Entropy optimized Darcy-Forchheimer nanofluid (Silicon dioxide, Molybdenum disulfide) subject to temperature dependent viscosity.

Background In this research communication, entropy optimized Darcy-Forchheimer flow with magnetohydrodynamic over a stretched surface is considered. Here Molybdenum disulfide (MOS2) and Silicon dioxide (SiO2) are taken as a nanoparticles and Propylene glycol as a continuous phase liquid. Electrically conducting fluid is considered and flow is generated via stretched surface of sheet. The total entropy rate which is depends on four types of irreversibilities i.e., heat transfer, porosity, fluid friction and dissipation) is calculated via second law of thermodynamics. The energy expression is mathematically modeled and discussed subject to heat generation/absorption, dissipation, thermal radiation and Joule heating. Furthermore, temperature dependent viscosity is accounted. Method The nonlinear PDE's (partial differential equations) are first changed to ODE's (ordinary differential equations) through implementation of appropriate similarity variables (transformations). The numerical results of ordinary ones are computed via Built-In-Shooting method. The results for the flow field, temperature, skin friction, Nusselt number and entropy generation are discussed against various sundry flow parameters graphically. Results Salient characteristics of sundry flow parameters on the entropy generation rate, velocity, Bejan number, gradients of velocity, gradient of temperature and temperature are examined and display graphically. The results are computed for both nanoparticles. From obtained results it is observed that temperature field increases versus higher thermal Biot number for both nanoparticles. It is also observed that the thermal field is more in presence of Molybdenum disulfide as compared to Silicon dioxide, because the thermal conductivity of Molybdenum disulfide is higher than Silicon dioxide. Entropy generation and Bejan number show contrast impact versus higher estimations of Brinkman number versus both nanoparticles.