Performance of Heat Transfer in Micropolar Fluid with Isothermal and Isoflux Boundary Conditions Using Supervised Neural Networks

The current study delivers a numerical investigation on the performance of heat transfer and flow micropolar fluid in porous Darcy structures with isothermal isoflux walls (boundary conditions) stretching sheet. dynamics mechanism such flows are modelled by nonlinear partial differential equations that reduced to system ordinary utilizing porosity medium similarity functions. Generally, explicit or analytical solutions for problems hard calculate. Therefore, we have designed computer artificial intelligence-based technique. reliability neural networks using machine learning (ML) approach is used local optimization technique investigate behaviours different material parameters as Prandtl number, parameters, Reynolds index parameter, injection/suction parameter temperature profile, speed, spin/rotational behaviour microstructures. approximate determined efficient compared classical Runge–Kutta fourth-order method generalized finite difference approximation quasi-uniform mesh. accuracy errors lies around 10?8 10?10 between traditional strategy. ML-based techniques solve without discretization computational work, not subject continuity differentiability governing model. Moreover, results illustrated briefly help implement microfluids drug administering, elegans immobilization, pH controlling processes.