Affiliation:
1. Department of Mechanical Engineering, PES University, Bangalore 560085, India
Abstract
Abstract
Modeling of turbulence heat transfer for supercritical fluids, using computational fluid dynamics (CFD) software, is always challenging due to the drastic property variations near the critical point. The use of artificial neural networks (ANNs) along with numerical methods has shown promising results in predicting heat transfer coefficients of heat exchangers. In this study, the accuracy of four different turbulent models available in the commercial CFD software—ansysfluent is investigated against the available experimental results. The k–ε Re-normalization group (RNG) model, with enhanced wall treatment, is found to be the best-suited turbulence model. Further, K–ε RNG turbulence model is used in CFD for parametric analysis to generate the data for ANN studies. A total of 1,34,698 data samples was generated and fed into the ANN program to develop an equation that can predict the heat transfer coefficient. It was found that, for the considered range of values, the absolute average relative deviation is 3.49%.
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science
Reference27 articles.
1. Heat Transfer of Supercritical Carbon Dioxide in Printed Circuit Heat Exchanger Geometries;ASME J. Therm. Sci. Eng. Appl.,2011
2. Based Modeling on the Turbulent Convection Heat Transfer of Supercritical CO2 in the Printed Circuit Heat Exchangers for the Supercritical CO2 Brayton Cycle;Int. J. Heat Mass Transfer,2016
3. Supercritical CO2 as Heat Transfer Fluid: A Review;Appl. Thermal Eng.,2017
4. Heat Transfer to Supercritical Fluids Flowing in Channels—Empirical Correlations (Survey);Nucl. Eng. Des.,2004
5. Review of Supercritical CO2 Power Cycle Technology and Current Status of Research and Development;Nucl. Eng. Technol.,2015
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