Statistical thermal study of ternary hybrid nanofluid flow in coaxial cylinder: artificial neural network approach

Abstract

The objective of this study is to examine heat and mass transfer aspects of ternary nanofluid flow in coaxial cylinder under the influence of Arrhenius activation energy, microorganisms’ concentration and bioconvection Peclet number, which a pivotal rolet in various scientific and engineering applications. The flow of ternary nanofluid is caused due to stretching inner cylinder with stationary outer cylinder. The nonlinear partial equations are derived for the flow model and reduced to non-linear ordinary differential equation by applying suitable similarity transformation. The resultant equations are resolved mathematically using Runge Kutta Fehlberg (RKF45) technique. The obtained numerical results are validated with the published work to check the exactness of the solution methodology and it is noticed that the present outcomes are on par with published work. The physical behaviour of the pertinent parameters is analysed through graphical depiction. The derived quantities like drag force and Sherwood number are studied through tabular column. Additionally, the heat transfer rate is analysed by using backpropagated Levenberg-Marquardt Machine learning algorithm. Further, the correlation between the parameter on the rate of heat transfer is analysed by using Mean square error and regression graphs. The key outcome of this research is that, the temperature upsurges by increasing the solid volume of nanoparticle due to higher thermal conductivity of the nanoparticles. Further, it is perceived from the artificial neural network model that, the correlation between the input parameters and output data are strongly correlated (R = 1).