This study aims to examine how Ag and TiO2 hybrid nanofluids (HNFs) move through a nonparallel stretching channel to improve heat transfer (HT).
Stretching walls that converge and diverge are responsible for the influence on the (Ag and TiO2) HNF flow. The heat absorption omission and viscous dissipation characters are also included in the model to increase the HT rate. The governing equations are solved through the control volume finite element method (CVFEM) using the (FEA Tool-Multiphysics) software. Solving the transformed equations is done through a Wavelet-based physics-informed neural network.
The behavior of nanoparticle (NP) properties is evaluated by presenting the results as NP concentration, illustrated through streamlines, isotherms and average Nusselt numbers. The impact of model parameters such as Eckert number, heat absorption, omission parameter, Magnetic field, Reynold number and stretching and shrinking parameters has been observed. The statistical analysis of the results revealed an 11.8% improvement in HT. The (mean squared error) results and (error normalized squared error) are observed with the best validation using the neural network. A comparison has been made to validate the obtained results.
The main contribution is the stretched walls concept solution through CVFEM and an unsupervised neural network, which were not focused on earlier for the converging and diverging channel with the combination of Ag and TiO2. The extension is possible in the case of other nanomaterials and experimental analysis.
The main contribution is the stretched walls concept solution through CVFEM and an unsupervised neural network, which were not focused on earlier for the converging and diverging channel with the combination of Ag and TiO2. The study provides an intriguing method to assess the microscopic view of parameters that are crucial in thermodynamic processes and ultimately lead to the optimal thermal configuration.
Renewable energy is the main and most important factor in human development and this investigation focuses on thermal energy sources.
The main contributions are the CVFEM and unsupervised neural network, which were not focused on earlier for the converging and diverging channel with the combination of Ag and TiO2. The study provides an intriguing method to assess the microscopic view of parameters that are crucial in thermodynamic processes and ultimately lead to the optimal thermal configuration.
