Artificial neural network method for engine oil based Cu–Ag–Au Tri-hybrid nanofluid over a Riga surface
Mariam M. O. Alsoufi, Kotha Gangadhar, Saeed Dinarvand
Abstract
Advanced nanofluids, also known as ternary nanofluids, impressed engineers and scientists with their unique molecular structure and enhanced heat transfer capabilities. These might help in aerodynamics, biomedical engineering, aeronautical science, and electrical gadgets. These may be utilised as coolants in a variety of industrial applications, particularly in the electrical and chemical sectors. The Casson-Maxwell fluid model is used to explain a unique combination of nanoparticles, copper, silver, gold, and engine oil in a base fluid that has good thermodynamic characteristics for improving thermal and electromagnetic effects. Engine oil is used as the foundation fluid running through a Riga plate. The controlling PDEs are converted to ODEs. The bvp4c approach is used to determine the solution in order to the ordinary differential equation. This model employs a revolutionary calculating numbers intelligently technology, a perceptron with many layers that uses feed-forward and back-propagation, as well as a technique for artificial neural networks based on the Levenberg-Marquardt formula. The data is collected for neurological network confirmation, examination, and training. The one that models effectiveness and the average square error are derived using artificial neural networks.