JOURNAL ARTICLE
Fractal modeling of non-integer Newtonian fluid through comparison of Sumudu and Laplace transforms.
Published In: International Journal of Geometric Methods in Modern Physics, 2025, v. 22, n. 5. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Akhund, Amarah; Abro, Kashif Ali 3 of 3
Abstract
The exploration for knowing the complexity of fractal patterns (Koch snowflake, Julia shape, Chaos pro, Sierpinski triangle and Mandelbrot shape) among the rheological fluid models enables self-similar structures at different scales. In this paper, a fractal–fractional modeling of Newtonian fluid flow is developed for the velocity field by invoking power law kernel. In order to envisage the distinct computational characteristics and applications, the comparative analysis for the Sumudu transform and Laplace transform is investigated for fractal–fractional model of Newtonian fluid under exact analytical solutions. By employing fractal versus non-fractal parameters, analytical solutions for fractal–fractional model of Newtonian fluid by means of fractal Sumudu transform and fractal Laplace transform have been deduced by substituting Δ 1 = Δ 1 = 1. The sensitivity analysis for fractal–fractional model of Newtonian fluid is also emphasized for the sake of rheological phenomenon via multiple regression, Pearson correlation and probable error. Our results suggest that variations of viscosity depicted the behavior of shear stress on Newtonian fluid and predicted the complex motion under resistance of Newtonian fluid. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Geometric Methods in Modern Physics. 2025/04, Vol. 22, Issue 5, p1
- Document Type:Article
- Subject Area:Mathematics
- Publication Date:2025
- ISSN:0219-8878
- DOI:10.1142/S0219887824503286
- Accession Number:185202893
- Copyright Statement:Copyright of International Journal of Geometric Methods in Modern Physics 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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