JOURNAL ARTICLE

Mathematical modeling and analysis of non-Newtonian Rabinowitsch fluid flow in an elliptical duct: Insights into pseudoplastic and dilatant behaviors.

  • Published In: Modern Physics Letters B, 2025, v. 39, n. 30. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Haider, Jamil Abbas; Alrihieli, Haifaa F.; Alnfiai, M. M.; Hussien, Mohamed 3 of 3

Abstract

This research introduces a novel mathematical model for the peristaltic flow of non-Newtonian Rabinowitsch fluid within an elliptical duct, uniquely capturing both pseudoplastic and dilatant behaviors. By employing Cartesian coordinates with elliptical boundary conditions, the model preserves the duct's geometric integrity. The resulting complex partial differential equations, though challenging, were solved exactly using dimensional analysis and scaling methods. Additionally, perturbation techniques were utilized to thoroughly analyze the flow dynamics. Comprehensive graphical analyses depict key characteristics such as dimensionless velocity, axial pressure gradient, and pressure rise, offering fresh insights into Rabinowitsch fluid behavior in elliptical geometries. The findings reveal that an increase in volumetric flow rate significantly enhances central velocity in the duct, particularly for pseudoplastic fluids, while dilatant fluids exhibit reduced velocity under similar conditions. Notably, the pressure gradient demonstrates distinct patterns, with dilatant fluids showing oscillatory fluctuations, underscoring the limitations of Newtonian models in accurately representing these complex fluid dynamics. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Modern Physics Letters B. 2025/10, Vol. 39, Issue 30, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2025
  • ISSN:0217-9849
  • DOI:10.1142/S0217984925501738
  • Accession Number:186449827
  • Copyright Statement:Copyright of Modern Physics Letters B is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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