| 用于概念设计的离心泵叶轮多目标优化 |
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| 引用本文： | 郑赟韬,蔡国飙,尘军.用于概念设计的离心泵叶轮多目标优化[J].航空动力学报,2007,22(9):1554-1559. |
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| 作者姓名： | 郑赟韬 蔡国飙 尘军 |
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| 作者单位： | 1. 北京航空航天大学,宇航学院,北京,100083
2. 北京航天动力研究所,北京,100076 |
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| 摘 要： | 用两种多目标优化方法对某型液体火箭发动机氧化剂泵叶轮进行多目标优化设计.在优化过程中,以泵的扬程、效率和泵质量为优化目标,对叶轮的主要几何参数进行优化.用NCGA (Neighborhood Cultivation Genetic Algorithm)方法得到优化问题的Pareto前沿;用超传递近似法得到分目标的最佳权值,进而采用评价函数法求出优化问题的最佳设计值.并分析了主要结构参数对泵性能参数的影响.
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| 关 键 词： | 航空、航天推进系统 火箭发动机 离心泵 叶轮 优化 Pareto前沿 概念设计 离心 泵叶轮 多目标优化 conceptual design impeller centrifugal pump 影响 性能参数 主要结构参数 分析 设计值 评价函数法 权值 最佳 近似法 Pareto 优化问题 方法 Neighborhood |
| 文章编号： | 1000-8055(2007)09-1554-06 |
| 收稿时间： | 8/8/2006 12:00:00 AM |
| 修稿时间： | 2006年8月8日 |
Multi-objective optimization of centrifugal pump impeller for conceptual design |
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| ZHENG Yun-tao,CAI Guo-biao and CHEN Jun.Multi-objective optimization of centrifugal pump impeller for conceptual design[J].Journal of Aerospace Power,2007,22(9):1554-1559. |
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| Authors: | ZHENG Yun-tao CAI Guo-biao and CHEN Jun |
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| Institution: | School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;School of Astronautics, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;China Aerospace Science and Technology Corporation Eleventh Institute, Beijing 100076, China |
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| Abstract: | Two multi-objective optimization methods were applied to design the centrifugal oxidizer pump impeller for liquid propellant rocket engine.Major geometric parameters of pump impeller were optimized for the targeted delivery head,efficiency and mass of pump.The Pareto Front of multi-objective optimization was gained by Neighborhood Cultivation Genetic Algorithm(NCGA);the super-transitive approximation method was introduced to acquire optimal weights of individual objectives.Then,Evaluation Function method was used to obtain optimal design of multi-objective optimization.The effects of major structural parameters on pump performance parameters were also analyzed. |
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| Keywords: | aerospace propulsion system rocket engine centrifugal pump impeller optimization pareto front |
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