Multi-fidelity modeling and analysis of a pressurized vessel-pipe-safety valve system based on MOC and surrogate modeling methods

被引:3
|
作者
Song, Xueguan [1 ,2 ]
Li, Qingye [1 ,2 ]
Liu, Fuwen [1 ,2 ]
Zhou, Weihao [1 ,2 ]
Zong, Chaoyong [1 ,2 ]
机构
[1] Dalian Univ Technol, Sch Mech Engn, Dalian 116023, Peoples R China
[2] State Key Lab High Performance Precis Mfg, Dalian, Peoples R China
基金
中国国家自然科学基金;
关键词
PVPSV system; MOC; T-AHS model; Fluid -structure interaction; Dynamic instabilities; Numerical simulation; RELIEF VALVES; DYNAMIC-BEHAVIOR; GAS SERVICE; VAPOR; OPTIMIZATION;
D O I
10.1016/j.net.2023.04.033
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
A pressurized vessel-pipe-safety valve (PVPSV) combination is a commonly used configuration in nuclear power plants, and a good numerical model is essential for the system design, sizing and performance optimization. However, owing to the large-scale and cross-scale features, it is still a challenge to build a system level numerical model with both high accuracy and efficiency. To overcome this, a novel system level modeling method which can synthesize the advantages of various models is proposed in this paper. For system modeling, the analytical approach, the method of characteristics (MOC) and the surrogate model approach are respectively adopted to predict the dynamics of the pressure vessel, the connecting pipe and the safety valve, and different models are connected through data interfaces. With this system model, dynamic simulations were carried out and both the stable and the unstable system responses were obtained. For the model verification purpose, the simulation results were compared with those obtained from experiments and full CFD simulations. A good agreement and a better efficiency were obtained, verifying the ability of the model and the feasibility of the modeling method proposed in this paper.& COPY; 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:3088 / 3101
页数:14
相关论文
共 50 条
  • [41] A harmonic domain regressor with dynamic task weighting strategy for multi-fidelity surrogate modeling in engineering design
    You, Lin
    Xing, Songqing
    Yi, Jin
    Yuan, Shujin
    Yang, Jiangtao
    Pu, Huayan
    Luo, Jun
    ADVANCED ENGINEERING INFORMATICS, 2025, 64
  • [42] RSAL-iMFS: A framework of randomized stacking with active learning for incremental multi-fidelity surrogate modeling
    Liu, Zongqi
    Song, Xueguan
    Zhang, Chao
    Ma, Yunsheng
    Tao, Dacheng
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2023, 120
  • [43] Multi-Fidelity Modeling and Reliability Analysis of Off-Shore Production Wells
    Hamdan, Bayan
    Wang, Pingfeng
    2023 ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, RAMS, 2023,
  • [44] Modal Analysis of Flight Vehicle Wing Structure Based on Multi-fidelity Surrogate Model
    Zhang H.
    Li X.
    Fang W.
    Wu Z.
    Hong D.
    Yuhang Xuebao/Journal of Astronautics, 2023, 44 (10): : 1496 - 1502
  • [45] A novel geometric nonlinear reduced order modeling method using multi-fidelity surrogate for real-time structural analysis
    He, Xiwang
    Yang, Liangliang
    Li, Kunpeng
    Pang, Yong
    Kan, Ziyun
    Song, Xueguan
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2023, 66 (11)
  • [46] A novel geometric nonlinear reduced order modeling method using multi-fidelity surrogate for real-time structural analysis
    Xiwang He
    Liangliang Yang
    Kunpeng Li
    Yong Pang
    Ziyun Kan
    Xueguan Song
    Structural and Multidisciplinary Optimization, 2023, 66
  • [47] PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling
    Jakeman, J. D.
    ENVIRONMENTAL MODELLING & SOFTWARE, 2023, 170
  • [49] A multi-fidelity surrogate modeling method based on variance-weighted sum for the fusion of multiple non-hierarchical low-fidelity data
    Cheng, Meng
    Jiang, Ping
    Hu, Jiexiang
    Shu, Leshi
    Zhou, Qi
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2021, 64 (06) : 3797 - 3818
  • [50] A multi-fidelity surrogate modeling method based on variance-weighted sum for the fusion of multiple non-hierarchical low-fidelity data
    Meng Cheng
    Ping Jiang
    Jiexiang Hu
    Leshi Shu
    Qi Zhou
    Structural and Multidisciplinary Optimization, 2021, 64 : 3797 - 3818