A titanium oxide- and silver-based hybrid nanofluid flow between two Riga walls that converge and diverge through a machine-learning approach

The silver (Ag) and titanium oxide (TiO ) nanoparticle combination is responsible for the uniform heat distribution, which enhances the heat transfer of the hybrid nanofluid (HNF). The HNFs are capable of enhancing heat transfer, which has significant applications in energy resources and heat exchan...

Full description

Saved in:
Bibliographic Details
Published in:Nanotechnology reviews (Berlin) Vol. 14; no. 1; pp. 2371 - 9
Main Authors: Alqarni, Manal M., Khan, Rajwali, Alghamdi, Wajdi, Aljohani, Mashael A., Gul, Taza, Mahmoud, Emad E.
Format: Journal Article
Language:English
Published: Berlin De Gruyter 01.01.2025
Walter de Gruyter GmbH
Subjects:
Ag + TiO
ag + tio2 nanomaterials
artificial neural network, control volume finite element method
Divergence
Energy resources
Energy sources
Errors
Finite element method
Fluid flow
Heat distribution
Heat exchangers
Heat transfer
Learning algorithms
Machine learning
Nanofluids
nanomaterials
Nanoparticles
Neural networks
Nonlinear systems
Riga walls, converge and diverge channel
Rigid walls
Silver
Titanium dioxide
Titanium oxide
Titanium oxides
ISSN:2191-9097, 2191-9089, 2191-9097
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The silver (Ag) and titanium oxide (TiO ) nanoparticle combination is responsible for the uniform heat distribution, which enhances the heat transfer of the hybrid nanofluid (HNF). The HNFs are capable of enhancing heat transfer, which has significant applications in energy resources and heat exchangers. The flow is the result of the convergence and divergence of two non-parallel Riga walls. The control volume finite element method is employed to obtain a microscopic view of the HNFs between non-parallel rigid walls. Furthermore, the governing equations of the physical system are solved using a machine learning technique called wavelet-based physics-informed neural network. The methodology’s implementation is flexible due to the nonlinearity of the equations, and the activation function used enhances the accuracy of the solution. Enhancement of the heat transfer rate is obtained by varying the embedded parameters. The percentage increase in heat transfer rate is achieved from 30.7 to 24.3% using the Ag and TiO HNFs. The fuzzy inference technique function outputs, mean squared error results, and error normalized squared error are observed with the best validation employing the neural network. In the end, the results are compared with the existing literature.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2191-9097
2191-9089
2191-9097
DOI:10.1515/ntrev-2025-0212