Priyadharshini, P and Vanitha Archana, M (2024) Mathematical modelling for viscoinelastic nanofluid flow over a stretching sheet with machine learning: an application to tissue adhesive. Mathematical modelling for viscoinelastic nanofluid flow over a stretching sheet with machine learning: an application to tissue adhesive. pp. 1-16.
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Mathematical modelling for viscoinelastic nanofluid flow over a stretching sheet with machine learning- an application to tissue adhesive.pdf - Published Version
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Abstract
The medicated tissue adhesive on a stretching surface through Prandtl-Eyring nanofluid flow is empha
sized in the current article to estimate the heat transfer rate and optimize the adhesion process. The
effects of Brownian motion and thermophoresis on electrically conducting viscoinelastic nanofluid are
taken into consideration with the sight of convective states. The modeled governing equations are nondi
mensionalized by operating similarity variables to stimulate the optimization process. The result of an
executed model is solved scientifically by employing the NDSolve technique. The influence of various
parameters on the fluid momentum, thermal, and concentration distributions is accentuated through
graphs. Furthermore, the machine learning approach is enhanced to analyze the physical quantities of
interest in the entire region. The outcomes are validated to those that have already been published in
the pertinent area literature to determine the effectiveness of viscoinelastic nanofluid. The findings re
vealed that the Prandtl-Eyring fluid parameter enhances the momentum, while the Hartmann number
indicates the reverse trend. In addition, it delivers that the proposed machine learning model is capable
of forecasting the physical quantities with lower error (10−3) and is a powerful engineering tool that can
be effectively employed in a viscoinelastic nanofluid.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Machine Learning; Stretching Sheet; Tissue Adhesive; Viscoinelastic Nanofluid 2010 AMS 65G20; 76A05; 76Z99 |
| Divisions: | PSG College of Arts and Science > Department of Mathematics |
| Depositing User: | Dr. B Sivakumar |
| Date Deposited: | 10 Dec 2025 07:00 |
| Last Modified: | 10 Dec 2025 07:00 |
| URI: | https://ir.psgcas.ac.in/id/eprint/2569 |
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