Implementation of Particle Swarm Optimization Algorithm on Entropy Generation of Carreau Nanofluid Flow Embedded with Carbon Nanotubes Over a Non Linear Stretching Sheet

This study investigates the Implementation of Particle Swarm Optimization algorithm on entropy generation of Carreau nanofluid flow embedded with CNTs over a non linear stretching sheet. The baic intention of Carreau nanofluid defined with the power-law exponent distinguishes shear thinning and shear thickening behaviors in various sectors like both medical and industrial applications. Using similarity transformations, the governing nonlinear partial differential equations are converted into ordinary differential equations, solved efficiently via the PSO algorithm implemented in Python. This study analyzes the effects of key parameters—magnetic, Weissenberg, nonlinear stretching, thermal radiation, Eckert, and Schmidt numbers on momentum, thermal, and concentration profiles. Detailed plots and tables illustrate the parametric influences, validating the findings against established research at various values of stretching parameter ( m ) under the absence of nanoparticles. This research contributes to understanding Carreau nanofluid dynamics, shedding light on interactions between nonlinear stretching, thermal radiation, and nanofluid properties, with implications for engineering applications involving non-Newtonian fluids and nanotechnology. The study demonstrates PSO’s efficiency in solving complex fluid dynamics problems, highlighting its potential for broader applications, and providing valuable insights into optimizing nanofluid flow and heat transfer in various industrial settings.