Neural network architecture to optimize the nanoscale thermal transport of ternary magnetized Carreau nanofluid over 3D wedge

Abstract

Significance: Incorporation of nanoparticles in base fluid water is significant for analysis of thermal behavior of nanofluid mixtures, which has various applications in materials science and thermal engineering, and supervised neural scheme predicts the thermal behavior by solving Carreau nanofluid model. Motive: This article brings the investigation related to prediction of thermal transport of a ternary magnetized hybrid nanofluid [(Al2O3, CuO, TiO2)/H2O] with a three-dimensional Carreau nanofluid model over a wedge. Three nanoparticles dispersed in water (H2O). Inclined magnetic field is considered for judgement of velocity profile and thermal radiation is utilized to scrutinize the temperature distribution of nanofluid. The Carreau mathematical model is chosen to depict the rheological characteristics of non-Newtonian fluids at very high and very low shear rate. Methodology: Physical assumptions creates the system of Partial differential equations (PDEs) and these are converted into ordinary differential equations (ODEs) by similarity tool. Further ODEs are dealt with bvp4c scheme and further prediction of solution is made by Levenberg-Marquardt neural network (LM-NN) supervised neural scheme. Findings: Increased volume friction coefficients of nanoparticles increases the transport of heat. High inclined magnetic effect, thermal radiation, pressure gradient and shear strain parameter predict higher thermal transport. © 2024 The Author(s)