Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface
Little is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/12/5/878 |
| _version_ | 1827650955218255872 |
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| author | Abdul Hamid Ganie Fazlullah Fazal Carlos Andrés Tavera Romero Muhammad Sulaiman |
| author_facet | Abdul Hamid Ganie Fazlullah Fazal Carlos Andrés Tavera Romero Muhammad Sulaiman |
| author_sort | Abdul Hamid Ganie |
| collection | DOAJ |
| description | Little is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on water conveying 47 nm of alumina nanoparticles across a uniform surface in this study. The Levenberg–Marquardt backpropagated neural network (LMB-NN) architecture was used to examine the transport phenomena of 47 nm conveying nanoparticles. The partial differential equations (PDEs) are converted into a system of Ordinary Differential Equations (ODEs). To assess our soft-computing process, we used the RK4 method to acquire reference solutions. The problem is investigated using two situations, each with three sub-cases for the change of the rotation parameter K and the volume fraction <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ϕ</mi></semantics></math></inline-formula>. Our simulation results are compared to the reference solutions. It has been proven that our technique is superior to the current state-of-the-art. For further explanation, error histograms, regression graphs, and fitness values are graphically displayed. |
| first_indexed | 2024-03-09T20:27:21Z |
| format | Article |
| id | doaj.art-8ec69e12d5a54d1a914018077ef5e37d |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-09T20:27:21Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-8ec69e12d5a54d1a914018077ef5e37d2023-11-23T23:31:33ZengMDPI AGNanomaterials2079-49912022-03-0112587810.3390/nano12050878Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform SurfaceAbdul Hamid Ganie0Fazlullah Fazal1Carlos Andrés Tavera Romero2Muhammad Sulaiman3Basic Science Department, College of Science and Theoretical Studies, Saudi Electronic University, Abha Male 61421, Saudi ArabiaDepartment of Mathematics, Abdul Wali Khan University, Mardan 23200, PakistanCOMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali 76001, ColombiaDepartment of Mathematics, Abdul Wali Khan University, Mardan 23200, PakistanLittle is known about the rising impacts of Coriolis force and volume fraction of nanoparticles in industrial, mechanical, and biological domains, with an emphasis on water conveying 47 nm nanoparticles of alumina nanoparticles. We explored the impact of the volume fraction and rotation parameter on water conveying 47 nm of alumina nanoparticles across a uniform surface in this study. The Levenberg–Marquardt backpropagated neural network (LMB-NN) architecture was used to examine the transport phenomena of 47 nm conveying nanoparticles. The partial differential equations (PDEs) are converted into a system of Ordinary Differential Equations (ODEs). To assess our soft-computing process, we used the RK4 method to acquire reference solutions. The problem is investigated using two situations, each with three sub-cases for the change of the rotation parameter K and the volume fraction <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>ϕ</mi></semantics></math></inline-formula>. Our simulation results are compared to the reference solutions. It has been proven that our technique is superior to the current state-of-the-art. For further explanation, error histograms, regression graphs, and fitness values are graphically displayed.https://www.mdpi.com/2079-4991/12/5/878Levenberg-Marquardt algorithmbackpropagation neural networkmathematical modelingRunge-Kutta order four techniquemachine learningwater and alumina nanofluid |
| spellingShingle | Abdul Hamid Ganie Fazlullah Fazal Carlos Andrés Tavera Romero Muhammad Sulaiman Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface Nanomaterials Levenberg-Marquardt algorithm backpropagation neural network mathematical modeling Runge-Kutta order four technique machine learning water and alumina nanofluid |
| title | Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface |
| title_full | Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface |
| title_fullStr | Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface |
| title_full_unstemmed | Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface |
| title_short | Quantitative Features Analysis of Water Carrying Nanoparticles of Alumina over a Uniform Surface |
| title_sort | quantitative features analysis of water carrying nanoparticles of alumina over a uniform surface |
| topic | Levenberg-Marquardt algorithm backpropagation neural network mathematical modeling Runge-Kutta order four technique machine learning water and alumina nanofluid |
| url | https://www.mdpi.com/2079-4991/12/5/878 |
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