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Chinnasamy P, Sivajothi R, Sathish S, Abbas M, Jeyakrishnan V, Goel R, Alqahtani MS, Loganathan K. Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application. Sci Rep 2024; 14:555. [PMID: 38177235 PMCID: PMC10767104 DOI: 10.1038/s41598-023-49480-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/08/2023] [Indexed: 01/06/2024] Open
Abstract
Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg-Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.
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Affiliation(s)
- P Chinnasamy
- Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
| | - R Sivajothi
- Department of Management, R L Institute of Management Studies (A Unit of Subbalakshmi Lakshmipathy College of Science), Madurai, Tamil Nadu, India
| | - S Sathish
- Department of Mathematics, School of Science, National Institute of Technology, Tadepalligudem, Andhra Pradesh, India
| | - Mohamed Abbas
- Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia
| | - V Jeyakrishnan
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Rajat Goel
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, 61421, Abha, Saudi Arabia
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK
| | - K Loganathan
- Department of Mathematics and Statistics, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India.
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