JOURNAL ARTICLE

Mathematical modeling of electroosmotic flow and thermal transport in stenotic tapered arteries using finite difference method.

  • Published In: Modern Physics Letters B, 2025, v. 39, n. 25. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Haider, Jamil Abbas; Ahmad, Shahbaz; Nadeem, Sohail 3 of 3

Abstract

This study investigates the electroosmotic flow and thermal transport of nanofluids, specifically aluminum oxide and titanium dioxide, within tapered arteries with stenosis. Using the finite difference method (FDM), we modeled the flow dynamics and heat transfer mechanisms under the assumption of low Reynolds numbers and mild stenosis. The study focuses on the effects of nanoparticle volume fraction, heat absorption parameters, and electroosmotic forces on blood flow, velocity profiles, and temperature distribution. Results show that increasing nanoparticle concentration and heat absorption significantly reduce flow velocity, particularly in divergent artery geometries. Additionally, the inclusion of electroosmotic effects enhances heat transfer, while the Grashof number influences central velocity. These findings offer valuable insights into optimizing nanoparticle-based therapeutic interventions for cardiovascular treatments, providing a framework for enhancing drug delivery systems and improving the efficacy of heat-based therapies. The study's outcomes could lead to improved diagnostic tools and therapeutic strategies for managing cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Modern Physics Letters B. 2025/09, Vol. 39, Issue 25, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2025
  • ISSN:0217-9849
  • DOI:10.1142/S0217984925501179
  • Accession Number:185308973
  • 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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