Mutual Interdependence of the Physical Parameters Governing the Boundary-Layer Flow of Non-Newtonian Fluids
Al-Ashhab S. Wei D. Alyami S.A. Azad A.K.M. Moni M.A.
May-2 2022MDPI
Applied Sciences (Switzerland)
2022#12Issue 10
We consider non-Newtonian boundary-layer fluid flow, governed by a power-law Ostwald-de Waele rheology. Boundary-layer flows of non-Newtonian fluids have far-reaching applications, and are very frequently encountered in physical, as well as, engineering and industrial processes. A similarity transformation results in a BVP consisting of an ODE and some boundary conditions. Our aim is to derive highly accurate analytical relationships between the physical and mathematical parameters associated with the BVP and boundary-layer flow problem. Mathematical analyses are employed, where the results are verified at the numerical computational level, illustrating the accuracy of the derived relations. A set of “Crocco variables” is used to transform the problem, and, where appropriate, techniques are used to deal with the resulting singularities in order to establish an efficient computational setting. The resulting computational setting provides an alternative, which is different from those previously used in the literature. We employ it to carry out our numerical computations.
boundary-layer flow , boundary-value problem , non-linear , non-Newtonian fluid , power-law model , semi-infinite domain , singularity
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Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 13318, Saudi Arabia
Department of Mathematics, Nazarbayev University, 53 Kabanbay Batyr Ave, Astana, 010000, Kazakhstan
Faculty of Science, Engineering & Technology, Swinburne University of Technology, Sydney, 2150, NSW, Australia
ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, 2145, NSW, Australia
Artificial Intelligence & Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, 4072, QLD, Australia
Department of Mathematics and Statistics
Department of Mathematics
Faculty of Science
ProCan®
Artificial Intelligence & Digital Health Data Science
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