Fractional-order particle swarm based multi-objective PWR core loading pattern optimization

被引：39

作者：

Zameer, Aneela ^{[1
]}

Muneeb, Muhammad ^{[1
]}

Mirza, Sikander M. ^{[2
]}

Raja, Muhammad Asif Zahoor ^{[3
]}

机构：

[1] Pakistan Inst Engn & Appl Sci, Dept Comp & Informat Sci, Islamabad 45650, Pakistan

[2] Pakistan Inst Engn & Appl Sci, Dept Phys & Appl Math, Islamabad 45650, Pakistan

[3] COMSATS Univ Islamabad, Dept Elect & Comp Engn, Attock Campus, Attock 43600, Pakistan

来源：

ANNALS OF NUCLEAR ENERGY | 2020年 / 135卷 / 135期

关键词：

Fractional order particle swarm optimization; Pressurized water reactor; Core reload pattern; Reactor safety; FUEL-MANAGEMENT OPTIMIZATION; ALGORITHM; PSO; SYSTEMS; DESIGN;

D O I：

10.1016/j.anucene.2019.106982

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 this study the multi-objective core reload pattern optimization has been performed using the Fractional Order Particle Swarm Optimization (FOPSO) algorithm. The multi-objective goals aimed at maximization of the cycle multiplication factor while maintaining the core radial power peaking factor flat within the prescribed safety limits. These calculations have been performed for the first core loading of CHASNUPP-1 using PSU-LEOPARD and MCRAC codes for burnup dependent group constant generation and the subsequent diffusion theory-based criticality and cycle burnup calculations, respectively. Using the proposed FOPSO scheme, enhancement in the cycle length have been observed while maintaining power peaking factor within the prescribed constraints throughout the cycle. The FOPSO methodology has been found robust and efficient. (C) 2019 Elsevier Ltd. All rights reserved.

引用

页数：11

共 50 条

[21] Comment on “Particle swarm optimization with fractional-order velocity”
Ling-Yun Zhou
Shang-Bo Zhou
Muhammad Abubakar Siddique
Nonlinear Dynamics, 2014, 77 : 431 - 433
[22] A Multi-Objective Particle Swarm Optimization Based on Grid Distance
Leng, Rui
Ouyang, Aijia
Liu, Yanmin
Yuan, Lian
Wu, Zongyue
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2020, 34 (03)
[23] Multi-Objective Particle Swarm Optimization Based on Grid Ranking
Li L.
Wang W.
Xu X.
Li W.
Wang, Wanliang (zjutwwl@zjut.edu.cn), 1600, Science Press (54): : 1012 - 1023
[24] Integrated optimization by multi-objective particle swarm optimization
Tokyo Metropolitan University, 1-1, Minamiosawa, Hachioji-shi, Tokyo 192-0397, Japan
IEEJ Trans. Electr. Electron. Eng., 1931, 1 (79-81):
[25] Integrated Optimization by Multi-Objective Particle Swarm Optimization
Kawarabayashi, Masaru
Tsuchiya, Junichi
Yasuda, Keiichiro
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2010, 5 (01) : 79 - 81
[26] Surrogate-based Multi-Objective Particle Swarm Optimization
Santana-Quintero, Luis V.
Coello Coello, Carlos A.
Hernandez-Diaz, Alfredo G.
Osorio Velazquez, Jesus Moises
2008 IEEE SWARM INTELLIGENCE SYMPOSIUM, 2008, : 166 - +
[27] Multi-Objective Particle Swarm Optimization Based on Gaussian Sampling
Li, Guosen
Yan, Li
Qu, Boyang
IEEE ACCESS, 2020, 8 : 209717 - 209737
[28] Multi-Objective Particle Swarm Optimization Based on Fuzzy Optimality
Shen, Yongpeng
Ge, Gaorui
IEEE ACCESS, 2019, 7 : 101513 - 101526
[29] Multi-objective Particle Swarm Optimization Based on Adaptive Mutation
Saha, Debasree
Banerjee, Suman
Jana, Nanda Dulal
2015 THIRD INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT), 2015,
[30] An Improved Multi-objective Particle Swarm Optimization
Xu, Shengbing
Ouyang, Zhiping
Feng, Jiqiang
2020 5TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (ICCIA 2020), 2020, : 19 - 23

← 1 2 3 4 5 →