Research on optimization design of PWR flow distribution device based on numerical simulation

被引:4
作者
Xie, Yuanming [1 ,2 ,3 ]
Li, Wenqiang [1 ,2 ]
Yu, Tianda [3 ]
Deng, Chaojun [3 ]
Hu, Xuefei [3 ]
机构
[1] Sichuan Univ, Sch Mech Engn, 24 South Sect 1,Yihuan Rd, Chengdu 610065, Peoples R China
[2] Innovat Method & Design Key Lab Sichuan, Chengdu, Peoples R China
[3] Nucl Power Inst China, Chengdu, Peoples R China
关键词
Pressurized water reactor; flow distribution device; numerical simulation; surrogate model; optimization design; CORE INLET; GEOMETRY;
D O I
10.1080/00223131.2020.1758228
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
In the lower chamber of pressurized water reactor (PWR), the flow distribution device is the core module to distribute coolant into the core. It has complex structure and numerous design parameters. Therefore, it has important theoretical and practical significance to optimize the device. The mesh independence verification, turbulence model selection, and data processing all can influence the numerical simulation results of the lower chamber, in order to research the influence, a numerical simulation method based on the original model of CNP1000 reactor lower chamber is proposed in this paper. In the method, an optimization design method of flow distribution device is established based on surrogate model. The main design variables and optimization objectives are determined based on the device's structure and function characteristics. And then it respectively adopts Kriging algorithm and multi-objective genetic algorithm to establish a surrogate model of flow distribution device and optimize it globally. Finally, the optimal design variables are obtained. Compared with the device's performance before optimization, the after optimization has smaller total pressure loss and more uniform flow. The effectiveness and practicability of proposed optimization design method can be verified.
引用
收藏
页码:1074 / 1090
页数:17
相关论文
共 39 条
[1]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[2]  
[丁宗华 Ding Zonghua], 2018, [原子能科学技术, Atomic Energy Science and Technology], V52, P1635
[3]   Wrap-around L2-discrepancy of random sampling, Latin hypercube and uniform designs [J].
Fang, KT ;
Ma, CX .
JOURNAL OF COMPLEXITY, 2001, 17 (04) :608-624
[4]   Uniform design: Theory and application [J].
Fang, KT ;
Lin, DKJ ;
Winker, P ;
Zhang, Y .
TECHNOMETRICS, 2000, 42 (03) :237-248
[5]  
Giunta A.A., 2003, Overview of modern design of experiments methods for computational simulations
[6]  
[郭超 Guo Chao], 2018, [核科学与工程, Nuclear Science and Engineering], V38, P353
[7]   Development of metamodeling based optimization system for high nonlinear engineering problems [J].
Hu, Wang ;
Li Enying ;
Li, G. Y. ;
Zhong, Z. H. .
ADVANCES IN ENGINEERING SOFTWARE, 2008, 39 (08) :629-645
[8]  
Hu ZH, 2010, STUDY KEY TECHNIQUES
[9]   Coolant flow field in a real geometry of PWR downcomer and lower plenum [J].
Jeong, Ji Hwan ;
Han, Byoung-Sub .
ANNALS OF NUCLEAR ENERGY, 2008, 35 (04) :610-619
[10]  
Jiang XH, 2002, NUCL POWER ENG, V2, P49