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Title: Prediction of heat transfer coefficients for forced convective boiling of N 2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks

Abstract

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 futuremore » calculations. Implementation is straightforward within a variety of platforms and the matrices with the ANN weights are given in the appendix for efficient programming.« less

Authors:
 [1];  [1];  [1];  [2]
  1. Univ. of Guanajuato, Salamanca (Mexico)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1558864
Report Number(s):
[LLNL-JRNL-745595]
[Journal ID: ISSN 0011-2275; 899897]
Grant/Contract Number:  
[AC52-07NA27344]
Resource Type:
Accepted Manuscript
Journal Name:
Cryogenics
Additional Journal Information:
[ Journal Volume: 92; Journal Issue: C]; Journal ID: ISSN 0011-2275
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 97 MATHEMATICS AND COMPUTING; Heat transfer coefficient; Boiling; Heat exchangers, cryogenics; Artificial intelligence

Citation Formats

Barroso-Maldonado, J. M., Belman-Flores, J. M., Ledesma, S., and Aceves, S. M. Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. United States: N. p., 2018. Web. doi:10.1016/j.cryogenics.2018.04.005.
Barroso-Maldonado, J. M., Belman-Flores, J. M., Ledesma, S., & Aceves, S. M. Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks. United States. doi:10.1016/j.cryogenics.2018.04.005.
Barroso-Maldonado, J. M., Belman-Flores, J. M., Ledesma, S., and Aceves, S. M. Wed . "Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks". United States. doi:10.1016/j.cryogenics.2018.04.005. https://www.osti.gov/servlets/purl/1558864.
@article{osti_1558864,
title = {Prediction of heat transfer coefficients for forced convective boiling of N2-hydrocarbon mixtures at cryogenic conditions using artificial neural networks},
author = {Barroso-Maldonado, J. M. and Belman-Flores, J. M. and Ledesma, S. and Aceves, S. M.},
abstractNote = {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.},
doi = {10.1016/j.cryogenics.2018.04.005},
journal = {Cryogenics},
number = [C],
volume = [92],
place = {United States},
year = {2018},
month = {4}
}

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