Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks

Barroso-Maldonado, J. M.; Belman-Flores, J. M.; Ledesma, S.; Aceves, S. M.

doi:10.1016/j.cryogenics.2018.04.005

Prediction of heat transfer coefficients for forced convective boiling of N₂-hydrocarbon mixtures at cryogenic conditions using artificial neural networks

Journal Article · Wed Apr 11 00:00:00 EDT 2018 · Cryogenics

DOI:https://doi.org/10.1016/j.cryogenics.2018.04.005· OSTI ID:1558864

Barroso-Maldonado, J. M. ^[1]; Belman-Flores, J. M. ^[1]; Ledesma, S. ^[1]; Aceves, S. M. ^[2]

Univ. of Guanajuato, Salamanca (Mexico)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

A key problem faced in the design of heat exchangers, especially for cryogenic applications, is the determination of convective heat transfer coefficients in two-phase flow such as condensation and boiling of non-azeotropic refrigerant mixtures. Here, this paper proposes and evaluates three models for estimating the convective coefficient during boiling. These models are developed using computational intelligence techniques. The performance of the proposed models is evaluated using the mean relative error (mre), and compared to two existing models: the modified Granryd’s correlation and the Silver-Bell-Ghaly method. The three proposed models are distinguished by their architecture. The first is based on directly measured parameters (DMP-ANN), the second is based on equivalent Reynolds and Prandtl numbers (eq-ANN), and the third on effective Reynolds and Prandtl numbers (eff-ANN). In conclusion, the results demonstrate that the proposed artificial neural network (ANN)-based approaches greatly outperform available methodologies. While Granryd's correlation predicts experimental data within a mean relative error mre = 44% and the S-B-G method produces mre = 42%, DMP-ANN has mre = 7.4% and eff-ANN has mre = 3.9%. Considering that eff-ANN has the lowest mean relative error (one tenth of previously available methodologies) and the broadest range of applicability, it is recommended for future calculations. Implementation is straightforward within a variety of platforms and the matrices with the ANN weights are given in the appendix for efficient programming.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1558864

Alternate ID(s):: OSTI ID: 1775714

Report Number(s):: LLNL-JRNL--745595; 899897

Journal Information:: Cryogenics, Journal Name: Cryogenics Journal Issue: C Vol. 92; ISSN 0011-2275

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited By (1)

Investigation of boiling heat transfer coefficients of different refrigerants for low fin, Turbo-B and Thermoexcel-E enhanced tubes using computational smart schemes Mehralizadeh, Afsaneh; Shabanian, Seyed Reza; Bakeri, Gholamreza Journal of Thermal Analysis and Calorimetry, Vol. 141, Issue 3 https://doi.org/10.1007/s10973-019-09075-x	journal	November 2019

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Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
Artificial intelligence
Boiling
Heat exchangers
cryogenics
Heat transfer coefficient