Intelligent computing Levenberg Marquardt approach for entropy optimized single-phase comparative study of second grade nanofluidic system

In the presented research study, the novel intelligence based numerical computation by artificial neural networks backpropagated with Levenberg Marquardt method (ANN-BLMM) has been designed for the comparative study of second grade nanofluid flow model (SG-NFM) and examined the effects of parameters of interest associated with the proposed fluid flow system on its velocity and temperature profiles. The designed SG-NFM initially represented by system of PDEs which can be converted into system of non-linear ODEs through the subsequent corresponding transformation. The reference dataset for the SG-NFM is obtained by state of the art Adams numerical method in Mathematica Software for the different scenarios of SG-NFM by variation of unsteadiness parameter, parameters of velocity slip, Biot number, porous media parameter, relaxation time, thermal radiation, volume fraction of nanoparticles and suction/injection. The approximated solutions are interpreted for designed SG-NFM by testing, training and validation process of ANN-BLMM. Moreover, the comparative studies and performance analysis of ANN-BLMM are validated through regression analysis, histogram studies, correlation index and results of MSE. Furthermore, the effects on skin friction, Nusselt and number entropy generation are also analyzed.

Automated summary

In this study, a novel intelligence based numerical computation method, ANN-BLMM, was designed to compare the second grade nanofluid flow model (SG-NFM) and examine the effects of various parameters on its velocity and temperature profiles.