Optimization Design of Distributed Propeller Position Based on MOEA/D Algorithm

被引：0

作者：

Chen Xian ^{[1
]}

Wang Yuanyuan ^{[2
]}

Yu Longzhou ^{[1
]}

Huang Jiangtao ^{[1
]}

He Chengjun ^{[1
]}

Shu Bowen ^{[1
]}

机构：

[1] China Aerodynam Res & Dev Ctr, Aerosp Technol Inst, Mianyang 621000, Peoples R China

[2] China Aviat Ind Dev Res Ctr, Beijing 100012, Peoples R China

来源：

2023 ASIA-PACIFIC INTERNATIONAL SYMPOSIUM ON AEROSPACE TECHNOLOGY, VOL II, APISAT 2023 | 2024年 / 1051卷

关键词：

Multi-Objective Optimization; Pareto Leading Edge; Distributed Propulsion; Propeller Position; Lift-Drag Ration;

D O I：

10.1007/978-981-97-4010-9_74

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Distributed propulsion technology can be used to improve the takeoff and landing performance of aircraft. However, while the propeller slipstream increases the wing lift, it also the drag, which may lead to a decrease in the lift-drag ratio, thereby affecting cruise performance of the aircraft. Therefore, it is necessary to optimize distributed propellers for improving the cruise performance. The installation position is one of the key parameters of distributed propellers and has a significant impact on the aerodynamic performance. Thus, the MOEA/D algorithm is used for the optimization design of the distributed propeller position. Results show that the optimized configuration has a larger lift-drag ratio, indicating that optimizing the installation position of distributed propellers can improve the cruise performance of aircraft. Moreover, the MOEA/D algorithm can be used for the selection of distributed propellers, providing an important reference for the design of distributed propulsion aircraft.

引用

页码：970 / 978

页数：9

共 50 条

[11] Dynamic Rebalancing Optimization for Bike-Sharing System Using Priority-Based MOEA/D Algorithm
Hu, Runqiu
Zhang, Zhizheng
Ma, Xinwei
Jin, Yuchuan
IEEE ACCESS, 2021, 9 : 27067 - 27084
[12] MOEA/D with opposition-based learning for multiobjective optimization problem
Ma, Xiaoliang
Liu, Fang
Qi, Yutao
Gong, Maoguo
Yin, Minglei
Li, Lingling
Jiao, Licheng
Wu, Jianshe
NEUROCOMPUTING, 2014, 146 : 48 - 64
[13] Success-History Based Parameter Adaptation in MOEA/D Algorithm
Akhmedova, Shakhnaz
Stanovov, Vladimir
ADVANCES IN SWARM INTELLIGENCE, ICSI 2020, 2020, 12145 : 455 - 462
[14] Hydrodynamic optimization of ship's hull-propeller system under multiple operating conditions using MOEA/D
Zakerdoost, Hassan
Ghassemi, Hassan
JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2021, 26 (02) : 419 - 431
[15] Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for Optimal Design of Hydrogen Supply Chains
Cantu, Victor H.
Azzaro-Pantel, Catherine
Ponsich, Antonin
30TH EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING, PTS A-C, 2020, 48 : 883 - 888
[16] Design optimization of small distributed energy system based on NSGA-Ⅱ algorithm
Ding S.
Zhou B.
Hu B.
Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2021, 42 (01): : 438 - 445
[17] MOEA/D with chain-based random local search for sparse optimization
Hui Li
Jianyong Sun
Mingyang Wang
Qingfu Zhang
Soft Computing, 2018, 22 : 7087 - 7102
[18] Solving Multi-Objective Portfolio Optimization Problem Based on MOEA/D
Zhao, Pengxiang
Gao, Shang
Yang, Nachuan
2020 12TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI), 2020, : 30 - 37
[19] MOEA/D with chain-based random local search for sparse optimization
Li, Hui
Sun, Jianyong
Wang, Mingyang
Zhang, Qingfu
SOFT COMPUTING, 2018, 22 (21) : 7087 - 7102
[20] Multi-objective optimization algorithm for satellite range scheduling based on preference MOEA
Sun G.
Chen H.
Peng S.
Du C.
Li J.
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, 2021, 42 (04):

← 1 2 3 4 5 →