Analytical analysis of the double stratification on Casson nanofluid over an exponential stretching sheet
Case Studies in Thermal Engineering, ISSN: 2214-157X, Vol: 50, Page: 103492
2023
- 9Citations
- 4Captures
- 1Mentions
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Saveetha School of Engineering Researchers Report on Findings in Nanofluids (Analytical analysis of the double stratification on Casson nanofluid over an exponential stretching sheet)
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Article Description
Nanofluids have shown tremendous potential in the heat intensification of several manufacturing industries and have been utilized extensively in energy technologies in recent years. The goal of this exploratory paper is to examine the impact of double stratification on steady two-dimensional MHD Casson nanofluid flow over an exponentially porous stretching sheet. Thermophoresis and Brownian impact of the nanofluid model is described. In addition, the influence of joule heating, viscous dissipation, and heat generation are considered. The governing partial differential equations are converted into nonlinear coupled ordinary differential equations using similarity transformation, and they are then resolved using the Homotopy analysis method. The established nonlinear expressions have been solved analytically using the homotopic concept. Graphs demonstrate the influence of non-dimensional limitations on fluid momentum, thermal, and concentration. The drag force coefficient factor, Nusselt number and Sherwood number for various controlled factors on the Casson nanofluid properties are examined and tabulated. Researchers interested in the thermal science of liquid drifts are drawn to the mutual interaction of double stratification in MHD drift regimes since it affects various thermal engineering viewpoints in daily life.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2214157X23007980; http://dx.doi.org/10.1016/j.csite.2023.103492; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85171784982&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S2214157X23007980; https://dx.doi.org/10.1016/j.csite.2023.103492
Elsevier BV
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