Impact of solid-fluid interfacial layer and nanoparticle diameter on Maxwell nanofluid flow subjected to variable thermal conductivity and uniform magnetic field.

Autor:	Srilatha P; Department of Mathematics, Institute of Aeronautical Engineering, Hyderabad, India., Kumar RSV; Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India., Kumar RN; Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, Karnataka, India., Gowda RJP; Department of Mathematics, Bapuji Institute of Engineering & Technology, Davanagere, 577004, Karnataka, India., Abdulrahman A; Department of Chemistry, College of Science, King Khalid University, Abha, 61421, Saudi Arabia., Prasannakumara BC; Department of Studies and Research in Mathematics, Davangere University, Shivagangotri, Davangere, 577002, Karnataka, India.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2023 Oct 23; Vol. 9 (11), pp. e21189. Date of Electronic Publication: 2023 Oct 23 (Print Publication: 2023).
DOI:	10.1016/j.heliyon.2023.e21189
Abstrakt:	The utilization of Maxwell fluid with nanoparticle suspension exhibits promising prospects in enhancing the efficacy of energy conversion and storage mechanisms. They have the potential to be utilized in sophisticated cooling systems for power generation facilities, thereby augmenting the overall energy efficacy. Keeping this in mind, the current research examines the Maxwell nanofluid flow over a rotating disk with the impact of a heat source/sink. The present study centers on the examination of flow characteristics in the existence of a uniform magnetic field. The conversion of governing equations into ordinary differential equations is achieved using appropriate similarity variables. To derive the Nusselt number ( Nu ) and skin friction (SF) model related to the flow and temperature parameters, the suggested back-propagation artificial neural networking (ANN) technique is used. The Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) method is used to solve the reduced equations and produce the necessary data to create the Nu and SF model. Both the Nu and SF models require 1000 data for training the network, respectively. Graphs are utilized to communicate numerical outcomes. The results concluded that the upsurge in magnetic parameter drops the velocity profile but advances the heat transport. Rise in the thermal conductivity parameter, increases the heat transport. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2023 The Authors.)
Databáze:	MEDLINE
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