View in EDS

Enhancing thermal efficiency in rotating third-grade fluids: A local nonsimilarity framework with ANN-based entropy analysis.

Saved in:
Bibliographic Details
Title: Enhancing thermal efficiency in rotating third-grade fluids: A local nonsimilarity framework with ANN-based entropy analysis.
Authors: Malik, Iqra Nasir1 (AUTHOR), Mustafa, M.1 (AUTHOR) meraj_mm@hotmail.com
Source: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 3/10/2026, Vol. 40 Issue 6, p1-27. 27p.
Subjects: Rotational flow, Artificial neural networks, Viscoelastic materials, Energy conversion, Nonequilibrium thermodynamics, Pseudoplastic fluids, Energy dissipation
Abstract: The third-grade viscoelastic model, formulated within the Rivlin–Ericksen constitutive framework, offers a distinct advantage over other models by simultaneously capturing normal stress differences and shear-thinning behavior in nonlinear fluids. This work applies third-grade fluid model to investigate viscoelastic rotating flow with heat dissipation effects over a stretchable surface, highlighting the combined impact of rotation, viscous dissipation and entropy generation, which has not been addressed in prior studies. The involvement of third-grade fluid's stresses generates nonsimilar terms in both momentum and heat transfer equations. Compared with the prior studies conducted for nonrotating frame, this work reports a locally nonsimilar analysis wherein the derivatives along the streamwise direction are retained. Irreversibility effects in the model are further scrutinized by evaluating entropy generation rate and its dependence on fluid's rheological properties. A neural network framework trained with the Levenberg–Marquardt (LM) algorithm is also applied to predict entropy rates and Bejan number. Multiple validation metrics including regression plot (RP), mean squared error (MSE) and histograms are used to demonstrate the reliability of artificial neural network (ANN) based forecasts. The results obtained from the ANN closely align with those from the bvp4c method, exhibiting an exceptionally low mean absolute error. The flow fields and associated fluid dynamic characteristics are analyzed under varying rotation rates, elasticity parameters and shear-thinning effects. This study reveals that entropy generation intensifies near the wall when the temperature difference increases, primarily due to stronger thermal gradients. As viscous dissipation intensifies, more mechanical energy transforms into heat and the associated entropy generation rate becomes progressively higher. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied 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.)
Database: Engineering Source
Description
Abstract:The third-grade viscoelastic model, formulated within the Rivlin–Ericksen constitutive framework, offers a distinct advantage over other models by simultaneously capturing normal stress differences and shear-thinning behavior in nonlinear fluids. This work applies third-grade fluid model to investigate viscoelastic rotating flow with heat dissipation effects over a stretchable surface, highlighting the combined impact of rotation, viscous dissipation and entropy generation, which has not been addressed in prior studies. The involvement of third-grade fluid's stresses generates nonsimilar terms in both momentum and heat transfer equations. Compared with the prior studies conducted for nonrotating frame, this work reports a locally nonsimilar analysis wherein the derivatives along the streamwise direction are retained. Irreversibility effects in the model are further scrutinized by evaluating entropy generation rate and its dependence on fluid's rheological properties. A neural network framework trained with the Levenberg–Marquardt (LM) algorithm is also applied to predict entropy rates and Bejan number. Multiple validation metrics including regression plot (RP), mean squared error (MSE) and histograms are used to demonstrate the reliability of artificial neural network (ANN) based forecasts. The results obtained from the ANN closely align with those from the bvp4c method, exhibiting an exceptionally low mean absolute error. The flow fields and associated fluid dynamic characteristics are analyzed under varying rotation rates, elasticity parameters and shear-thinning effects. This study reveals that entropy generation intensifies near the wall when the temperature difference increases, primarily due to stronger thermal gradients. As viscous dissipation intensifies, more mechanical energy transforms into heat and the associated entropy generation rate becomes progressively higher. [ABSTRACT FROM AUTHOR]
ISSN:02179792
DOI:10.1142/S0217979226500554