Analysis of melting heat-mass characteristics of Sutterby nanofluid flow based on design of intelligent-based neuro-structures
Annotatsiya
The great effect of nano-technology has elevated the average to the extraordinary in the quickly varying fields of digitalization and science. The relationship of nanofluid with bioconvection has range of applications in different spheres of life, it opened new frontiers for enhancement of transport phenomena, since thermal conductivity of nanofluid is extraordinary so these fluids are considered as ideal heat transmission fluid. Bioconvection is collective movement of microorganisms within fluid medium which is a natural occuring phenomenon. The recent exploration emphasis on individual as well as combined applications of nanofluid along bioconvection in various disciplines including biomedical devices, solar energy appliances, microfluidics as well as enhanced thermal capabilities. There has been a notable change in recent years as we continue to move into the field of nano-scale innovation due to scientific and technological advancements. A chemical mechanism of mass and heat transfer include fluid flow across a variety of geometries, subject to certain conditions. A theoretical numerical analysis of the bio-convective Sutterby nano-fluid flow is presented in this recent manuscript. The system of PDEs is developed than these partial differential equations in the boundary layer was converted to ODEs via similarity transformations. LM-BPS is utilized to resolve the obtained structure of ODEs mathematically a MATLAB’s neural network of artificial intelligence. With specific controlled parameters (Parameter for heat generation/absorption), (Unsteadiness parameter), (Magnetic variable), (Prandtl number), (Parameter for Brownian motion), (Parameter for thermophoresis), (Lewis concentration number) and (Peclet number) the distributions velocity profile , temperature profile , concentration profile and bio-convection are given graphically for all eight scenarios. To complete the approximate solution of the Bio-connective Sutterby nano-fluidic model for different scenarios, Testing, validation and training process is carried out using the LM-BPS. The accuracy of the suggested LM-BPS is then confirmed by comparison with reference data. Mean-squared error, Regression analysis plots,fitness, Histogram analysis and State of function are used to validate the efficacy of the suggested LM-BPS.