Volume 105, Issue 2 e202200528
ORIGINAL PAPER

Sensitivity evaluation of Brownian motion and chemical reaction parameter on transport of motile gyrotactic microorganism in nanofluid over a wedge

Dilawar Hussain

Dilawar Hussain

Laboratory of Aerospace Entry Descent and Landing Technology, College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China

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Zaheer Asghar

Zaheer Asghar

Centre for Mathematical Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan

Centre for Physics and Applied Mathematics, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan

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Ahmed Zeeshan

Corresponding Author

Ahmed Zeeshan

Department of Mathematics & Statistics, FoS, International Islamic University, H-10, Islamabad, Pakistan

Department of Mathematics, College of Science, Korea University, Seongbuk-gu, Seoul, Republic of Korea

Correspondence

Ahmed Zeeshan, Department of Mathematics & Statistics, FoS, International Islamic University Islamabad, H-10, Islamabad 44000, Pakistan.

Email: [email protected]

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Usman Masud

Usman Masud

PIMSATS, Karachi, Pakistan

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First published: 17 December 2024
Citations: 1

Abstract

In any natural flow, there are different factors that influence the movement of microorganisms in fluid; some factors are significant while others are not. In this article, authors want to analyse the sensitivity of the movement of motile gyrotactic microorganisms to variation in Schmidt number (Sc), chemical reaction parameter (Kr), and Brownian motion parameter (Nb). For this purpose, a PDE is transformed into ODEs by using appropriate similarity transformation. The transformed ODEs are solved numerically by using MATLAB's built-in software bvp4c. Then, by using these numerical results, we have developed a correlation between input variables and output responses for the Sherwood number ( S h x $S{h}_x$ ) and density number ( S n x $S{n}_x$ ) by using response surface methodology (RSM). The residual for output responses is plotted in graphical form and shows strong correlations and best-fitted models between input parameters and output responses. Finally, the sensitivity analysis is performed and displays the results in graphical form and concludes that the input factor Sc is the most sensitive to S h x $S{h}_x$ for lower cases of A and Kr is the most sensitive among other parameters for middle and higher cases of B.

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