Particle Swarm Optimization for exploring Darcy-Forchheimer flow of Casson fluid between co-axial rotating disks with the Cattaneo-Christov model.

In this paper, we carried out a numerical analysis of the fluid dynamics and heat transfer occurring between two parallel disks. The study accounts for the impact of temperature-dependent fluid viscosity and thermal conductivity. We systematically investigated various parameters, including viscosity, thermal conductivity, rotational behavior (rotation or counter-rotation), and the presence of stretching, aiming to comprehend their effects on fluid velocity, temperature profiles, and pressure distributions. Our research constructs a mathematical model that intricately couples fluid heat transfer and pressure distribution within the rotating system. To solve this model, we employed the 'Particle Swarm Optimization' method in tandem with the finite difference approach. The results are presented through visual representations of fluid flow profiles, temperature, and pressure distributions along the rotational axis. The findings revealed that the change in Casson factor from 2.5 to 1.5 resulted in a reduction of skin friction by up to 65%, while the change in local Nusselt number was minimal. Furthermore, both the viscosity variation parameter and thermal conductivity parameters were found to play significant roles in regulating both skin friction and local Nusselt number. These findings will have practical relevance to scientists and engineers working in fields related to heat management, such as those involved in rotating gas turbines, computer storage devices, medical equipment, space vehicles, and various other applications.

Wavelets based physics informed neural networks to solve non-linear differential equations.

In this study, the applicability of physics informed neural networks using wavelets as an activation function is discussed to solve non-linear differential equations. One of the prominent equations arising in fluid dynamics namely Blasius viscous flow problem is solved. A linear coupled differential equation, a non-linear coupled differential equation, and partial differential equations are also solved in order to demonstrate the method's versatility. As the neural network's optimum design is important and is problem-specific, the influence of some of the key factors on the model's accuracy is also investigated. To confirm the approach's efficacy, the outcomes of the suggested method were compared with those of the existing approaches. The suggested method was observed to be both efficient and accurate.