Browse > Article
http://dx.doi.org/10.1016/j.net.2021.12.023

Application of the machine learning technique for the development of a condensation heat transfer model for a passive containment cooling system  

Lee, Dong Hyun (School of Mechanical Engineering, Pusan National University)
Yoo, Jee Min (School of Mechanical Engineering, Pusan National University)
Kim, Hui Yung (School of Mechanical Engineering, Pusan National University)
Hong, Dong Jin (School of Computer Science and Engineering, Pusan National University)
Yun, Byong Jo (School of Mechanical Engineering, Pusan National University)
Jeong, Jae Jun (School of Mechanical Engineering, Pusan National University)
Publication Information
Nuclear Engineering and Technology / v.54, no.6, 2022 , pp. 2297-2310 More about this Journal
Abstract
A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.
Keywords
PCCS; Condensation heat transfer; Non-condensable gas; Machine learning;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S. Azizi, E. Ahmadloo, Prediction of heat transfer coefficient during condensation of R134a in inclined tubes using artificial neural network, Appl. Therm. Eng. 106 (2016) 203-210.   DOI
2 D.E. Rumelhart, J.L. McClelland, Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Vol. 1: Foundations, MIT Press. (1986) 547-611.
3 E. Heidari, M.A. Sobati, S. Movahedirad, Accurate prediction of nanofluid viscosity using a multi-layer perceptron artificial neural network (MLP-ANN), Chemometr. Intell. Lab. Syst. 155 (2016) 73-85.   DOI
4 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.
5 D. Ma, T. Zhou, J. Chen, S. Qi, M.A. Shahzad, Z. Xiao, Supercritical water heat transfer coefficient prediction analysis based on BP neural network, Nucl. Eng. Des. 320 (2017) 400-408.   DOI
6 G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700-4708.
7 A. Baghban, M. Kahani, M.A. Nazari, M.H. Ahmadi, W.M. Yan, Sensitivity analysis and application of machine learning methods to predict the heat transfer performance of CNT/water nanofluid flows through coils, Int. J. Heat Mass Tran. 128 (2019) 825-835.   DOI
8 K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv preprint arXiv:1409, 2014, p. 1556.
9 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.
10 G.M. Hobold, A.K. da Silva, Visualization-based nucleate boiling heat flux quantification using machine learning, Int. J. Heat Mass Tran. 134 (2019b) 511-520.   DOI
11 Y. Suh, R. Bostanabad, Y. Won, Deep learning predicts boiling heat transfer, Sci. Rep. 11 (1) (2021) 1-10.   DOI
12 B.G. Jeon, H.C. No, Conceptual design of passive containment cooling system with air holdup tanks in the concrete containment of improved APR+, Nucl. Eng. Des. 267 (2014) 180-188.   DOI
13 H. Wei, G.H. Su, S.Z. Qiu, W. Ni, X. Yang, Applications of genetic neural network for prediction of critical heat flux, Int. J. Therm. Sci. 49 (1) (2010) 143-152.   DOI
14 A.A. Dehbi, A generalized correlation for steam condensation rates in the presence of air under turbulent free convection, Int. J. Heat Mass Tran. 86 (2015) 1-15.   DOI
15 P.F. Peterson, V.E. Schrock, T. Kageyama, Diffusion layer theory for turbulent vapor condensation with noncondensable gases, J. Heat Tran. 115 (1993) 998-1003.   DOI
16 Y. Liao, K. Vierow, A generalized diffusion layer model for condensation of vapor with noncondensable gases, J. Heat Tran. 129 (2007) 988-994.   DOI
17 H.M. Park, J.H. Lee, K.D. Kim, Wall temperature prediction at critical heat flux using a machine learning model, Ann. Nucl. Energy 141 (2020) 107334.   DOI
18 J. Hu, L. Shen, G. Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141.
19 J. Kim, C. Lee, Prediction of turbulent heat transfer using convolutional neural networks, J. Fluid Mech. (2020) 882.
20 C.O. Popiel, Free convection heat transfer from vertical slender cylinders: a review, Heat Tran. Eng. 29 (6) (2008) 521-536.   DOI
21 J. Su, Z. Sun, G. Fan, M. Ding, Experimental study of the effect of noncondensable gases on steam condensation over a vertical tube external surface, Nucl. Eng. Des. 262 (2013) 201-208.   DOI
22 U.K. Kim, J.W. Yoo, Y.J. Jang, Y.G. Lee, Measurement of heat transfer coefficients for steam condensation on a vertical 21.5-mm-OD tube in the presence of air, J. Nucl. Sci. Technol. 57 (8) (2020) 905-916.   DOI
23 J.Y. Lee, J.J. Jeong, J.H. Kang, B.J. Yun, Improvement of the condensation heat transfer model of the MARS-KS1. 3 code using a modified diffusion layer model, Prog. Nucl. Energy 108 (2018) 260-269.   DOI
24 A.P. Colburn, O.A. Hougen, Design of cooler condensers for mixtures of vapors with noncondensing gases, Ind. Eng. Chem. 26 (11) (1934) 1178-1182.   DOI
25 J.W. Kim, Y.G. Lee, H.K. Ahn, G.C. Park, Condensation heat transfer characteristic in the presence of non-condensable gas on natural convection at high pressure, Nucl. Eng. Des. 239 (4) (2009) 688-698.   DOI
26 J. Cai, Predicting the critical heat flux in concentric-tube open thermosiphon: a method based on support vector machine optimized by chaotic particle swarm optimization algorithm, Heat Mass Tran. 48 (8) (2012) 1425-1435.   DOI
27 G.M. Hobold, A.K. da Silva, Automatic detection of the onset of film boiling using convolutional neural networks and Bayesian statistics, Int. J. Heat Mass Tran. 134 (2019a) 262-270.   DOI
28 T. Tagami, Interim Report on Safety Assessment and Facilities Establishment Project for June 1965. No. 1, Japanese Atomic Energy Agency, Unpublished Work, 1965.
29 J. Su, Z. Sun, G. Fan, M. Ding, Analysis of experiments for the effect of noncondensable gases on steam condensation over a vertical tube external surface under low wall subcooling, Nucl. Eng. Des. 278 (2014) 644-650.   DOI
30 Y.G. Lee, Y.J. Jang, D.J. Choi, An experimental study of airesteam condensation on the exterior surface of a vertical tube under natural convection conditions, Int. J. Heat Mass Tran. 104 (2017) 1034-1047.   DOI
31 H.Y. Kim, J. Moon, D.J. Hong, E. Cha, B.J. Yun, Prediction of critical heat flux for narrow rectangular channels in a steady state condition using machine learning, Nucl. Eng. Technol. 53 (6) (2021) 1796-1809.   DOI
32 J.H. Kang, H.Y. Kim, J.Y. Bak, S.G. Lim, B.J. Yun, Condensation of steam mixed with non-condensable gas on vertical heat exchanger tubes in circumstances with free convection, Int. J. Heat Mass Tran. 169 (2021) 120925.   DOI
33 H. Liu, N.E. Todreas, M.J. Driscoll, An experimental investigation of A passive cooling unit for nuclear plant containment, Nucl. Eng. Des. 199 (3) (2000) 243-255.   DOI
34 L.E. Herranz, M.H. Anderson, M.L. Corradini, A diffusion layer model for steam condensation within the AP600 containment, Nucl. Eng. Des. 183 (1-2) (1998) 133-150.   DOI
35 A.A. Dehbi, PhD. dissertation, The Effects of Non-condensable Gases on Steam Condensation under Turbulent Natural Convection Conditions, Massachusets Institute of Technology, Department of Nuclear Eng., 1991.
36 G. Fan, P. Tong, Z. Sun, Y. Chen, Development of a new empirical correlation for steam condensation rates in the presence of air outside vertical smooth tube, Ann. Nucl. Energy 113 (2018) 139-146.   DOI
37 N. Giannetti, M.A. Redo, J. Jeong, S. Yamaguchi, K. Saito, H. Kim, Prediction of two-phase flow distribution in microchannel heat exchangers using artificial neural network, Int. J. Refrig. 111 (2020) 53-62.   DOI
38 B.T. Jiang, F.Y. Zhao, Combination of support vector regression and artificial neural networks for prediction of critical heat flux, Int. J. Heat Mass Tran. 62 (2013) 481-494.   DOI
39 T.B. Trafalis, O. Oladunni, D.V. Papavassiliou, Two-phase flow regime identification with a multiclassification support vector machine (SVM) model, Ind. Eng. Chem. Res. 44 (12) (2005) 4414-4426.   DOI
40 A.M. Ghahdarijani, F. Hormozi, A.H. Asl, Convective heat transfer and pressure drop study on nanofluids in double-walled reactor by developing an optimal multi-layer perceptron artificial neural network, Int. Commun. Heat Mass Tran. 84 (2017) 11-19.   DOI
41 B.R. Bird, W.E. Stewart, E.N. Lightfoot, Transport Phenomena, John Wiley & Sons Inc, New York, 1960.
42 M. Hojjat, Modeling heat transfer of non-Newtonian nanofluids using hybrid ANN-Metaheuristic optimization algorithm, J. Part. Sci. Technol. 3 (2017) 233-241.
43 Y. Jang, D. Choi, S. Kim, D. Jerng, Y. Lee, Development of an empirical correlation for condensation heat transfer coefficient on a vertical tube in the presence of a non-condensable gas, Trans. Korean Soc. Mech. Eng. 42 (3) (2018) 187-196.
44 S. Benteboula, F. Dabbene, Modeling of wall condensation in the presence of noncondensable light gas, Int. J. Heat Mass Tran. 151 (2020) 119313.   DOI