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http://dx.doi.org/10.1016/j.net.2020.12.007

Prediction of critical heat flux for narrow rectangular channels in a steady state condition using machine learning  

Kim, Huiyung (Department of Mechanical Engineering, Pusan National University)
Moon, Jeongmin (Department of Mechanical Engineering, Pusan National University)
Hong, Dongjin (Department of Mechanical Engineering, Pusan National University)
Cha, Euiyoung (Department of Mechanical Engineering, Pusan National University)
Yun, Byongjo (Department of Mechanical Engineering, Pusan National University)
Publication Information
Nuclear Engineering and Technology / v.53, no.6, 2021 , pp. 1796-1809 More about this Journal
Abstract
The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.
Keywords
Narrow rectangular channel; Critical heat flux; Machine learning; Neural network; Downward flow;
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1 Y. Liu, T.N. Dinh, Y. Sato, B. Niceno, Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results, Appl. Therm. Eng. 144 (2018) 305-320.   DOI
2 C. Xia, W. Hu, Z. Guo, Natural convective boiling in vertical rectangular narrow channels, Exp. Therm. Fluid Sci. 12 (3) (1996) 313-324.   DOI
3 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
4 J. Larsen, L.K. Hansen, C. Svarer, M. Ohlsson, Design and regularization of neural networks: the optimal use of a validation set, in Neural Networks for Signal Processing VI, in: Proceedings of the 1996 IEEE Signal Processing Society Workshop, IEEE., 1996, pp. 62-71.
5 F. Tanaka, T. Hibiki, Y. Saito, T. Takeda, K. Mishima, Thermal-hydraulic Design Concept of the Solid-Target System of Spallation Neutron Source, 2001.
6 Y. Liu, N.T. Dinh, R.C. Smith, X. Sun, Uncertainty quantification of two-phase flow and boiling heat transfer simulations through a data-driven modular Bayesian approach, Int. J. Heat Mass Tran. 138 (2019) 1096-1116.   DOI
7 R. Salakhutdinov, A. Mnih, G.E. Hinton, Restricted Boltzmann machines for collaborative filtering, in: Proceedings of the 24th International Conference on Machine Learning, 2007.
8 R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann 2 (12) (1995) 1137-1143.
9 S. Arlot, A. Celisse, A survey of cross-validation procedures for model selection, Stat. Surv. 4 (2010) 40-79.   DOI
10 A. Ng, Machine Learning Yearning: Technical Strategy for Ai Engineers in the Era of Deep Learning, 2019, pp. 25-26. https://www.mlyearning.org.
11 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media, 2009, pp. 219-260.
12 Y. Kim, K. Suh, One-dimensional critical heat flux concerning surface orientation and gap size effects, Nucl. Eng. Des. 226 (3) (2003) 277-292.   DOI
13 F.W. Staub, The void fraction in subcooled boiling-prediction of vapour volumetric fraction, J. Heat Tran. 90 (1968) 151-157.   DOI
14 W. Chang, X. Chu, A.F.B.S. Fareed, S. Pandey, J. Luo, B. Weigand, E. Laurien, Heat transfer prediction of supercritical water with artificial neural networks, Appl. Therm. Eng. 131 (2018) 815-824.   DOI
15 L. Yang, W. Dai, Y. Rao, M.K. Chyu, Optimization of the hole distribution of an effusively cooled surface facing non-uniform incoming temperature using deep learning approaches, Int. J. Heat Mass Tran. 145 (2019) 118749.   DOI
16 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 (CVPR), 2015, pp. 1-9.
17 F. Tachibana, M. Akiyama, H. Kawamura, Non-hydrodynamic aspects of pool boiling burnout, J. Nucl. Sci. Technol. 4 (3) (1967) 121-130.   DOI
18 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
19 M. Ma, J. Lu, G. Tryggvason, Using statistical learning to close two-fluid multiphase flow equations for a simple bubbly system, Phys. Fluids 27 (9) (2015), 092101.   DOI
20 P. Amani, K. Vajravelu, Intelligent modeling of rheological and thermophysical properties of green covalently functionalized graphene nanofluids containing nanoplatelets, Int. J. Heat Mass Tran. 120 (2018) 95-105.   DOI
21 H.S. Lim, Y.T. Kang, Estimation of finish cooling temperature by artificial neural networks of backpropagation during accelerated control cooling process, Int. J. Heat Mass Tran. 126 (2018) 579-588.   DOI
22 A. Zendehboudi, R. Saidur, I.M. Mahbubul, S.H. Hosseini, Data-driven methods for estimating the effective thermal conductivity of nanofluids: a comprehensive review, Int. J. Heat Mass Tran. 131 (2019) 1211-1231.   DOI
23 H. Tayara, K.G. Soo, K.T. Chong, Vehicle detection and counting in highresolution aerial images using convolutional regression neural network, IEEE Access 6 (2018) 2220-2230.   DOI
24 G.M. Hobold, A.K. da Silva, Machine learning classification of boiling regimes with low speed, direct and indirect visualization, Int. J. Heat Mass Tran. 125 (2018) 1296-1309.   DOI
25 H. Wei, S. Zhao, Q. Rong, H. Bao, Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods, Int. J. Heat Mass Tran. 127 (2018) 908-916.   DOI
26 A. Shahsavar, S. Khanmohammadi, A. Karimipour, M. Goodarzi, A novel comprehensive experimental study concerned synthesizes and prepare liquid paraffin-Fe3O4 mixture to develop models for both thermal conductivity & viscosity: a new approach of GMDH type of neural network, Int. J. Heat Mass Tran. 131 (2019) 432-441.   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 (2019) 262-270.   DOI
28 Y. Li, H. Wang, X. Deng, Image-based reconstruction for a 3D-PFHS heat transfer problem by ReConNN, Int. J. Heat Mass Tran. 134 (2019) 656-667.   DOI
29 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
30 G.M. Hobold, A.K. da Silva, Visualization-based nucleate boiling heat flux quantification using machine learning, Int. J. Heat Mass Tran. 134 (2019) 511-520.   DOI
31 Y. Lv, Y. Duan, W. Kang, Z. Li, F.Y. Wang, Traffic flow prediction with big data: a deep learning approach, IEEE Trans. Intell. Transport. Syst. 16 (2) (2014) 865-873.   DOI
32 G. Litjens, T. Kooi, B.E. Bejnordi, A.A.A. Setio, F. Ciompi, M. Ghafoorian, J.A.W.M. Laak, B. Ginneken, C.I. S anchez, A survey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60-88.   DOI
33 X. Zhao, K. Shirvan, R.K. Salko, F. Guo, On the prediction of critical heat flux using a physics-informed machine learning-aided framework, Appl. Therm. Eng. 164 (2020) 114540.   DOI
34 M. Ravichandran, M. Bucci, Online, quasi-real-time analysis of highresolution, infrared, boiling heat transfer investigations using artificial neural networks, Appl. Therm. Eng. 163 (2019) 114357.   DOI
35 G.E. Hinton, Training products of experts by minimizing contrastive divergence, Neural Comput. 14 (8) (2002) 1771-1800.   DOI
36 T. Shimobaba, T. Kakue, T. Ito, Convolutional neural network-based regression for depth prediction in digital holography, in: IEEE 27th International Symposium on Industrial Electronics (ISIE), Cairns, QLD, 2018, pp. 1323-1326.
37 J. Du, Y. Xu, Hierarchical deep neural network for multivariate regression, Pattern Recogn. 63 (2017) 149-157.   DOI
38 A. Ameri, M.A. Akhaee, E. Scheme, K. Englehart, Regression convolutional neural network for improved simultaneous EMG control, J. Neural. Eng. 16 (2019), 036015.   DOI
39 F. Khademi, S.M. Jamal, N. Deshpande, S. Londhe, Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression, International Journal of Sustainable Built Environment 5 (2016) 355-369.   DOI
40 M. He, Y. Lee, Application of deep belief network for critical heat flux prediction on microstructure surfaces, Nucl. Technol. (2019) 1-17.
41 K. Lan, D.T. Wang, S. Fong, L.S. Liu, K.K. Wong, N. Dey, A survey of data mining and deep learning in bioinformatics, J. Med. Syst. 42 (8) (2018) 139.   DOI
42 A. Kamilaris, F.X. Prenafeta-Boldu, Deep learning in agriculture: a survey, Comput. Electron. Agric. 147 (2018) 70-90.   DOI
43 G. Sateesh Babu, P. Zhao, X.L. Li, Deep convolutional neural network based regression approach for estimation of remaining useful life, Database Systems for Advanced Applications (2016) 214-228.
44 A. Komeilibirjandi, A.H. Raffiee, A. Maleki, M. Alhuyi Nazari, M. Safdari Shadloo, Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network, J. Therm. Anal. Calorim. 139 (4) (2020) 2679-2689.   DOI
45 G.E. Hinton, Deep belief networks, Scholarpedia 4 (5) (2009) 5497.   DOI
46 G.E. Hinton, P. Dayan, B.J. Frey, R.M. Neal, The "wake-sleep" algorithm for unsupervised neural networks, Science 268 (5214) (1995) 1158-1161.   DOI
47 G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18 (7) (2006) 1527-1554.   DOI
48 Y. Sudo, K. Miyata, H. Ikawa, M. Kaminaga, M. Ohkawara, Experimental study of differences in DNB heat flux between upflow and downflow in vertical rectangular channel, J. Nucl. Sci. Technol. 22 (8) (1985) 604-618.   DOI
49 M.H. Ahmadi, A. Tatar, M.A. Nazari, R. Ghasempour, A.J. Chamkha, W.M. Yan, Applicability of connectionist methods to predict thermal resistance of pulsating heat pipes with ethanol by using neural networks, Int. J. Heat Mass Tran. 126 (2018) 1079-1086.   DOI
50 S. Kim, P.Y. Lu, S. Mukherjee, M. Gilbert, L. Jing, V. Ceperic, M. Soljacic, Integration of neural network-based symbolic regression in deep learning for scientific discovery, in: IEEE Transactions on Neural Networks and Learning Systems, 2020.
51 Y. Sudo, Study on critical heat flux in rectangular channels heated from one or both sides at pressures ranging from 0.1 to 14 MPa, J. Heat Tran. 118 (3) (1996) 680-688.   DOI
52 S. Mirshak, W.S. Durant, R.H. Towell, Heat Flux at Burnout, No. DP-355, Du Pont de Nemours (EI) & Co. Savannah River Lab., Augusta, Ga, 1959.
53 K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv Preprint arXiv:1409, 2014, p. 1556.
54 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.
55 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.
56 L. Liu, W. Ouyang, X. Wang, P. Fieguth, J. Chen, X. Liu, M. Pietikainen, Deep learning for generic object detection: a survey, Int. J. Comput. Vis. 128 (2) (2020) 261-318.   DOI
57 H.Y. Kim, J.Y. Bak, J.J. Jeong, J.H. Park, B.J. Yun, Investigation of the CHF correlation for a narrow rectangular channel under a downward flow condition, Int. J. Heat Mass Tran. 130 (1) (2019) 60-71.   DOI
58 M. Kaminaga, Y. Sudo, A new CHF correlation scheme proposed for vertical rectangular channels heated from both sides in nuclear research reactors, in: The 1st JSME/ASME Joint International Conference on Nuclear Engineering, 1991.
59 G. Guglielmini, E. Nannei, On the effect of heating wall thickness on pool boiling burnout, Int. J. Heat Mass Tran. 19 (9) (1976) 1073-1075.   DOI
60 M. Kureta, H. Akimoto, Critical heat flux correlation for subcooled boiling flow in narrow channels, Int. J. Heat Mass Tran. 45 (20) (2002) 4107-4115.   DOI
61 M. Kureta, H. Akimoto, Critical heat flux of subcooled flow boiling in narrow rectangular channels, in: Proceedings of the 6th International Conference on Nuclear Engineering, ICONE-7, Tokyo, Japan, 1999, p. 7016.
62 S.K. Moon, W.P. Baek, S.H. Chang, Parametric trends analysis of the critical heat flux based on artificial neural networks, Nucl. Eng. Des. 163 (1-2) (1996) 29-49.   DOI
63 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
64 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
65 V.H. Del Valle, An experimental study of critical heat flux in subcooled flow boiling at low pressure including the effect of wall thickness, in: Paper Presented at the ASME·JSME Thermal Engineering Joint Conference, Honolulu, Hawaii, 1983.
66 M. Kaminaga, K. Yamamoto, Y. Sudo, Improvement of critical heat flux correlation for research reactors using plate-type fuel, J. Nucl. Sci. Technol. 35 (12) (1998) 943-951.   DOI
67 F. Tanaka, T. Hibiki, K. Mishima, Correlation for flow boiling critical heat flux in thin rectangular channels, J. Heat Tran. 131 (12) (2009) 121003.   DOI