Blasius and Sakiadis flow of titania-copper-water based hybrid nanofluid flow: An artificial neural network modeling

Document Type : Article


1 Department of Mathematics, Davanagere University, Davanagere, Karnataka, India

2 Department of Mathematics, COMSATS University Islamabad, Sahiwal 57000, Pakistan


The addition of different nanoparticles in conventional fluid with proper quantity gives the hybrid fluids which have higher thermo-physical properties. The geometry of the hybrid nanoparticles has substantial impacts in numerous engineering and bio-medical applications. Blasius and Sakiadis flows are considered under under the impact of viscous dissipation phenomenon. Both flows are exemplified at the surface of laminar boundary layer conditions in water-based hybrid nanofluid comprising copper and Titania with Prandtl number 6.2. The heat transport is executed by the implication of intelligent computing paradigm through process of Artificial Levenberg Marquardt back propagated neural networks. The nonlinear PDE’s which governs the fluid flow are organized into set of nonlinear ODE’s by using the similarity functions. Runge-Kutta-Fehlberg’s fourth-fifth order (RKF-45) based shooting scheme is utilized to solve the reduced ODE’s. The larger buoyancy parameter values enhanced the velocity over stationary surface with constant free-stream velocity for Blasius flow and vertical movement of planer surface is moved in stagnant free-stream for Sakiadis flow. The thermal and concentration profiles are reduced against higher buoyancy values for both the cases.


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