Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm
The aim of this research is to provide a new computer-assisted approach for predicting thermophoresis particle decomposition on three-dimensional Casson nanofluid flow that passed over a stretched surface (thermophoresis particle decomposition on three-dimensional Casson nanofluid flow; TPD-CNF). In...
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De Gruyter
2024-02-01
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Online Access: | https://doi.org/10.1515/phys-2023-0181 |
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author | Khan Amna Aljuaydi Fahad Khan Zeeshan Islam Saeed |
author_facet | Khan Amna Aljuaydi Fahad Khan Zeeshan Islam Saeed |
author_sort | Khan Amna |
collection | DOAJ |
description | The aim of this research is to provide a new computer-assisted approach for predicting thermophoresis particle decomposition on three-dimensional Casson nanofluid flow that passed over a stretched surface (thermophoresis particle decomposition on three-dimensional Casson nanofluid flow; TPD-CNF). In order to understand the flow behavior of nanofluid flow model, an optimized Levenberg–Marquardt learning algorithm with backpropagation neural network (LMLA-BPNN) has been designed. The mathematical model of TPD-CNF framed with appropriate assumptions and turned into ordinary differential equations via suitable similarity transformations are used. The bvp4c approach is used to collect the data for the LMLA-BPNN, which is used for parameters related with the TPD-CNF model controlling the velocity, temperature, and nanofluid concentration profiles. The proposed algorithm LMLA-BPNN is used to evaluate the obtained TDP-CNF model performance in various instances, and a correlation of the findings with a reference dataset is performed to check the validity and efficacy of the proposed algorithm for the analysis of nanofluids flow composed of sodium alginate nanoparticles dispersed in base fluid water. Statistical tools such as Mean square error, State transition dynamics, regression analysis, and error dynamic histogram investigations all successfully validate the suggested LMLA-BPNN for solving the TPD-CNF model. LMLA-BPNN networks have been used to numerically study the impact of different parameters of interest, such as Casson parameter, power-law index, thermophoretic parameter, and Schmidt number on flow profiles (axial and transverse), and energy and nanofluid concentration profiles. The range, i.e., 10−4–10−5 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks. |
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spelling | doaj.art-71a497ad762442f6a965f45bb0d341a52024-03-04T07:29:36ZengDe GruyterOpen Physics2391-54712024-02-012218001010.1515/phys-2023-0181Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithmKhan Amna0Aljuaydi Fahad1Khan Zeeshan2Islam Saeed3Department of Mathematics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, 23200, PakistanDepartment of Mathematics, College of Science and Humanities, Prince Sattam bin Abulaziz University, Al-Kharj, 11942, Saudi ArabiaDepartment of Mathematics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, 23200, PakistanDepartment of Mathematics, Abdul Wali Khan University Mardan, Khyber Pakhtunkhwa, 23200, PakistanThe aim of this research is to provide a new computer-assisted approach for predicting thermophoresis particle decomposition on three-dimensional Casson nanofluid flow that passed over a stretched surface (thermophoresis particle decomposition on three-dimensional Casson nanofluid flow; TPD-CNF). In order to understand the flow behavior of nanofluid flow model, an optimized Levenberg–Marquardt learning algorithm with backpropagation neural network (LMLA-BPNN) has been designed. The mathematical model of TPD-CNF framed with appropriate assumptions and turned into ordinary differential equations via suitable similarity transformations are used. The bvp4c approach is used to collect the data for the LMLA-BPNN, which is used for parameters related with the TPD-CNF model controlling the velocity, temperature, and nanofluid concentration profiles. The proposed algorithm LMLA-BPNN is used to evaluate the obtained TDP-CNF model performance in various instances, and a correlation of the findings with a reference dataset is performed to check the validity and efficacy of the proposed algorithm for the analysis of nanofluids flow composed of sodium alginate nanoparticles dispersed in base fluid water. Statistical tools such as Mean square error, State transition dynamics, regression analysis, and error dynamic histogram investigations all successfully validate the suggested LMLA-BPNN for solving the TPD-CNF model. LMLA-BPNN networks have been used to numerically study the impact of different parameters of interest, such as Casson parameter, power-law index, thermophoretic parameter, and Schmidt number on flow profiles (axial and transverse), and energy and nanofluid concentration profiles. The range, i.e., 10−4–10−5 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks.https://doi.org/10.1515/phys-2023-0181nanofluidsodium alginate nanoparticlescasson fluidsstretched surfacelevenbergmarquardt backpropagation algorithmneural networks |
spellingShingle | Khan Amna Aljuaydi Fahad Khan Zeeshan Islam Saeed Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm Open Physics nanofluid sodium alginate nanoparticles casson fluids stretched surface levenberg marquardt backpropagation algorithm neural networks |
title | Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm |
title_full | Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm |
title_fullStr | Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm |
title_full_unstemmed | Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm |
title_short | Numerical analysis of thermophoretic particle deposition on 3D Casson nanofluid: Artificial neural networks-based Levenberg–Marquardt algorithm |
title_sort | numerical analysis of thermophoretic particle deposition on 3d casson nanofluid artificial neural networks based levenberg marquardt algorithm |
topic | nanofluid sodium alginate nanoparticles casson fluids stretched surface levenberg marquardt backpropagation algorithm neural networks |
url | https://doi.org/10.1515/phys-2023-0181 |
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