Three-dimensional boiling water reactor core transient simulation based on discontinuity factor

Duan Xinhui; Jiang Ping; Wang Bingshu

doi:10.11884/HPLPB201830.180178

Volume 30 Issue 12

Dec. 2018

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Duan Xinhui, Jiang Ping, Wang Bingshu. Three-dimensional boiling water reactor core transient simulation based on discontinuity factor[J]. High Power Laser and Particle Beams, 2018, 30: 126003. doi: 10.11884/HPLPB201830.180178

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Three-dimensional boiling water reactor core transient simulation based on discontinuity factor

doi: 10.11884/HPLPB201830.180178

1.
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
2.
Baoding Sinosimu Technology Co.Ltd, Baoding 071051, China
3.
College of Electronic Information Engineering, Hebei University, Baoding 071002, China

Received Date: 2018-06-26
Rev Recd Date: 2018-10-06
Publish Date: 2018-12-15

Abstract

Abstract

The coarse mesh finite difference method with discontinuity factor correction is one of the effective methods to realize the core transient three-dimensional numerical simulation.The calculation method of coarse mesh interface discontinuity factor and the boundary albedo determines the precision in the process of real-time simulation.In the process of calculation of discontinuity factor and boundary albedo, the fine cell calculation and coarse mesh homogenization process is eliminated.Directly under the condition of the coarse cell, based on the nodal expansion method and nonlinear iterative strategy, coarse cell interface discontinuity factor ratio and boundary albedo are deduced.The corresponding calculation programs are developed.From a typical boiling water reactor 3Dtransient simulation benchmark, the method is proven to be available.The static and transient precisions both in space domain and time domain, are equivalent with that of the advanced nodal method, and the computational efficiency is superior to that of the advanced nodal method.This method provides a feasible choice for the development of full scope simulator of transient calculation of three-dimensional core model.
- generalized equivalence theory,
- discontinuity factor,
- boiling water reactor core,
- transient simulation

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References

[1]	郑友琦, Lee Deokjung. 基于非线性迭代的压水堆瞬态计算程序开发[J]. 强激光与粒子束, 2017, 29: 036001. doi: 10.11884/HPLPB201729.160297 Zheng Youqi, Lee Deokjung. Nodal code development for pressurized water reactor transient analysis based on non-linear iteration method. 2017, 29: 036001 doi: 10.11884/HPLPB201729.160297
[2]	Janosy J S, Kereszturi A, Hazi G, et al. Real-time 3D simulation of a pressurized water nuclear reactor[C]//International Conference on Computer Modelling and Simulation. 2010: 414-419.
[3]	Georgieva E, Dinkov Y, Ivanov K, et al. Benchmarking the NEM real-time core model for VVER-1000 simulator application: Asymmetric core[C]//ASME 24th International Conference on Nuclear Engineering. 2016.
[4]	Masashi T, Tatsuya I, Moore B. Development of kinetics model for BWR core simulator AETNA[J]. Journal of Nuclear Science & Technology, 2003, 40(4): 201-212.
[5]	Smith K S. Spatial homogenization methods for light water reactor analysis[D]. Massachusetts: Massachusetts Institute of Technology, 1980: 12-90.
[6]	Tatsuya I, Munenari Y. Advanced nodal methods of the few-group BWR core simulator NEREUS[J]. Journal of Nuclear Science & Technology, 1999, 36(11): 996-1008.
[7]	郭炯, 李富. 不连续因子计算强吸收体区域的改进[J]. 原子能科学技术, 2013, 47(s1): 33-37. https://www.cnki.com.cn/Article/CJFDTOTAL-YZJS2013S1009.htm Guo Jiong, Li Fu. Improvement of discontinuous factor for strong absorber region. Atomic Energy Science and Technology, 2013, 47(s1): 33-37 https://www.cnki.com.cn/Article/CJFDTOTAL-YZJS2013S1009.htm
[8]	吕栋, 俞陆林, 韩宇, 等. 处理轴向三维非均匀效应的单组件均匀化模型[J]. 核动力工程, 2014(s2): 140-142. (Lü https://www.cnki.com.cn/Article/CJFDTOTAL-HDLG2014S2038.htm Dong, Yu Lulin, Han Yu, et al. Single assembly pin-by-pin homogenization model for handling axial 3D heterogeneity effect. Nuclear Power Engineering, 2014(s2): 140-142 https://www.cnki.com.cn/Article/CJFDTOTAL-HDLG2014S2038.htm
[9]	Bernal A, Roman J E, Miró R, et al. Assembly discontinuity factors for the neutron diffusion equation discretized with the finite volume method. Application to BWR[J]. Annals of Nuclear Energy, 2016, 97: 76-85. doi: 10.1016/j.anucene.2016.06.023
[10]	Vidal-Ferràndiz A, González-Pintor S, Ginestar D, et al. Use of discontinuity factors in high-order finite element methods[J]. Annals of Nuclear Energy, 2016, 87: 728-738. doi: 10.1016/j.anucene.2015.06.021
[11]	Zimin V G, Ninokata H. Nodal neutron kinetics model based on nonlinear iteration procedure for LWR analysis[J]. Annals of Nuclear Energy, 1998, 25(8): 507-528. doi: 10.1016/S0306-4549(97)00078-9
[12]	Singh T, Mazumdar T, Pandey P. NEMSQR: A 3-D multi group diffusion theory code based on nodal expansion method for square geometry[J]. Annals of Nuclear Energy, 2014, 64: 230-243. doi: 10.1016/j.anucene.2013.09.041

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