N. Ameer Ahammad

The present research examines the unsteady sensitivity analysis and entropy generation of blood-based silver–titanium dioxide flow in a tilted cylindrical W-shape symmetric stenosis artery. The study considers various factors such as the electric field, joule heating, viscous dissipation, and heat source, while taking into account a two-dimensional pulsatile blood flow and periodic body acceleration. The finite difference method is employed to solve the governing equations due to the highly nonlinear nature of the flow equations, which requires a robust numerical technique. The utilization of the response surface methodology is commonly observed in optimization procedures. Drawing inspiration from drug delivery techniques used in cardiovascular therapies, it has been proposed to infuse blood with a uniform distribution of biocompatible nanoparticles. The figures depict the effects of significant parameters on the flow field, such as the electric field, Hartmann number, nanoparticle volume fraction, body acceleration amplitude, Reynolds number, Grashof number, and thermal radiation, on velocity, temperature (nondimensional), entropy generation, flow rate, resistance to flow, wall shear stress, and Nusselt number. The velocity and temperature profiles improve with higher values of the wall slip parameter. The flow rate profiles increase with an increment in wall velocity but decrease with the Womersley number. Increasing the intensity of radiation and decreasing magnetic fields both result in a decrease in the rate of heat transfer. The blood temperature is higher with the inclusion of hybrid nanoparticles than the unitary nanoparticles. The total entropy generation profiles increase for higher values of the Brickman number and temperature difference parameters. Unitary nanoparticles exhibit a slightly higher total entropy generation than hybrid nanoparticles, particularly when positioned slightly away from the center of the artery. The total entropy production decreases by 17.97% when the thermal radiation is increased from absence to 3. In contrast, increasing the amplitude of body acceleration from 0.5 to 2 results in a significant enhancement of 76.14% in the total entropy production.

The self-organizing map (SOM), which is a type of artificial neural network (ANN), was formulated as an optimal control problem. Its objective function is to minimize the mean quantization error, and the state equation is the weight updating equation of SOM. Based on the objective function and the state equations, the Hamiltonian equation based on Pontryagin’s minimum principle (PMP) was formed. This study presents two models of SOM formulated as an optimal control problem. In the first model, called SOMOC1, the design is based on the state equation representing the weight updating equation of the best matching units of the SOM nodes in each iteration, whereas in the second model, called SOMOC2, it considers the weight updating equation of all the nodes in the SOM as the state updating equation. The learning rate is treated as the control variable. Based on the solution of the switching function, a bang-bang control was applied with a high and low learning rate. The proposed SOMOC2 model performs better than the SOMOC1 model and conventional SOM as it considers all the nodes in the Hamiltonian equation, and the switching function obtained from it is influenced by all the states, which provides one costate variable for each. The costate determines the marginal cost of violating the constraint by the state equations, and the switching function is influenced by this, hence producing a greater improvement in terms of the mean quantization error at the final iteration. It was found that the solution leads to an infinite order singular arc. The possible solutions for the suitable learning rates during the singular arc period are discussed in this study.