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Entropy–heat transfer coupling in vibrational non-Newtonian nanofluid flow.

  • Published In: Pramana: Journal of Physics, 2026, v. 100, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Tripure, Amrita; Sao, Manoj; Mishra, Santosh Kumar; Nagpal, Shubhrata; Singh, Pushpendra 3 of 3

Abstract

This study examines the combined effects of heat transfer and entropy generation in non-Newtonian nanofluid flow subjected to mechanical vibration within a cylindrical pipe. Using the volume of fluid method, the impacts of vibrational parameters—amplitude, frequency and Reynolds number—are analysed under two thermal boundary conditions: constant heat flux (HF) and constant wall temperature (WT). While Vibration boosts convective heat transfer through increased radial mixing and flow instability, it also influences entropy Generation by changing the balance between thermal and viscous irreversibility. At a frequency of 20 Hz and an amplitude of 5 mm, the entropy generation rate decreased from 0.58 to 0.28 under WT conditions, indicating enhanced thermodynamic performance. A sensitivity analysis shows amplitude as the most influential parameter affecting both heat and entropy transport. The results demonstrate that selecting optimal vibrational parameters can simultaneously improve heat transfer and reduce irreversibility, providing a second-law-based approach for designing energy-efficient thermal systems with nanofluids. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Pramana: Journal of Physics. 2026/03, Vol. 100, Issue 1, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2026
  • ISSN:0304-4289
  • DOI:10.1007/s12043-025-03047-7
  • Accession Number:191498060
  • Copyright Statement:Copyright of Pramana: Journal of Physics is the property of Springer Nature 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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