Application of POD reduced-order algorithm on data-driven modeling of rod bundle |
Kang, Huilun
(Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University)
Tian, Zhaofei (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) Chen, Guangliang (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) Li, Lei (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) Wang, Tianyu (Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University) |
1 | Y. Mi, M. Ishii, L.H. Tsoukalas, Flow regime identification methodology with neural networks and two-phase flow models, Nucl. Eng. Des. 204 (1-3) (Feb. 2001) 87-100, https://doi.org/10.1016/S0029-5493(00)00325-3. DOI |
2 | T. Cong, G. Su, S. Qiu, W. Tian, Applications of ANNs in flow and heat transfer problems in nuclear engineering: a review work, Prog. Nucl. Energy 62 (Jan. 2013) 54-71, https://doi.org/10.1016/J.PNUCENE.2012.09.003. DOI |
3 | G. Chen, Z. Zhang, Z. Tian, X. Dong, Y. Wang, CFD simulation for the optimal design and utilization of experiment to research the flow process in PWR, Ann. Nucl. Energy 94 (Aug. 2016) 1-9, https://doi.org/10.1016/J.ANUCENE.2016.02.007. DOI |
4 | M.V. Holloway, D.E. Beasley, M.E. Conner, "Investigation of swirling flow in rod bundle subchannels using computational fluid dynamics," in International Conference on Nuclear Engineering, Proceedings, ICONE (2006), https://doi.org/10.1115/ICONE14-89068. DOI |
5 | Xu Wang, Jiaqing Kou, Weiwei Zhang, Multi-fidelity surrogate reduced-order modeling of steady flow estimation, Int. J. Numer. Methods Fluid. (2020), https://doi.org/10.1002/fld.4850, 2020. DOI |
6 | D. Balduzzi, M. Frean, L. Leary, J.P. Lewis, K.W.D. Ma, B. McWilliams, "The shattered gradients problem: if resnets are the answer, then what is the question?," in 34th International Conference on Machine Learning, ICML 1 (2017) 536-549, 2017. |
7 | C.C. Liu, Y.M. Ferng, C.K. Shih, CFD evaluation of turbulence models for flow simulation of the fuel rod bundle with a spacer assembly, Appl. Therm. Eng. 40 (Jul. 2012) 389-396, https://doi.org/10.1016/J.APPLTHERMALENG.2012.02.027. DOI |
8 | J. Huang, H. Liu, W. Cai, Online in situ prediction of 3-D flame evolution from its history 2-D projections via deep learning, J. Fluid Mech. 875 (Sep. 2019), https://doi.org/10.1017/jfm.2019.545.R2. DOI |
9 | L. Sun, H. Gao, S. Pan, J.-X. Wang, Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data, Comput. Methods Appl. Mech. Eng. 361 (Apr. 2020), https://doi.org/10.1016/J.CMA.2019.112732, 112732. DOI |
10 | Z. Luo, J. Du, Z. Xie, Y. Guo, A reduced stabilized mixed finite element formulation based on proper orthogonal decomposition for the nonstationary Navier-Stokes equations, Int. J. Numer. Methods Eng. 88 (1) (2011) 31-46, https://doi.org/10.1002/nme.3161. DOI |
11 | Z. Karoutas, C. Gu, B. Sholin, 3-D flow analyses for design of nuclear fuel spacer, in: Proceedings of the 7th International Meeting on Nuclear Reactor Thermal-Hydraulics, NURETH-7), New York, USA, 1995, pp. 3153-3174. |
12 | G. Chen, Z. Zhang, Z. Tian, Optimal meshing methods and schemes for the simulation of assembly, Trans. Am. Nucl. Soc. 111 (2014) 1572-1575. |
13 | N. Amanifard, N. Nariman-Zadeh, M. Borji, A. Khalkhali, A. Habibdoust, Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms, Energy Convers. Manag. 49 (2) (Feb. 2008) 311-325, https://doi.org/10.1016/J.ENCONMAN.2007.06.002. DOI |
14 | D. Ma, T. Zhou, J. Chen, S. Qi, M. Ali Shahzad, Z. Xiao, Supercritical water heat transfer coefficient prediction analysis based on BP neural network, Nucl. Eng. Des. 320 (Aug. 2017) 400-408, https://doi.org/10.1016/J.NUCENGDES.2017.06.013. DOI |
15 | 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 (Jun. 2020), https://doi.org/10.1016/J.ANUCENE.2020.107334, 107334. DOI |
16 | X. Li, Y. Gao, Methods of simulating large-scale rod bundle and application to a 17 × 17 fuel assembly with mixing vane spacer grid, Nucl. Eng. Des. 267 (Feb. 2014) 10-22, https://doi.org/10.1016/J.NUCENGDES.2013.11.064. DOI |
17 | N. Vaziri, A. Hojabri, A. Erfani, M. Monsefi, B. Nilforooshan, Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: a comparison study, Nucl. Eng. Des. 237 (4) (Feb. 2007) 377-385, https://doi.org/10.1016/J.NUCENGDES.2006.05.005. DOI |
18 | B.T. Jiang, J.S. Ren, P. Hu, F.Y. Zhao, Prediction of critical heat flux for water flow in vertical round tubes using support vector regression model, Prog. Nucl. Energy 68 (Sep. 2013) 210-222, https://doi.org/10.1016/J.PNUCENE.2013.07.004. DOI |
19 | T. Aaron, O.T. Schmidt, C. Tim, Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis, J. Fluid Mech. 847 (2017) 821-867, https://doi.org/10.1017/jfm.2018.283. DOI |
20 | L. Sirovich, Turbulence and the dynamics of coherent structures. I - coherent structures. II - symmetries and transformations. III - dynamics and scaling, Q. Appl. Math. 45 (3) (1987), https://doi.org/10.1090/qam/910463. DOI |
21 | M.D. McKay, R.J. Beckman, W.J. Conover, Comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 21 (1979) 239-245, https://doi.org/10.1080/00401706.1979.10489755. DOI |
22 | Y. Wang, et al., CFD simulation of flow and heat transfer characteristics in a 5×5 fuel rod bundles with spacer grids of advanced PWR, Nucl. Eng. Technol. (Dec. 2019), https://doi.org/10.1016/J.NET.2019.12.012. DOI |
23 | D.S. Rowe, B.M. Johnson, J.G. Knudsen, Implications concerning rod bundle crossflow mixing based on measurements of turbulent flow structure, Int. J. Heat Mass Tran. 17 (3) (Mar. 1974) 407-419, https://doi.org/10.1016/0017-9310(74)90012-X. DOI |
24 | C.M. Lee, Y.D. Choi, Comparison of thermo-hydraulic performances of large scale vortex flow (LSVF) and small scale vortex flow (SSVF) mixing vanes in 17 × 17 nuclear rod bundle, Nucl. Eng. Des. 237 (24) (Dec. 2007) 2322-2331, https://doi.org/10.1016/J.NUCENGDES.2007.04.011. DOI |
25 | S.Y. Han, J.S. Seo, M.S. Park, Y.D. Choi, Measurements of the flow characteristics of the lateral flow in the 6 × 6 rod bundles with Tandem Arrangement Vanes, Nucl. Eng. Des. 239 (12) (Dec. 2009) 2728-2736, https://doi.org/10.1016/J.NUCENGDES.2009.09.026. DOI |
26 | P.L.S. Serra, P.H.F. Masotti, M.S. Rocha, D.A. de Andrade, W.M. Torres, R.N. de Mesquita, Two-phase flow void fraction estimation based on bubble image segmentation using Randomized Hough Transform with Neural Network (RHTN), Prog. Nucl. Energy 118 (Jan. 2020), https://doi.org/10.1016/J.PNUCENE.2019.103133, 103133. DOI |
27 | X.-Z. Cui, K.-Y. Kim, Three-dimensional analysis of turbulent heat transfer and flow through mixing vane in A subchannel of nuclear reactor, J. Nucl. Sci. Technol. 40 (10) (Oct. 2003) 719-724, https://doi.org/10.1080/18811248.2003.9715412. DOI |
28 | G. Chen, Z. Zhang, Z. Tian, L. Li, X. Dong, H. Ju, Design of a CFD scheme using multiple RANS models for PWR, Ann. Nucl. Energy 102 (Apr. 2017) 349-358, https://doi.org/10.1016/J.ANUCENE.2016.12.030. DOI |
29 | J. Zhang, et al., Prediction of flow boiling heat transfer coefficient in horizontal channels varying from conventional to small-diameter scales by genetic neural network, Nucl. Eng. Technol. 51 (8) (Dec. 2019) 1897-1904, https://doi.org/10.1016/J.NET.2019.06.009. DOI |
30 | W. K, T.-H. Chun, C.-H. Shin, D.-S. Oh, Numerical computation of heat transfer enhancement of a PWR rod bundle with mixing vane spacers, Nucl. Technol. 161 (1) (Jan. 2008) 69-79, https://doi.org/10.13182/NT08-A3914. DOI |
31 | M.A. Navarro, A.A.C. Santos, Evaluation of a numeric procedure for flow simulation of a 5 × 5 PWR rod bundle with a mixing vane spacer, Prog. Nucl. Energy 53 (8) (Nov. 2011) 1190-1196, https://doi.org/10.1016/J.PNUCENE.2011.08.002. DOI |
32 | J. Li, T. Zhou, Z. Ju, Q. Huo, Z. Xiao, Sensitivity analysis of CHF parameters under flow instability by using a neural network method, Ann. Nucl. Energy 71 (Sep. 2014) 211-216, https://doi.org/10.1016/J.ANUCENE.2014.03.040. DOI |
33 | R.N. de Mesquita, et al., Classification of natural circulation two-phase flow image patterns based on self-organizing maps of full frame DCT coefficients, Nucl. Eng. Des. 335 (Aug. 2018) 161-171, https://doi.org/10.1016/J.NUCENGDES.2018.05.019. DOI |
34 | K. Rehme, The structure of turbulence in rod bundles and the implications on natural mixing between the subchannels, Int. J. Heat Mass Tran. 35 (2) (Feb. 1992) 567-581, https://doi.org/10.1016/0017-9310(92)90291-Y. DOI |
35 | W. Qu, J. Xiong, S. Chen, Z. Qiu, J. Deng, X. Cheng, PIV measurement of turbulent flow downstream of mixing vane spacer grid in 5×5 rod bundle, Ann. Nucl. Energy 132 (Oct. 2019) 277-287, https://doi.org/10.1016/J.ANUCENE.2019.04.016. DOI |
36 | H. Wang, D. Lu, Y. Liu, "PIV measurement and CFD analysis of the turbulent flow in a 3 × 3 rod bundle, Ann. Nucl. Energy 140 (Jun. 2020), https://doi.org/10.1016/J.ANUCENE.2019.107135, 107135. DOI |
37 | A.C. Trupp, R.S. Azad, The structure of turbulent flow in triangular array rod bundles, Nucl. Eng. Des. 32 (1) (Apr. 1975) 47-84, https://doi.org/10.1016/0029-5493(75)90090-4. DOI |
38 | J. Xiong, R. Cheng, C. Lu, X. Chai, X. Liu, X. Cheng, CFD simulation of swirling flow induced by twist vanes in a rod bundle, Nucl. Eng. Des. 338 (Nov. 2018) 52-62, https://doi.org/10.1016/J.NUCENGDES.2018.08.003. DOI |
39 | J. Zang, X. Yan, Y. Li, X. Zeng, Y. Huang, "The flow resistance experiments of supercritical pressure water in 2 × 2 rod bundle, Int. J. Heat Mass Tran. 147 (Feb. 2020), https://doi.org/10.1016/J.IJHEATMASSTRANSFER.2019.118873, 118873. DOI |
40 | Y. Wang, Y.M. Ferng, L.X. Sun, CFD assist in design of spacer-grid with mixingvane for a rod bundle, Appl. Therm. Eng. 149 (Feb. 2019) 565-577, https://doi.org/10.1016/J.APPLTHERMALENG.2018.12.090. DOI |