The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks
The present study explores the unsteady flow of a nanoliquid past a stretching cylinder with the consequence of heat source/sink and chemical reaction. Additionally, the effect of nanoparticle aggregation, convective boundary conditions, and magnetic field on the liquid flow is taken into considerat...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Partial Differential Equations in Applied Mathematics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666818124003176 |
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| author | Sumanta Shagolshem Chandan K Malatesh Akkur Bharti Kumari Chander Prakash T.V. Smitha Naveen Kumar R |
| author_facet | Sumanta Shagolshem Chandan K Malatesh Akkur Bharti Kumari Chander Prakash T.V. Smitha Naveen Kumar R |
| author_sort | Sumanta Shagolshem |
| collection | DOAJ |
| description | The present study explores the unsteady flow of a nanoliquid past a stretching cylinder with the consequence of heat source/sink and chemical reaction. Additionally, the effect of nanoparticle aggregation, convective boundary conditions, and magnetic field on the liquid flow is taken into consideration. Utilizing similarity variables, the modeled equations are transformed into dimensionless ordinary differential equations (ODEs). Further, the obtained ODEs are numerically solved by using the Keller box method. Moreover, the physics-informed neural network (PINN) is applied to analyze the flow, heat, and mass transport features. Graphical illustrations are used to display the influence of various parameters on the velocity, concentration, and temperature profiles for aggregation and without aggregation cases. As the value of the magnetic parameter increases, the temperature and concentration profile upsurge, but the reverse trend can be seen in the velocity profile. The concentration and temperature profiles rise as the unsteadiness parameter increases, but the velocity profile declines. The concentration, velocity, and temperature profiles are strengthened by an improvement in the curvature parameter value. The intensification in the values of the chemical reaction parameter declines the concentration. |
| format | Article |
| id | doaj-art-8f48470c093b4aacbb45b51075f308cf |
| institution | Kabale University |
| issn | 2666-8181 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Partial Differential Equations in Applied Mathematics |
| spelling | doaj-art-8f48470c093b4aacbb45b51075f308cf2024-12-13T11:05:36ZengElsevierPartial Differential Equations in Applied Mathematics2666-81812024-12-0112100931The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networksSumanta Shagolshem0Chandan K1Malatesh Akkur2Bharti Kumari3Chander Prakash4T.V. Smitha5Naveen Kumar R6Computational Science Lab, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka 560035, IndiaAmrita School of Artificial Intelligence, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka 560035, IndiaDepartment of Physics and Electronics, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, IndiaNIMS School of Petroleum and Chemical Engineering, NIMS University Rajasthan, Jaipur, IndiaUniversity Centre for Research and Development, Chandigarh University, Mohali, Punjab 140413, IndiaComputational Science Lab, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka 560035, IndiaComputational Science Lab, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka 560035, India; Corresponding author.The present study explores the unsteady flow of a nanoliquid past a stretching cylinder with the consequence of heat source/sink and chemical reaction. Additionally, the effect of nanoparticle aggregation, convective boundary conditions, and magnetic field on the liquid flow is taken into consideration. Utilizing similarity variables, the modeled equations are transformed into dimensionless ordinary differential equations (ODEs). Further, the obtained ODEs are numerically solved by using the Keller box method. Moreover, the physics-informed neural network (PINN) is applied to analyze the flow, heat, and mass transport features. Graphical illustrations are used to display the influence of various parameters on the velocity, concentration, and temperature profiles for aggregation and without aggregation cases. As the value of the magnetic parameter increases, the temperature and concentration profile upsurge, but the reverse trend can be seen in the velocity profile. The concentration and temperature profiles rise as the unsteadiness parameter increases, but the velocity profile declines. The concentration, velocity, and temperature profiles are strengthened by an improvement in the curvature parameter value. The intensification in the values of the chemical reaction parameter declines the concentration.http://www.sciencedirect.com/science/article/pii/S2666818124003176NanofluidStretching cylinderMagnetic fieldHeat source/sinkChemical reactionsPINN |
| spellingShingle | Sumanta Shagolshem Chandan K Malatesh Akkur Bharti Kumari Chander Prakash T.V. Smitha Naveen Kumar R The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks Partial Differential Equations in Applied Mathematics Nanofluid Stretching cylinder Magnetic field Heat source/sink Chemical reactions PINN |
| title | The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks |
| title_full | The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks |
| title_fullStr | The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks |
| title_full_unstemmed | The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks |
| title_short | The nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina-water based nanofluid: A Keller box approach with applications of wavelet physics inspired neural networks |
| title_sort | nanoparticles aggregation aspects on the chemically reactive unsteady flow of alumina water based nanofluid a keller box approach with applications of wavelet physics inspired neural networks |
| topic | Nanofluid Stretching cylinder Magnetic field Heat source/sink Chemical reactions PINN |
| url | http://www.sciencedirect.com/science/article/pii/S2666818124003176 |
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