TY - JOUR
T1 - Structure Optimization of Bottom Nozzle for Flow Resistance Filtration-Bearing Performance
AU - Zhang, Bo
AU - Yuan, Pan
AU - Xiao, Zhong
AU - Guang, Honghao
AU - Chai, Zhenhong
AU - Li, Baotong
AU - Duan, Xin
AU - Xin, Yong
AU - Zhu, Fawen
AU - Sun, Kun
N1 - Publisher Copyright:
© 2025 Faculty of Geography UGM and The Indonesian Geographers Association.
PY - 2025
Y1 - 2025
N2 - To improve the comprehensive performance of the bottom nozzle, including coolant pressure drop, debris filtration, and load-bearing of structure, a multi-objective optimization approach was proposed to optimize the key dimensional parameters of the bottom nozzle with the goal of coolant pressure drop and debris filtration efficiency. Based on the approach, size optimization was conducted using the hexagonal bottom nozzle as an example. The results showed that the pressure drop and filtration efficiency were optimized by 12.5% and 6.3%, respectively. Meanwhile, the maximum stress was reduced by 14.0%, confirming a significant enhancement of the comprehensive performance. Furthermore, the multi-objective optimization approach is universal for the structural optimization of various types of bottom nozzle to improve its comprehensive performance.
AB - To improve the comprehensive performance of the bottom nozzle, including coolant pressure drop, debris filtration, and load-bearing of structure, a multi-objective optimization approach was proposed to optimize the key dimensional parameters of the bottom nozzle with the goal of coolant pressure drop and debris filtration efficiency. Based on the approach, size optimization was conducted using the hexagonal bottom nozzle as an example. The results showed that the pressure drop and filtration efficiency were optimized by 12.5% and 6.3%, respectively. Meanwhile, the maximum stress was reduced by 14.0%, confirming a significant enhancement of the comprehensive performance. Furthermore, the multi-objective optimization approach is universal for the structural optimization of various types of bottom nozzle to improve its comprehensive performance.
KW - Bottom nozzle
KW - Debris filtration
KW - Multi-objective optimization
KW - Particle swarm optimization
KW - Pressure drop
KW - Proxy model
UR - https://www.scopus.com/pages/publications/105017172610
U2 - 10.13832/j.jnpe.2025.01.0225
DO - 10.13832/j.jnpe.2025.01.0225
M3 - 文章
AN - SCOPUS:105017172610
SN - 0258-0926
VL - 46
SP - 225
EP - 231
JO - Hedongli Gongcheng/Nuclear Power Engineering
JF - Hedongli Gongcheng/Nuclear Power Engineering
IS - 1
ER -