Мақола

Prabhakar fractional approach for enhancement of heat transfer due to hybrid nanomaterial with sinusoidal heat conditions

Shuhe SunSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, ChinaM. Ijaz KhanDepartment of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, LebanonKamel Al‐KhaledDepartment of Mathematics & Statistics, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110, JordanAli RazaDepartment of Mathematics, Minhaj University Lahore, PakistanSherzod AbdullaevDepartment of Science and Innovation, Tashkent State Pedagogical University named after Nizami, Bunyodkor street 27, Tashkent, UzbekistanSami Ullah KhanDepartment of Mathematics, Namal University, Mianwali, 42250, PakistanNissren TamamDepartment of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi ArabiaSayed M. EldinCenter of Research, Faculty of Engineering, Future University in Egypt, New Cairo, 11835, Egypt

Case Studies in Thermal Engineeringjournal2023en

ABI

Аннотация

The thermal improvement in heat transfer investigation using a hybrid nanofluid model is investigated. In a class of hybrid nanofluid, aluminum oxide, and titanium oxide nanoparticles are used. Kerosene oil and water decomposition are used in hybrid nanoparticles. The analysis is carried out for sinusoidal boundary constraints. Moreover, the current phenomenon's gravitational impact and magnetic force features are disclosed. The porous inclined surface, which moves with a sinusoidal pattern, endorsed the flow. The dimensionless system with suitable variables is achieved. The fractional framework with generalized Prabhakar model is used for the solution procedure. The basic concept of the fractional approach is presented. The physical contribution of parameters is observed for analyzing the thermal phenomenon.

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Мавзулар

Nanofluid Flow and Heat Transfer Fractional Differential Equations Solutions Heat Transfer Mechanisms

Идентификаторлар

DOI: 10.1016/j.csite.2023.103240

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