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Thermal performance of Ag–water nanofluid flow over a curved surface due to chemical reaction using Buongiorno's model
Author(s) -
Mishra Ashish,
Pandey Alok,
Kumar Manoj
Publication year - 2021
Publication title -
heat transfer
Language(s) - English
Resource type - Journals
eISSN - 2688-4542
pISSN - 2688-4534
DOI - 10.1002/htj.21875
Subject(s) - nanofluid , thermophoresis , curvature , mechanics , dimensionless quantity , shooting method , heat generation , brownian motion , heat transfer , nonlinear system , materials science , thermodynamics , lewis number , chemistry , mass transfer , boundary value problem , physics , mathematics , mathematical analysis , geometry , statistics , quantum mechanics
An analysis of heat and mass transfer is carried out under the influence of chemical reaction, friction heating, and heat generation/absorption over a curved surface. The impacts of random motion attributes of nanoparticles and thermophoresis are also applied in the expressions of energy and concentration. With the help of assigned transformations, the nonlinear partial differential equations are changed to dimensionless nonlinear ordinary differential equations. Then, the numerical solution is obtained using fourth‐fifth order Runge‐Kutta‐Fehlberg method via the shooting technique. The impacts of relevant parameters on velocity, temperature, and concentration are depicted through graphs and tables. The results illustrate that the lowest concentration distribution of nanofluid is related to the higher value of chemical reaction parameter. Moreover, it is found that thermophoresis and Brownian motion parameters have a propensity to increase the temperature profile while curvature parameter decreases the velocity profile. Also, velocity and temperature fields show a similar behavior for the increasing values of volume fraction of the nanoparticles, while a reverse trend is observed in the concentration profile under the same condition. To authenticate the results of the current study, the obtained data were compared with previously published data.