ANN model of three-dimensional micropolar dusty hybrid nanofluid flow with coriolis force: biomedical applications

Biomedical engineers, medical scientists, and clinicians are interested in measuring blood flow rate because it is used to detect cardiovascular diseases such as atherosclerosis and arrhythmia. A variety of non-Newtonian fluid models have been used by numerous researchers to examine how blood flows through the cardiovascular system. The micropolar fluid model can more accurately represent the rheological behavior of blood due to its shear-thinner properties. Additionally, gold and magnesium oxide nanoparticles are used as antibacterial for the injured tissues and to improve the fluid's heat transfer rate. This study aims to analyze the heat transfer behavior of the MHD micropolar dusty hybrid nanofluid in the presence of a heat source/sink and linear thermal radiation over a three-dimensional surface. A novel implementation of an intelligent numerical computing solver based on MLP feed-forward backpropagation ANN with the Levenberg-Marquardt algorithm is provided in the current study. Data were gathered for the ANN model's testing, certification, and training. Established mathematical equations are nonlinear, which are resolved for velocity, temperature, skin friction coefficient, and the rate of heat transfer by using the bvp4c with MATLAB solver. The ANN model selects data, constructs and trains a network, then evaluates its efficacy via mean square error. In the entire study, dusty hybrid nanofluid has greater influence than dusty fluid. Improving the values of the vortex viscosity and fluid-particle interaction parameters increases the rate of heat transfer. The temperature profile of dusty and dusty hybrid nanofluid rises for the higher values of the thermal radiation parameter.

Automated summary

This study aims to analyze the heat transfer behavior of the MHD micropolar dusty hybrid nanofluid in the presence of a heat source/sink and linear thermal radiation over a three-dimensional surface. A novel implementation of an intelligent numerical computing solver based on MLP feed-forward backpropagation ANN with the Levenberg-Marquardt algorithm is provided in the current study. Dusty hybrid nanofluid has a greater influence than dusty fluid, and improving the values of the vortex viscosity and fluid-particle interaction parameters increases the rate of heat transfer. The temperature profile of dusty and dusty hybrid nanofluid rises for higher values of the thermal radiation parameter.