Effect of Nanoparticle Diameter in Maxwell Nanofluid Flow with Thermophoretic Particle Deposition

Pudhari Srilatha, Hanaa Abu-Zinadah, Ravikumar Shashikala Varun Kumar, M. D. Alsulami, Rangaswamy Naveen Kumar, Amal Abdulrahman and Ramanahalli Jayadevamurthy Punith Gowda ()
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Pudhari Srilatha: Department of Mathematics, Institute of Aeronautical Engineering, Hyderabad 500043, India
Hanaa Abu-Zinadah: Department of Statistics, College of Science, University of Jeddah, Jeddah 21931, Saudi Arabia
Ravikumar Shashikala Varun Kumar: Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India
M. D. Alsulami: Department of Mathematics, College of Sciences and Arts at Alkamil, University of Jeddah, Jeddah 21931, Saudi Arabia
Rangaswamy Naveen Kumar: Department of Mathematics, Dayananda Sagar College of Engineering, Bangalore 560078, India
Amal Abdulrahman: Department of Chemistry, College of Science, King Khalid University, Abha 61421, Saudi Arabia
Ramanahalli Jayadevamurthy Punith Gowda: Department of Mathematics, Bapuji Institute of Engineering and Technology, Davanagere 577004, India

Mathematics, 2023, vol. 11, issue 16, 1-23

Abstract: The time-dependent Maxwell nanofluid flow with thermophoretic particle deposition is examined in this study by considering the solid–liquid interfacial layer and nanoparticle diameter. The governing partial differential equations are reduced to ordinary differential equations using suitable similarity transformations. Later, these reduced equations are solved using Runge–Kutta–Fehlberg’s fourth and fifth-order method via a shooting approach. An artificial neural network serves as a surrogate model, making quick and precise predictions about the behaviour of nanofluid flow for various input parameters. The impact of dimensionless parameters on flow, heat, and mass transport is determined via graphs. The results reveal that the velocity profile drops with an upsurge in unsteadiness parameter values and Deborah number values. The rise in space and temperature-dependent heat source/sink parameters value increases the temperature. The concentration profile decreases as the thermophoretic parameter upsurges. Finally, the method’s correctness and stability are confirmed by the fact that the maximum number of values is near the zero-line error. The zero error is attained near the values 2.68 × 10 − 6 , 2.14 × 10 − 9 , and 8.5 × 10 − 7 for the velocity, thermal, and concentration profiles, respectively.

Keywords: Maxwell fluid; neural network; unsteady flow; thermophoretic particle deposition; interfacial layer and nanoparticle diameter (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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