Numerical study of TC4-NiCr/EG+Water hybrid nanofluid over a porous cylinder with Thompson and Troian slip boundary condition: Artificial neural network model

The analysis is intended to demonstrate two-phase flow of hybrid dusty Eyring–Powell nanofluid flow over a porous cylinder with Thompson and Troian slip boundary condition. The combination of (C₂H₆O₂) ethylene glycol (40 %) and (H₂O) water (60 %) -based TC4 (TC4 is an alloy of titanium consisting of 90 % titanium, 6 % aluminium, and 4 % vanadium) was utilized to preparing the hybrid nanofluids. Ti–6Al–4V is another name for this alloy) and nickel-chromium (NiCr) nanoparticles flow properties after the mechanism has been filled with dusty particles. A new approach to the execution of an intelligent numerical computing solution is shown in this paper, which uses the Levenberg-Marquard algorithm to drive MLP feed-forward back-propagation ANN. Data were collected for the testing, certification, and training of the ANN model. The bvp4c with MATLAB solver is used to resolve nonlinear versions of well-known mathematical problems, such as those involving the skin friction coefficient, heat transfer rates, velocity, temperature, and other variables. The ANN model is designed to perform the following tasks: selecting data, building networks, training networks, and assessing their effectiveness via the use of the mean square error measure. The influence of key factors on the fluid transport characteristics is demonstrated via tables and graphs. The velocity profile is higher values, resulting in an amplification of the curvature parameter. The temperature rises when the radiation parameter values decline, and the specific heat ratio parameter exhibits a similar pattern. Higher values of the velocity slip parameter and melting factor both are decays on the Nusselt number. This type of theoretical investigation is a necessary aspect of many aircraft applications, including power production, cooling of electrical and propulsion reactors.