Levenberg-marquardt design for analysis of maxwell fluid flow on ternary hybrid nanoparticles passing over a riga plate under convective boundary conditions

• The velocity field shows an opposite pattern for α , it is an increasing function of the modified Hartmann number Λ . • The temperature distribution is more influenced by the Biot number ( B i ) and thermal radiation ( R d ) . • A l 2 O 3 / H 2 O material, Λ has a greater impact on the velocity of the A l 2 O 3 + G r a p h e n e + C N T / H 2 O and A l 2 O 3 + G r a p h e n e / H 2 O materials. • The temperature rise for the A l 2 O 3 / H 2 O system has been more noticeable than for the A l 2 O 3 + G r a p h e n e + C N T / H 2 O and A l 2 O 3 + G r a p h e n e / H 2 O systems. This study provides an examination of the effects of the Maxwell fluid on ternary hybrid nanoparticles passing through a Riga plate under convective boundary conditions using neural network backpropagation. Water is combined with the ternary nanoparticles A l 2 O 3 , G r a p h e n e a n d C N T as the base fluid. Heat sources and sinks as well as thermal radiation are taken into account in the heat transfer study. Partial differential equations can be transformed into a form without any dimensions by applying a similarity transformation. The Finite element method is then employed to resolve this altered equation. The problem's highly nonlinear equations are numerically resolved by utilizing techniques from the Artificial Neural Network method. The Artificial Neural Network method solution technique is used to solve these ordinary differential equations and create the Neural Network Backpropagation reference dataset. The plots illustrating the solution and error analysis for varying different parameters are analyzed in MATLAB using this reference dataset. Regression analysis, error histogram, and mean squared error data are used to assess neural network backpropagation performance. The techniques for testing, validating, and training are applied to the model solution. The impacts of fluid flow and thermal field were examined through important factors. It is found that velocity field shows an opposite pattern for Maxwell fluid variable, it is an increasing function of the modified Hartmann number and the temperature distribution is more influenced by the Biot number and thermal radiation.

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Publication Details

Journal
Results in Engineering
Published
2024-11-27
DOI
https://doi.org/10.1016/j.rineng.2024.103502
Citations
32
Primary Topic
Nanofluid Flow and Heat Transfer
Type
article
Field-Weighted Citation Impact
5.90
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article

Levenberg-marquardt design for analysis of maxwell fluid flow on ternary hybrid nanoparticles passing over a riga plate under convective boundary conditions

Noreen Sher Akbar, Farooq Ahmed Shah, Tayyab Zamir, Arseniy V. Mikhaylov
32 citations
Results in Engineering
Nanofluid Flow and Heat Transfer
5.90
article

Levenberg-marquardt design for analysis of maxwell fluid flow on ternary hybrid nanoparticles passing over a riga plate under convective boundary conditions

Noreen Sher Akbar, Farooq Ahmed Shah, Tayyab Zamir, Arseniy V. Mikhaylov
article en
32 citations

Abstract

• The velocity field shows an opposite pattern for α , it is an increasing function of the modified Hartmann number Λ . • The temperature distribution is more influenced by the Biot number ( B i ) and thermal radiation ( R d ) . • A l 2 O 3 / H 2 O material, Λ has a greater impact on the velocity of the A l 2 O 3 + G r a p h e n e + C N T / H 2 O and A l 2 O 3 + G r a p h e n e / H 2 O materials. • The temperature rise for the A l 2 O 3 / H 2 O system has been more noticeable than for the A l 2 O 3 + G r a p h e n e + C N T / H 2 O and A l 2 O 3 + G r a p h e n e / H 2 O systems. This study provides an examination of the effects of the Maxwell fluid on ternary hybrid nanoparticles passing through a Riga plate under convective boundary conditions using neural network backpropagation. Water is combined with the ternary nanoparticles A l 2 O 3 , G r a p h e n e a n d C N T as the base fluid. Heat sources and sinks as well as thermal radiation are taken into account in the heat transfer study. Partial differential equations can be transformed into a form without any dimensions by applying a similarity transformation. The Finite element method is then employed to resolve this altered equation. The problem's highly nonlinear equations are numerically resolved by utilizing techniques from the Artificial Neural Network method. The Artificial Neural Network method solution technique is used to solve these ordinary differential equations and create the Neural Network Backpropagation reference dataset. The plots illustrating the solution and error analysis for varying different parameters are analyzed in MATLAB using this reference dataset. Regression analysis, error histogram, and mean squared error data are used to assess neural network backpropagation performance. The techniques for testing, validating, and training are applied to the model solution. The impacts of fluid flow and thermal field were examined through important factors. It is found that velocity field shows an opposite pattern for Maxwell fluid variable, it is an increasing function of the modified Hartmann number and the temperature distribution is more influenced by the Biot number and thermal radiation.

Results in EngineeringVol. 24
Prince Mohammad bin Fahd University (SA), COMSATS University Islamabad (PK), Financial University (RU), Western Caspian University (AZ), Baku Eurasian University
Climate action
Openalex Percentile: Top 3%
Nanofluid Flow and Heat Transfer
5.90
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