A Machine Learning Approach to Correlation Development Applied to Fin-Tube Bundle Heat Exchangers

Karl Lindqvist, Zachary T. Wilson, Erling Næss and Nikolaos V. Sahinidis
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Karl Lindqvist: Department of Energy and Process Engineering, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
Zachary T. Wilson: Department of Chemical Engineering, Carnegie Mellon University, Pittsburg, PA 15213, USA
Erling Næss: Department of Energy and Process Engineering, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
Nikolaos V. Sahinidis: Department of Chemical Engineering, Carnegie Mellon University, Pittsburg, PA 15213, USA

Energies, 2018, vol. 11, issue 12, 1-16

Abstract: Heat exchanger designers need reliable thermal-hydraulic correlations to optimize heat exchanger designs. This work combines an adaptive sampling method with a Computational Fluid Dynamics (CFD) simulator to obtain increased accuracy and validity range of heat transfer and pressure drop predictions using a limited number of data points. Correlation efficacy was evaluated based on a steam generator case study. The sensitivity to the design parameters was analyzed in detail. The RMSE (root mean square error) of the developed correlations were reduced, through CFD sampling, from 28% to 15% for pressure drop, and from 33% to 25% heat transfer, compared to regression on experimental data only. The best reference correlations have RMSE values of 43% and 33% on pressure drop and heat transfer, respectively, on an independent validation set. Indeed, a radically different fin-tube geometry was suggested for the case study, compared to results using the Escoa correlations.The developed correlations show good to excellent agreement with trends in the CFD model. The quantitative error of predicted heat transfer and pressure drop coefficients at the case study optimum, however, was as large as those of the Escoa correlations. More data are likely needed to improve accuracy for compact heat exchanger designs further.

Keywords: numerical modeling; surrogate model; correlation; fin-tube; spiral fin-tube; CFD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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