiation parameter (R d) and the cylinder-shaped nanoparticles, ϕ 3 , 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. [ABSTRACT FROM AUTHOR]