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Entropy Generation Assessment for Wall-Bounded Turbulent Shear Flows Based on Reynolds Analogy Assumptions

1
Civil and Mechanical Engineering Department, School of Computing and Engineering, University of Missouri-Kansas City, Kansas City, MO 64110, USA
2
School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
3
Department of Mechanical Engineering, Institute of Energy and Power Plant Technology, Technische Universität Darmstadt, 64289 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(12), 1157; https://doi.org/10.3390/e21121157
Received: 7 October 2019 / Revised: 11 November 2019 / Accepted: 19 November 2019 / Published: 26 November 2019
Heat transfer modeling plays a major role in design and optimization of modern and efficient thermal-fluid systems. Further, turbulent flows are thermodynamic processes, and thus, the second law of thermodynamics can be used for critical evaluations of such heat transfer models. However, currently available heat transfer models suffer from a fundamental shortcoming: their development is based on the general notion that accurate prediction of the flow field will guarantee an appropriate prediction of the thermal field, known as the . In this work, an assessment of the capability of the in predicting turbulent heat transfer when applied to shear flows of fluids of different Prandtl numbers will be given. Towards this, a detailed analysis of the predictive capabilities of the concerning entropy generation is presented for steady and unsteady state simulations. It turns out that the provides acceptable results only for mean entropy generation, while fails to predict entropy generation at small/sub-grid scales. View Full-Text
Keywords: Reynolds Analogy; entropy generation; steady/unsteady calculations Reynolds Analogy; entropy generation; steady/unsteady calculations
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Ziefuss, M.; Karimi, N.; Ries, F.; Sadiki, A.; Mehdizadeh, A. Entropy Generation Assessment for Wall-Bounded Turbulent Shear Flows Based on Reynolds Analogy Assumptions. Entropy 2019, 21, 1157.

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