Evidence map›Paper›PMID 41606136›Full record

ArticleScientific reports2026

Minimizing frictional irreversibility in a rough-walled tapered bearing with a nanoparticle-enhanced Sutterby lubricant.

Yousaf Jazza, Hashim, Muhammad Saqib, Saeed Mousa

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yousaf JazzaDepartment of Mechanical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, Saudi Arabia.
HashimDepartment of Mathematics and Statistics, University of Haripur, Haripur, 22620, Pakistan. hashim@uoh.edu.pk.
Muhammad SaqibDepartment of Mathematics and Statistics, University of Haripur, Haripur, 22620, Pakistan.
Saeed MousaMechanical Engineering Department, College of Engineering and Computer Science, Jazan University, Jazan, 706, Saudi Arabia.

Funding

Deanship of Scientific Research, King Khalid University RGP2/423/46.
6 · The paper itself

Abstract

The thermodynamic performance of a tapered roller bearing is governed by the irreversible dissipation within its lubricating film. Minimizing the total entropy generation rate, which arises from fluid friction and heat transfer, is paramount for enhancing mechanical efficiency and operational longevity. This study conducts a numerical investigation into the entropy optimization of a nanoparticle-enhanced Sutterby lubricant within a rough-walled, tapered bearing geometry. The flow is modeled using the continuity, momentum, and energy equations, coupled with an entropy transport equation. The non-Newtonian lubricant behavior is characterized by the Sutterby fluid model, with its shear-thinning intensity governed by the Weissenberg number ([Formula: see text]). The analysis focuses on the influence of key dimensionless parameters: the Reynolds number, the Hartmann number for magneto-hydrodynamic effects, and a defined surface roughness parameter. The analysis demonstrates that the total entropy generation, comprising frictional and thermal contributions is highly sensitive to the converging-diverging geometry. It is found that increasing the Weissenberg number significantly reduces the Bejan number, indicating a shift from thermal to viscous flow irreversibility dominance. Furthermore, the nanoparticle volume fraction directly enhances thermal transport, reducing the temperature gradient contribution to entropy generation. Optimal conditions for minimizing entropy are identified for specific combinations of Reynolds number, Weissenberg number and surface roughness. The results provide a rigorous thermodynamic framework for optimizing tapered bearing performance. By quantifying the interplay between rheology, inertia, and surface texture on the entropy generation map, this work establishes design criteria for minimizing irreversibility. This enables the development of high-efficiency lubrication systems utilizing advanced non-Newtonian nano lubricants.

Indexed as

Convergent/divergent channelsFrictional irreversibilityHeat transportNumerical solutionsRough walls

Identifiers

PMID41606136
PMCPMC12909880

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.