MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm - Ali, Aatif; Ahammad, N. Ameer; Tag-Eldin, Elsayed; Gamaoun, Fehmi; Daradkeh, Yousef Ibrahim; Yassen, Mansour F. (Frontiers in Energy Research, 2022)
Various studies have been conducted on the topic of predicting the thermal conductivity of nanofluids. Here, the thermal conductivity of nanofluid is determined using Artificial Neural Networks (ANNs) since this approach is both rapid and accurate, as well as cost-effective. To forecast the thermal conductivity of MHD Williamson nanofluid flow through a vertical sheet, feed-forward neural network with various numbers of neurons has been evaluated and the best network based on the performance is selected. The fluid model incorporates the effects of Joule heating, heat generation absorption, thermal radiation, and a chemical reaction (MHD-WNFHGA). A combination of heat radiation and reactive species improves the energy and solute profiles. The magnetic Reynolds number is assumed so small therefore the generated magnetic field has no effect. A postulate of similarity variables are used to convert the physical model in the form of nonlinear partial differential equations to ordinary differential equations system. A supervised Levenberg–Marquardt backpropagation algorithm possesses multilayer perceptron is used for training the network, which is one of the top algorithms in machine learning. The bvp4cnumerical technique is adopted to build the datasets for the construction of a continuous neural network mapping. Flow, energy, and concentration profiles of the fluidic flow are constructed by adjusting several physical quantities such as Williamson parameter, thermal radiation parameter, magnetic parameter, Eckert number, Darcy number, Brownian motion and thermophoresis parameter. Analytical techniques such as error histogram graphs and regression-based statistical graphs are used to examine the accuracy of a suggested method. It's been found that the LevenbergMarquardt Backpropogation neural network mappings' derivation, convergence, authentication, and consistency have been proven.