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The blood flow over a stenotic artery is important investigation in mathematical fluid mechanics due to its significance in biomedical sciences. The present investigation aims to examine how nanoparticles affect circulation in a stenotic artery. We examine the significance of magnetized Casson nanofluid flow over a stenotic artery under consideration of the mathematical flow problem. By using the suitable self-similarity variables, the partial differential equation is transformed into ordinary differential equations. Then, the non-dimensional equations are solved using the MATLAB software in the Bvp5c scheme. By increasing the magnetic properties of the circulatory system's cells, which is a scheme that was previously utilized by raising the magnetic field parameter, there was a predictable decrease in the blood flow. Covering the stenosed artery with a greater amount of copper nanoparticles improves its heat transmission efficiency. The present technique may help distribute medications throughout the circulatory system. [ABSTRACT FROM AUTHOR]

Heat transfer innovation is essential in modern society because thermal management systems need effective heating and cooling processes. It is also an essential component in the vehicle industry and other types of transportation, in addition to automobile industry, aviation technology, the computer industry, and the manufacturing industry. By the inspiration of importance of magnetohydrodynamic hybrid nanofluid over a stretching cylinder with the influence of Williamson fluid and porous medium is examined in this current study. To convert the PDEs into ODEs, suitable self-similarity transformation is used. After applying transformations, for graphical purpose we have used the bvp5c technique. The impact of active parameters affecting the fluid's capacity to transfer significance is demonstrated in graphs and tables. In the result section we noticed on the velocity outlines decreased for increasing M parameter. The Cf and Nu increased for larger values of the M and curvature parameters. Additional properties of M and Rd parameter inputs result in improved temperature profiles. [ABSTRACT FROM AUTHOR]

The physiological system loses thermal energy to nearby cells via the bloodstream. Such energy loss can result in sudden death, severe hypothermia, anemia, high or low blood pressure, and heart surgery. Gold and iron oxide nanoparticles are significant in cancer treatment. Thus, there is a growing interest among biomedical engineers and clinicians in the study of entropy production as a means of quantifying energy dissipation in biological systems. The present study provides a novel implementation of an intelligent numerical computing solver based on an MLP feed-forward backpropagation ANN with the Levenberg–Marquard algorithm to interpret the Cattaneo–Christov heat flux model and demonstrate the effect of entropy production and melting heat transfer on the ferrohydrodynamic flow of the Fe3O4-Au/blood Powell–Eyring hybrid nanofluid. Similarity transformation studies symmetry and simplifies PDEs to ODEs. The MATLAB program bvp4c is used to solve the nonlinear coupled ordinary differential equations. Graphs illustrate the impact of a wide range of physical factors on variables, including velocity, temperature, entropy generation, local skin friction coefficient, and heat transfer rate. The artificial neural network model engages in a process of data selection, network construction, training, and evaluation through the use of mean square error. The ferromagnetic parameter, porosity parameter, distance from origin to magnetic dipole, inertia coefficient, dimensionless Curie temperature ratio, fluid parameters, Eckert number, thermal radiation, heat source, thermal relaxation parameter, and latent heat of the fluid parameter are taken as input data, and the skin friction coefficient and heat transfer rate are taken as output data. A total of sixty data collections were used for the purpose of testing, certifying, and training the ANN model. From the results, it is found that the fluid temperature declines when the thermal relaxation parameter is improved. The latent heat of the fluid parameter impacts the entropy generation and Bejan number. There is a less significant impact on the heat transfer rate of the hybrid nanofluid over the sheet on the melting heat transfer parameter. [ABSTRACT FROM AUTHOR]